Manipal University Jaipur

FOREWORD

This Book is published by Directorate of Alumni Relations, Manipal University

Jaipur. Manipal University Jaipur (MUJ) was launched in 2011 on an invitation

from the Government of Rajasthan as a self-financed State University. MUJ has

redefined academic excellence in the region with the Manipal way of learning,

inspiring students of all disciplines to learn and innovate through hands-on practical

experience. The purpose of the Directorate of Alumni Relations (DoAR) is to foster

a spirit of loyalty and to promote the general welfare of the University. The

Directorate supports the organization's goals to strengthen the ties between alumni,

the community, and the organization. Any educational institution's reputation

depends upon satisfactory results, academic achievements, co-curricular activities,

and its alumni, who are among the main stakeholders of the institutions. They

contribute immensely to the development of the University in multiple dimensions.

The recent trends that emerge from research is based on individual and collective

adherence to core values of objectivity, integrity, accountability, and stewardship of

Resent trends in Science and Engineering is intended to the publication of original

research articles as well as review articles, with emphasis on unsolved problems and

open questions in all sciences & Emerging technologies in Engineering. Social

sciences research (SSR) is an enterprise that is continuously evolving, but not

without some debilitating issues that impede the realization of its full potential and

usefulness. The book covers Sciences, Engineering and Social Sciences topics, such

as bioinformatics, computer sciences, physical sciences, chemical Sciences,

environmental sciences, management sciences, engineering technologies and

applications. Subsequently, all related field of social sciences are included within the

scope of the Recent Trends in Sciences, Engineering and Social Sciences.

Jaipur, India 11 December 21

Editors

This book is

Dedicated to our Stakeholders and

Directorate of Alumni Relations,

Manipal University Jaipur

Acknowledgment

We would like to express our gratitude and appreciation to all

those who gave us the possibility to complete this book directly or

indirectly. A special thanks to Prof. K Ramnarayan, Chairperson, Prof.

G. K. Prabhu, President, Prof. N. N. Sharma, Pro-President, Dr. Nitu

Bhatnagar, Registrar, Deans, Directors, HoDs of Manipal University

Jaipur for their stimulating suggestions and encouragement helped us to

coordinate especially in writing this book chapters.

We would also like to acknowledge the efforts of the alumni of

Manipal University Jaipur and their mentors, supervisors, coauthors in

achieving the goal as well as their encouragement to maintain our

progress in track.

We appreciate the crucial role of Dr. Karamadai Subban

Shivraj, Chief Librarian, Manipal University Jaipur at our workplace,

who gave the exemplary provision to use all required and the necessary

materials to complete this task.

A special thanks goes to Decennial Year event committee,

Department Alumni Coordinators and Nuclear Members who gave us

an unconditional support and a salient assistance during the

accomplishment of this task.

Contents

Chapter Title Page

No.

1 AUGMENTED REALITY INTRODUCTION AND APPLICATIONS

1

2 LUNG CANCER DETECTION USING IMAGE PROCESSING 12

3 SURVEY OF 5G ANTENNA FOR INDUSTRIAL AUTOMATION APPLICATIONS

19

4 KATHPUTLI 34

5 DIGITAL EDUCATION TURBULENCE AMONG EDUCATORS AND LEARNERS DURING THE COVID-19 PANDEMIC IN INDIA

64

6 INFLUENCE OF SOCIAL MEDIA ADDICTION ON ANXIETY, DEPRESSION, AND STRESS

76

7 ANALYSIS OF PREDICTIVE PATROLLING USING EXPLORATORY ANALYTICS

89

8 SENTIMENT ANALYSIS OF TWEETS TO DETERMINE POLITICAL OUTCOMES USING MACHINE LEARNING TECHNIQUES

102

9 ANALYSIS OF CRIME PREDICTION ALGORITHMS 111

10 BIO- PSYCHOSOCIAL CORRELATES OF DRUG ABUSE IN ADULTS AND ADOLESCENTS

132

11 FACTORS INFLUENCING RESPONSIBLE CONSUMPTION DURING POST COVID 19

154

12 FINANCIAL LITERACY LEVELS ACROSS POPULATION GROUPS

160

13 SYNTHESIS AND MÖSSBAUER SPECTROSCOPY OF MARCASITE FES2 NANOPARTICLES

172

14 CHEMICAL CHARACTERIZATION AND BIOLOGICAL ACTIVITY OF SILVER NANOPARTICLES BIOSYNTHESISED FROM EXTRACTS OF CISSUS SP.: NATURAL BONE HEALING HERB

184

15 PROSTITUTION IN INDIA-SOCIO-LEGAL PERSPECTIVE 194

16 GENDER NEUTRALITY VIS-A-VIS STATUTORY PROVISION

201

1

CHAPTER 1

AUGMENTED REALITY INTRODUCTION

AND APPLICATIONS

Suman Bhakar Department of Computer and Communication Engineering Manipal University Jaipur [email protected] Devershi Pallavi Bhatt* Department of Computer Application School of Basic Sciences Manipal University Jaipur [email protected] (Corresponding Author) Vivek Sharma Department of Computer and Communication Engineering Manipal University Jaipur [email protected] Jitendra Singh Yadav Department of Computer and Communication Engineering Manipal University Jaipur [email protected] ******************************************************************** Abstract: Due to such dependency on robots, numerous research fields and proposal

have been aimed develop more methodologies. These can be helped to execute tasks

by robots within a time frame having minimum human interference and without any

delay. In this research, glyph detection system through Augmented Reality has been

proposed for fiducial glyph detection by machine/robots at minimum latency time.

Keywords: Augmented Reality, Latency, Glyph

********************************************************************

mailto:[email protected]




2

Introduction

Augmented Reality plays a significant role in enhancing the perception of users

towards the interaction with the real world. It is a key to detect displayed

information coming from virtual objects where users failed to identify it with their

senses. Primarily, this technology can be utilized in two areas such as in repairing of

complex mechanical devices and in medical applications. For example, during

surgery, doctors can use AR technique to visualize the internal things of human

body as a see through Head Mount Displays (HMD) [6-7]. It can be combined with

real time 3D sensors to scan a human body without any cuttings which reduces the

risk of performing operation. In case of complex machinery, it becomes quite

difficult to read and memorize the instructions from hand manual. AR makes the

whole procedure simple by representing the 3D drawing with systematic procedure

for handling the tool or device [8]. These are all real time system based applications

which motivate us to develop more methodologies for making it available to

common people also.

A real time system can be described as a system that is used to regulate an

environment by gathering data, formulating them and giving back the outcome

rapidly within time frame to influence the environment at that time. It is an

application program which performs the action under the time frame in a manner

that the user senses it for an instant. Real–Time Computing (RTC) depicts the

programming frameworks and hardware which is subjected to a ‘real time

restriction’ [9]. This restriction is used for an instance that is transmitting from an

event to system response. Real-time programs must ensure response inside indicated

time requirements, commonly known as ‘deadlines’ [10]. The accuracy of these

sorts of systems relies on upon their temporal features and in addition their

functional characteristics. Applications that are based on real-time responses are

frequently measured in milliseconds, and often in microseconds. Systems that

depends upon real-time, execute its task within a given time interval and time frame.

On the other hand, systems that are not operating in real time fail to execute the task

within a specified time frame. Here time is a very important and crucial factor and if

the system fails to execute within a timeframe then the result will be disastrous. In

3

these applications, latency plays a vital role and it must be less than a particular

measure value to render the result successful.

In spite of promising future of real time applications, there are still challenges which

need to be addressed, particularly with latency factor of machine/robots which is the

most vital component in getting effective and accurate results. There are a number of

factors which affects the latency in real-time applications such as LUX, distance,

camera processing time etc. It is thus imperative to study and propose a glyph

detection system that helps to minimize the latency factor in terms of various

challenging conditions.

Principles of AR World

AR and VR share many technologies and have lot of things are in common.

However, to differentiate between AR and VR, it is important to observe their

behavior towards the real world. Computer-generated virtual surroundings prefer

Head Mount Displays (HMDs) [11]. These have the potential to replace the real

world entirely with a synthetic environment generated by computers. Some certain

conditions such as with closed-view HMD, it covers the sight of users that makes

them blind with the removal of electrical power. On the other hand, AR supplements

are based on the concept of see-through HMDs [11]. It superimposes the real world

with virtual objects upon the wearer's view. Theoretically, the aim of the AR system

is to provide a seamless performance by the amalgam of two different worlds (reel

objects with real world objects).

Another constraint effectively used to illustrate AR known as latency. Latency is

defined as the delay in activity and response. Such end-to-end system delay causes

registration error. Latency plays a pivotal role while using HMD for AR and VR. As

compared with VR, AR requires registration that is much more accurate. Visual

conflicts arise due to registration errors in between the images of real and virtual

worlds. The differences in the sensitivity of human visual system and resolution of

human eye made the conflictions easy to detect [11]. Even the detection of tiny

offsets becomes easy in the images of virtual and real worlds. For instance while

wearing head mounted displays if the user moves his head, then the HMD should

4

show the changes in the virtual world instantly. Any huge delay, in this process, will

make the user’s mind dismiss the authenticity of the virtual world. High latency

while using Augmented Reality can make virtual objects or images misaligned with

real world environment and in turn reduces the performance and efficiency of the

system.

Main principle of this research is to reduce the latency and increase the efficiency of

AR system my using Glyph recognition with image processing technique. It has

been sorted out the end to end system delay which is also considered to be as

dynamic error due to the random movements in user’s position.

Advantages and Applications of Augmented Reality

AR shows its potential to sort out several challenges faced by users. Several

professionals are engaging themselves in the advancement of AR system. AR now

involves in users life with the completion of their day-to-day tasks [12]. Augmented

reality features some of the major benefits that are as follows:

• Industrial purpose- AR makes the things easier to carry industrial tasks such

as hard labor work of weight lifting conducted by robots or machines. In

addition, automatic operations with any manual help to control the machines.

• Product marketing for users- Professionals performs such tasks very easily,

which make the AR system a good choice for product detection. User can

obtain all the data about the product by merely scan the material or

packaging.

• Military applications- AR applications show their worth in several military

operations. In US military, soldiers can estimate their surroundings before

entering in enemy’s location. It also provides them hideout to attack their

enemies in better way.

• Education sector- AR has modernized and simplified the communications

between students and their teachers [22]. It helps the students to access the

data in form of text and images from textbooks anytime and anywhere. They

just need to scan the custom codes obtained from their organizations. In

addition, it provides a high-tech training methodology to the students.

5

• Corporate communication- AR reduces the gap between investors,

employees and employers with their communication set-up. If they are all

participating through AR content, the data will be in authorized hand only. It

is resulting as higher sales and profits by effectively compete with opponents.

• Reviews of sales and design- It requires a separate display for each product

during design and sales in order to create prototypes and loads of packaging

that is not at all a cost efficient process. AR performs the access and

customization of product by just scanning their look, shape and size. It can

be done through smart phones and devices.

The potential applications of this research are as under:

Real time system based application

Boston dynamics

Computation minimum latency

Machine

Robotics and bot application

Problem Definition

Nowadays, augmented reality has focused on robotic and real world application.

Real world entity and robotics are predictable through marker (glyph) based and

marker less (without glyph) technique in the field of augmented reality system.

Accurate 2D and 3D glyph detection with least delay time known as latency is the

key issue in the AR system. Where, robot and real world applications are conducting

the action through marker based or marker-less techniques. However, there is the

detection and registration complexity in markerless technique. Therefore, marker

based is more efficient then markerless technique. There is a certain issue in existing

detection technique through motion camera. Therefore, optimized process is needed

to detect the 3D and 2D glyph more accurately. Latency also plays a vital role in the

field of robotics and real time based applications where, action performs within

specific time frame. Thus, with the innovation in the theory of augmented reality and

rapid expansion of robots, there is a requirement to develop an algorithm for glyph

detection with the formulation of least delay time.

6

Objectives

The primary aim of this research is to conduct broad analytical as well theoretical

studies on augmented reality. The main issues to be analyzed in this research work

are stated as:

• To enhance the quality of the glyph apply image processing (IP prototype)

technique on glyph.

• To reduce the latency histogram analysis performed with the help of

pixilation algorithm to determine the noise present in the glyph.

• Glyph detection and determine the angle of glyph with respect to Pc camera

apply A forge. Net framework and 3D pose estimation algorithm.

• To develop computation proposed algorithm to recognize the (2D, 3D) glyph

at different angle, distance and luminous with minimum delay time.

• To compare and analyze the results, obtained from the both 2D detection and

3D detection.

• To Plot the graph result of 2D detection time and 3D detection time with

ideal value of system.

• To compute the percentage of latency improvement or accuracy of 2D glyph

with respect to 3D detection system.

Key Contributions

In this research, an attempt has been made to develop a new augmented reality

system which has a direct application in the robot navigation. Infrared optical

markers projected in the real world environment are analyzed by the robot and

pattern Ids are generated in this process. This framework empowers the robot to

control a complex movement by the robot instructions analogous to the pattern IDs.

A model framework is developed to execute basic experiments of remote robot

mapping.

This research includes glyph recognition with ‘Aforge.net framework’ for designing

the augmented reality system [5]. In addition, Posit estimation is preferred here for

the determination of location and orientation of glyph respecting camera. Image

processing with glyph recognition technique improves the communication to do

action at minimum latency time to Aurduino Uno controller. The simulation process

7

has been undertaken by virtual connection between C# application with Aforge.net

framework and Aurduino.Aforge.NET Framework’ in combination with C#

language to lay down a principle for development of applications that provides

image object detection abilities [5].

Various researches has been undertaken to differentiate and detect the object from

the background environment to determine the exact location and position of the

target object in the surrounding environment. In the same manner, Posit estimation

approach that is albeit unnoticeable in almost all applications provides the high level

of precision at the same time in distinguishing location and direction of the fiducial

marker regarding the camera [12]. However, latency plays a pivotal role in

increasing the overall efficiency of the machine/robot remains an underdeveloped

study. This application can be usefully in enhancing different abilities of robot, for

example, precise detection of object and movement of the same in the minimum

time or within the required time frame. Various factors such as distance from camera

to glyph, LUX, camera response time which affects the detection of arm of the

machine/robot are analyzed in this research.

The research has been undertaken in the same direction looking at tremendous

applications of such glyph detection augmented reality system and study the light

intensity as well as challenging distance between glyph and camera that affects the

fiducial glyph detection process by the camera accurately.

After problem identification, there is a need for developing an infrared glyph based

augmented reality system in order to minimize the latency for machine /robotic

applications. In our daily life, this research reports the involvement of augmented

reality as machines and robotics. The glyph based augmented reality system

minimize the latency under adverse conditions such as camera processing time,

distance between camera/glyph and luminous which affects the detection of object

through servo motor speed.

Dissertation Outline

This thesis is organized in total seven chapters presenting the details of theory,

literature review, AR techniques with recognition process and experimental work

8

comprising hardware design, assessment and investigation along with optimization

techniques for enhancing the performance of newly developed AR system.

The primary overview of AR system, its principles, advantages, applications

following with the main contributions of research work and objectives have already

been presented in Section One.

Further dissertation has been briefly outlined as follows:

Section Two provides an inclusive literature review on AR communication including history, origination and modelling schemes with their performance characteristics. This chapter presents the existing techniques with their main gaps and active research fields. This chapter also discusses the possible introduced methods that can be used to enhance the reliability of AR world. It discusses all the present scenario need to be deal with in order to develop an optimized system. This chapter can be useful to compare the developed methodology with the existing systems and their performance evaluations. Section Three focuses on the development of glyph detection system with its model

diagram. It includes the hardware section and the detailed examination of

experimental performance evaluation. Hardware set - up and designing of system

their technical specifications are outlined first. It includes the interfacing between

Ardunio and server motor. It includes the 2D and 3D detection techniques with

accuracy. Delays based on angles have been noted and observe their influence on

virtual reality. Real world applications based on dynamic errors have been illustrated

further.

Section four describes the latency factor. Histogram technique is used for noise

calculation present in communication link. A test bench is developed to calculate the

latency factor present in AR and try to minimize it for optimized performance.

Glyph recognition is explained here for computing and processing of data obtained

from variations in the luminous and glyph detection at different challenging distance

(feet) [12]. Image processing creates the glyph for augmented process and further

provides the image in its structured matrix representation. Posit algorithm processes

the glyph detection at the location and orientation of image with respect to camera

[12].

9

Section Five illustrates in detail the calculation of response time with or without

Glyph rectifiers. In this chapter a novel detection method is proposed for the

detection of actual process time. This chapter elaborates the calculation of system

actual time to capture the glyph with the augmented process [5]. In addition ideal

and actual transmission flow rate have been estimated by the use of designed

hardware.

Section Six explains and discusses the outcomes coming from simulation techniques

and algorithms which are designed by Glyph technique with image processing. It

includes the findings and their contributions in the research work. Graphical

representations and statistical analysis have been made on findings.

Finally, Section Seven includes the conclusions of this research work. It summarizes

the major contributions with findings. A discussion has been made and some

suggestions are included as the future directions.

Conclusion

Augmented Reality systems proposed an extraordinary world of visuals that

superimposes with the real element and computer-generated graphics. In this

research chapter is introduced the brief introduction about augmented reality and

problem definition along with motivation. Also, discussed the different section

which, defines the illustrate working of the proposed work.

References & Bibliography

1. R. Dnddera, C. Jia, V. Popescu, C. Nita-Rotaru, M. Dark, and C. S. York,

“Virtual Classroom Extension For Effective Distance Education,” IEEE

Computer Graphics and Applications, pp. 64-74, 2008.

2. W. Piekarski, B. Avery, B. H. Thomas, and P. Malbezin, “Hybrid Indoor and

Outdoor Tracking for Mobile 3D Mixed Reality,” Washington, DC, USA,

IEEE, pp. 266-267, 2003.

3. H. Liu, et al., “Mobile localization based on received signal strength and

pearson’s correlation coefficient,” International Journal of Distributed Sensor

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10

4. A. Mulloni, and T. Drummond, “Real-time detection and tracking for

augmented reality on mobile phones,” IEEE Transactions on Visualization

and Computer Graphics, vol. 16, no. 3, pp. 355-368, 2010.



Networks, vol. 2015, pp. 10, 2015.



IEEE, pp. 266-267, 2003.

7. Bejczy, K. Antal, W. S. Kim, and S. C. Venema, “The phantom robot:

Predictive displays for tele-operation with time delay,” NASA Tech Brief 16,

7,item #104. From JPL new technology report NPO-18277/7794, July 1992.

8. Mc Farland, and E. Richard, “CGI delay compensation,” NASA Technical

Memorandum S6703, 1986.

9. G. D. Hager, and P. N. Belhumeur, “Real-time tracking of image regions

with changes in geometry and illumination,” In Proceedings of IEEE CVPR,

1996.

10. N. Dalal, and B. Triggs, “Histograms of oriented gradients for human

detection,” IEEE Computer Society Conference on Computer Vision and

Pattern Recognition, vol. 1, pp.886-893, 2005.

11. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image

recognition,” In Proceedings of the IEEE Conference on Computer Vision

and Pattern Recognition, pp. 770-778, 2016.

12. D. Borland, and R. M. Taylor II, “Rainbow color map (still) considered

harmful,” IEEE Computer Graphics and Applications, vol. 27, no. 2, pp. 14–

17, March 2007.

13. P. Anandan, “A computational framework and an algorithm for the

measurement of visual motion,” International Journal of Computer Vision,

vol. 2, pp. 283-310, 1989.

14. M. Uenohara, and T. Kanade, “Vision-based object registration for real time

image overlay,” In Proceedings of Computer Vision, Virtual Reality, and

Robotics in Medicine, pp. 13–22, 1995.

11

15. S. Feiner, B. MacIntyre, and D. Seligmann, “Knowledge-based augmented

reality,” Commun. ACM, vol. 36, no. 7, pp. 52-62, July 1993.


“Virtual classroom extension for effective distance education,” IEEE

Computer Graphics and Applications, pp. 64-74, Jan./Feb. 2008.

17. Y. Shi, W. Xie, and G. Xu, “Smart remote classroom: creating a revolutionary

real-time interactive distance learning,” Proceedings of International

Conference on Web-Based Learning, LNCS 2436, 2002.

18. Azuma, Ronald, and G. Bishop, “Improving static and dynamic registration

in a see-through HMD,” In Computer Graphics, Annual Conference Series

(Proceedings of SIGGRAPH), pp. 197-204, 1994.

19. J. F. Rodgrigues, A. J. Traina, M. C. F. De Oliveira, and C. Traina, “Reviewing

data visualization: an analytical taxonomical study,” In Tenth International

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20. Saipullah, and K. Muzzammil, “OpenCV based real-time video processing

using android smart phone,” International Journal of Computer Technology

and Electronics Engineering, vol. 1, no. 3, pp. 1-6, 2012.

21. J. J. Lugo, and A. Zell, “Frame- work for autonomous on-board navigation

with the AR drone,” Journal of Intelligent and Robotic Systems, vol. 73,

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22. M. Tuceryan, Y. Genc, and N. Navab, “Single-point active alignment method

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12

CHAPTER 2

LUNG CANCER DETECTION USING IMAGE PROCESSING

Devershi Pallavi Bhatt Department of Computer Application School of Basic Sciences Manipal University Jaipur [email protected] Suman Bhakar* Department of Computer and Communication Engineering Manipal University Jaipur [email protected] (Corresponding Author) Vivek Sharma Department of Computer and Communication Engineering Manipal University Jaipur [email protected] ******************************************************************** Abstract: Lung cancer diagnosis is a challenging and difficult task. Radiologists face challenges in diagnose the Lung nodules accurately. Among men Lung Cancer is the most common kind of cancer and among women near to lung cancer, breast cancer has the second-highest mortality rate. According to clinical statistics, one out of eight women in their lives is diagnosed with breast cancer. Nevertheless, frequent clinical assessments and self-tests aid early detections and thereby improve the chance of survival significantly. Invasive tumor detection methods accelerate cancer spread to neighboring regions. A more reliable, effective, precise, and efficient, non-invasive cancer detection system is therefore needed. A computer program for error-free lung cancer detection using a CT scan is proposed in this report. Deep learning techniques such as convolution neural networks, sparse autoencoder and sparse autoencoder are used in this framework. They assess and evaluate the results of these methodologies with existing techniques. The analysis shows that the autoencoder stacked sparse performs better than other techniques. Keywords: Lung, Neural network, Deep learning ********************************************************************




13

Introduction

Lung cancer is one of the world's deadly diseases that easily destroy people's lives.

People's deaths due to lung cancer rise exponentially. The lung cancer detection and

diagnostic device are required to minimize the situation, to assist the radiologist and to

save a human life. In 2012 there have been about 1.83 million new cases of lung cancer

and over 1.5 million deaths are predicted, according to global cancer statistics [1].

In Ethiopia Lung Cancers Deaths were 1.584 or 0.25 percent of total fatalities

according to latest data released in 2017 by the World Health Organization. Medical

system imaging generates large quantities of medical picture with relevant disease

information. The medical picture is one of the important areas of study with many

illnesses in the fields of medical problems. Medical image processing is used in

analyzing, using different methods of medical image analysis to extract information

or expertise from all medical pictures and to solve medical problems. There are a

variety of medical imaging techniques that have been used to scan our bodies.

Figure 1: Chest X-Ray Image [1]

Tomography (CT scan), Positron Emission Tomography (PET), mammography, X-

ray, and Magnetic Resonance Image (MRI), ultrasound and so on are used to

diagnose and detect disease early [2]. However, computed tomography (CT scan)

imaging is one of the best imaging techniques in the field of lung cancer detection

and diagnosis, because it exposes any suspected and unsuspected CT-controlled lung

cancer nodules.

14

Bronchial Carcinoma

CT with special 3-D CAD systems are recognized and measured as the best standard

in the diagnosis of lung cancer. Discovering lung cancer in its early stages is

extremely critical. The 5-year survival rate for a lung cancer of specific stage is sixty

percentage, that explains that people who have that cancer are, on average, about

sixty percentage, people are likely to be alive for almost five years of detection.

Lung cancer is very critical, and it affects more victims than other cancers like breast

cancer, prostate cancer and colon cancer. The key cause for this is the asymptomatic

development of this cancer. If the lung cancer is found in its early stages, there is

chance of survival rate to a great extent according to the American Cancer Society.

The random detection of lung cancer in its initial stage in the x-ray image is

incredibly challenging. It is a fact that round lesions varying from 5–10 mm are

easily overlook.

Figure 2: Image Processing in Cancer Detection

Image processing for the identification of cancer is used. The detection of cancer

cells has proved to be successful. Two phases are primarily used for image pre-

processing. Segmentation of images Augmentation of images Separation is aimed

primarily to isolate the cells from the background and to improve images by

improving the contrast between cells of the cancer and the complete image scan of

the brain, lung, etc.

15

Image Segmentation

The first step is to divide the picture. Segmentation divides an image into its main

parts. Depending on the issue, the level of this subdivision is carried. It means that

when the tumor 's surface is identified; the segmentation will cease. The key goal is

therefore to separate the tumor from its context. surface is the key problem in the

edge detection process; Therefore, the scan appears very dim, which makes the edge

detection technique very confusing. Two steps have been taken to resolve the issue.

1. Histogram equalization

2. Thresholding

Histograms were equalized to increase the gray level near the edge of the image.

Equalized image thresholding is applied for obtaining a bilateral picture of the

cancer cells with gray level 1 and gray level 0 of the background.

Histogram Equalization

The histogram in an image shows how often the several gray levels in the image are

relatively frequent. Histogram modeling approaches provide a complexity technique

for adjusting the dynamic range and the contrast of a picture by modifying the

picture to the desired form of its intensity histogram. Histogram modelling operator

uses nonlinear and non-monotonic transfer features to map amid input and output

image pixel intensity values. [4] The histogram equalization utilizes a monotonous,

nonlinear mapping that re-assigns the pixel intensity in the input frame such that the

final output consists of a single intensity distribution. The original image and

histogram equalized picture.

Thresholding

The areas of the image that match necessary objects to the areas of the image that

correspond to the background are very helpful to separate. Thresholding also

provides a simple and effective way to conduct this segmentation based on the

different intensities or colors of the front and background areas of an image. The

input of a threshold is a grey scale or color image. The output is a binary image

representing the segmentation in the event of a simple implementation. The

16

backdrop is displayed by black pixels and the foreground is shown by white pixels.

A single parameter known as the intensity threshold is specified for the segmentation

in simple operation. Every pixel in the picture is compared to the threshold in a

single pass. If the pixel intensity is greater than the threshold, the pixel is set to

white in the display. On the other hand, it is set to black if it is below the threshold.

Image Enhancement

In contrast, the fundamental change in the picture is change. A contrast from the

normal region with the tumor may occur on an MRI but below the human perception

threshold. Thus, a sharpening filter is added in the digital picture to enhance the

contrast between the normal region and the tumor region, thus greatly enhancing the

image contrast.

Conclusion

A variety of problem areas such as finance, medicine, engineering, geology, physics,

and biology were effectively applied to neural networks. Statistically, due to their

potential use in prediction and classification problems, neural networks are

interesting.The non-linear data-driven autonomous adaptive forms are artificial

neural networks (ANNs). ANN is a dominant modeling tool, especially in the case of

unknown data relationship. ANN can recognize and study associated patterns

between input data sets and the relevant goal values. Following preparation, ANN

can predict the outcomes of new, independent input data. ANNs mimic the human

brain learning process and can handle nonlinear and complex data problems even

when the detail.



“Virtual Classroom Extension For Effective Distance Education,” IEEE

Computer Graphics and Applications, pp. 64-74, 2008.



IEEE, pp. 266-267, 2003.

17



Networks, vol. 2015, pp. 10, 2015.

A. Mulloni, and T. Drummond, “Real-time detection and tracking for

augmented reality on mobile phones,” IEEE Transactions on Visualization

and Computer Graphics, vol. 16, no. 3, pp. 355-368, 2010.



Networks, vol. 2015, pp. 10, 2015.



IEEE, pp. 266-267, 2003.

6. Bejczy, K. Antal, W. S. Kim, and S. C. Venema, “The phantom robot:

Predictive displays for tele-operation with time delay,” NASA Tech Brief 16,

7,item #104. From JPL new technology report NPO-18277/7794, July 1992.

7. Mc Farland, and E. Richard, “CGI delay compensation,” NASA Technical

Memorandum S6703, 1986.

8. G. D. Hager, and P. N. Belhumeur, “Real-time tracking of image regions

with changes in geometry and illumination,” In Proceedings of IEEE CVPR,

1996.

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19

CHAPTER 3

SURVEY OF 5G ANTENNA FOR INDUSTRIAL

AUTOMATION APPLICATIONS

Tapan Nahar

Research Scholar

Department of Electronics & Communication Engineering

Manipal University Jaipur, India.

Sanyog Rawat

Professor

Department of Electronics & Communication Engineering

Manipal University Jaipur, India.

[email protected] (Corresponding Author)

******************************************************************** Abstract: High performance millimeter-wave 5G antenna solution is required in

industrial automation which can effectively mitigate the problems associated with

currently used wireless technologies such as high latency or response time, insecure

communication, low transmission and reception speed, limited bandwidth, lower

capacity of network, connectivity issues due to multiple devices on network,

congestion in the radio channels, higher energy consumption and higher cost of

wired circuits and maintenance. Cost efficient solution is needed, which could

replace the computer based or programmable logic controller based hard wired

automation. To execute a highly flexible wireless automation of industries,

technology must be support mission-critical services which needs high reliability,

deterministic traffic properties and very low communication delays. Further, should

enable process optimization and communication for time critical/ non-time critical,

Intra/inter-enterprise communication between connected products, remote control,

video driven interaction between cobots and humans. 5G will provide wide

bandwidth than 4G LTE (long-term evolution) and Wi-Fi (wireless-fidelity), faster

connection, low latency and higher capacity support to connect thousands of devices

in smaller area which will accelerate production facilities. It’s ability to integrate


20

with time-sensitive networking (TSN) will support to achieve goal of smart

industries and enhancing the Ethernet to become more “deterministic”, available on

time with high reliability, reduced jitter and small latency. This paper presents the

survey of current issues of wireless industrial automations, requirement of 5G, and

review of 5G antennas for industrial automation applications.

Keywords: 5G, millimeter-wave antenna, wireless industrial automation, Industry

4.0, Industrial IoT (Internet of Things)

******************************************************************** Introduction

Industry 4.0, Industrial IoT, and the smart factory are the future technologies of

industrial production [1]. Manufacturing plant and intra logistics planning should be

more flexible, automated, and efficient, which necessitates the use of the appropriate

communication architecture and complete connectivity. The implementation of 5G

in the industry will offer up a lot of new capabilities [1], [2]. Researchers are

extremely interested in 5G-based industrial automation. Collaborative industrial

robots (cobots) are in high demand. It is critical to integrate 5G with robots. In

robotics, real-time control is implemented through the use of sensors for a variety of

functions. It necessitates the use of several complicated protocols, numerous sensors,

and more flexible and intelligent motion control. Due to severe connection latency,

implementing real-time control utilising an external PC is extremely difficult, if not

impossible. A 5G-based public network / personal network might close this gap, thus

further research is needed in this area [2].

5G is the most recent improvement in future wireless communication technology

that has yet to be introduced in India. The ideal millimetre wave antenna for 5G has

yet to be discovered. High data rates, large bandwidth, and low latency services will

be provided via millimetre wave bands [3]. The strength of 5G mobile networks will

enable ultra-high definition video streaming, conferencing, and broadcasting at

higher mobility without losing connection, industrial automation using billions of

device-to-device connectivity, mission-critical applications such as tele-surgery and

driverless cars due to low latency and highly reliable 5G networks, and increased

21

productivity due to real-time, high-quality data analytics [4]. In all of these

applications, a low-cost planar mm wave antenna will be utilised to transmit and

receive millimetre wave signals, which is appealing to automated smart industries

due to the high data rate and low latency that can be obtained [5].

The fourth industrial revolution is underway in the automation business, with fully

networked and automated "cyber-physical" plants in the works. This transformation

requires efficient plant networking and media-free communication of commodities

and equipment, and effective digitization is a vital component of that. The 3rd

Generation Partnership Programme (3GPP) is sponsoring the 5G system, which will

provide concepts and technologies that will make manufacturing businesses more

efficient. Wireless automation now accounts for 4% of the industrial automation

industry [1], [2]. Technology must enable mission-critical services that require high

dependability, predictable traffic characteristics, and extremely minimal communication

latency in order to implement a highly flexible wireless automation of industries.

Process optimization and communication for time-critical/non-time-critical, intra-

/inter-enterprise communication between linked goods, remote control, and video-

driven interaction between cobots and humans should also be possible [6].

This chapter is divided into seven sections. Introduction of future generation

industrial automation is introduced in Section 1. Section 2 describes requirements of

industries in terms of automation. Challenges in currently used industrial automations

are presented in Section 3. Section 4 highlights the role of 5G in industrial

automation. Investigation of reported works in 5G antenna for industrial applications

are discussed in Section 5. Section 6 consists of challenges in the 5G antennas.

Chapter is concluded in Section 7.

Requirements of Industrial Automation

There are various operations and application which is required in Industry 4.0 or

future generation wireless industrial automation [1]–[3].

• Controlling or Operating the instruments from remote place, like assembly

line robotic systems on the manufacturing plant. Welding, painting, and

assembling are examples of typical uses [1].

22

• Remotely managing supply chain equipment, for example, an employee can

manage untethered robots; common examples include unmanned ground

vehicles or forklifts [3].

• Monitoring the instruments from remote location, like the transmission of

diagnostic information, promises that maintenance team are ready to perform

repair work as needed [2].

• Machine-to-machine communication: machine-to-machine communication

in a closed loop to improve industrial processes [1], [3].

• Intra- and inter-enterprise information exchange: allowing monitoring assets

scattered across wider geographical locations across the production process [3].

• Assistance towards augmented reality in designing, service, and repairing

(through simulations): augmented reality can be used to support in the

accomplishment of procedural tasks in the design, servicing, and maintenance

domains [2].

• Wireless networks for transmission [1].

Challenges of Industrial Automation

Predictive and prescriptive maintenance plans, self-healing manufacturing lines with

near 0% downtime, remote control procedures, autonomous robots, and augmented

reality systems are all part of Industry 4.0. These capabilities necessitate improved

factory-floor connection. As a result, wireless communication is becoming more

business and mission important, necessitating increasingly demanding dependability,

latency, and security requirements. Not all wireless technologies can keep up with

today's smart industrial demands [1], [2].

Wireless solutions that are now accessible have a slew of issues. Manufacturing

nowadays is based on the integration of several systems, which results in large

amounts of data being produced. This massive growth in data collecting requires

quicker transmission and analysis with broadband capability and small response

time (latency), which is a significant problem for present technology. The current

frequency range employed in wireless technologies such as Bluetooth and WLAN

has a significant risk of being hacked. Because of the complexity of these wireless

protocols, there are unforeseen dangers in wireless equipment solutions which could

23

enable hackers to take control of a machine. The lack of available bandwidth on

wireless networks, as well as interference from competing services, is a major

concern [1], [3], [7]. With billions of devices, radio channel congestion is a major

issue. Adaptive frequency hopping (AFH) allows a Bluetooth device to discard

channels with a lot of data collisions. The effectiveness in a mixed-signal environment

is unclear, and collisions and data losses will occur if the radio formats do not

identify one other. Due to interference from the environment, an industrial sensor

that loses its control signal or ceases operating might have disastrous effects. Real-

time control is difficult, if not impossible, due to communication latency. For a

machine with more than 100 sensors and/or actuators, factory automation necessitates a

response time of less than 10 milliseconds. The transmission of raw sensor data at a

rapid update rate necessitates a lot of bandwidth. The proper operation of most

enterprises relies heavily on equipment maintenance. Depending on how long a

process is paused, an unexpected breakdown of equipment can cost a company a lot

of money. It is necessary to have a device or technology that can support augmented

reality solutions. Costly programmable logic circuits (PLCs), PC-based automation,

and hardwired circuits are being replaced by low-cost 5G-based antenna receivers

[1], [2].

Role of 5G in Industrial Automation

Upcoming communication systems are projected to transform present industry

structure by meeting demanding standards for ultra-high dependability and ultra-low

latency. In this vein, Millimeter-wave communication, which runs at frequencies

from 30 to 100 GHz, is the essential to execute future industrial automation. 5G is

quickly becoming the preferred link, acting as a conduit for data flow across all

elements of the industrial environment. To address the aforementioned issues, a

high-performance millimeter-wave 5G antenna should be used in 5G-based

industrial automation processes. Wide band and high gain millimeter-wave antennas

with narrow beamwidth and scanning capabilities will be developed, allowing for

high speed, low latency, low jitter, enhanced connection, increased capacity, cost-

effective, low power consumption, and highly dependable systems [7]–[10].

Because of their simplicity of manufacture on PCB (printed circuit board) together

24

with other electronics components and circuitry, microstrip antennas are becoming

increasingly popular. These are increasing at a faster rate in the communication

sectors because to their light weight and small size. Because of their cost-effectiveness,

planar construction, low profile, and simple production procedures, they may be

incorporated and suitable for embedded antennas. These antennas may be used to get

linear and circular polarisation radiation characteristics as well as multiband

operations [11].

The use of a high-performance millimeter-wave 5G antenna can alleviate problems

in the wireless automation sector. The millimeter-wave spectrum's wide bandwidth

will allow for minimal latency and speedier transmission [12],[13]. Since of their

narrow coverage range, millimeter-wave frequencies are a preferable choice for

interior communication because they have a lower risk of being hacked. Numerous

devices can access multiple channels and interact without interference or radio

collisions due to the lack of other services in the millimetre wave range and the

availability of free spectrum. It also offers improved connection, more capacity, and

a lower risk of interference. The 5G spectrum can help minimise response time. The

use of millimeter-wave antenna systems can allow a high update rate. 5G will

provide ultra-high-definition video services and ultra-fast internet speeds, allowing

for augmented reality solutions such as video broadcasting to deliver real-time help

and services from professionals. By replacing hefty PCs and hardwired electronics

with millimeter-wave 5G antenna receivers, the overall system cost will be

decreased [1], [2], [14].

Investigation of 5G Antennas

Currently, industrial automation antennas operate in the sub 1 GHz and sub 6 GHz

bands. Microwave antennas, which have an omnidirectional radiation pattern and a

restricted bandwidth, are used in industrial automation. Collinear array antennas

(high gain antennas that transmit signal in multiple directions), width antennas

(mobile worker applications), Yagi antennas (directional antennas), parabolic

reflector antennas (narrow beam antennas), and low profile panel antennas/chip

antennas are some of the most common industrial antennas. Wi-Fi, Bluetooth, RFID,

Multiband GSM, WLAN, and ZigBee are examples of common wireless technologies

25

that are employed [1]. Frequencies from 2.4 GHz to 5 GHz is utilized by the

antennas. Restricted bandwidth, high latency, limited data rate, large size, limited

battery life, reduced security, limited number of connected devices, tiny signal to

noise ratio, and other issues plague these antennas and technology [3]. Low-profile

5G antennas for industrial applications that support the millimeter-wave spectrum

are required to address these concerns. Due to the lack of other commercial uses, the

millimeter-wave spectrum is open to the public and allows ultra-wideband, low-

latency, high-capacity communication. Multiple input multiple output (MIMO)

transceivers are employed at Millimeter-wave frequencies to give a high signal

quality, noise suppression, wide band operations, directional properties, and support

a large number of users. Despite the fact that this radio wave behaviour was

traditionally thought to create signal interference, MIMO-enabled antennas can take

use of it to increase communication performance [15]. All 802.11n wireless

products, in fact, feature MIMO, which is important for allowing such devices to

deliver much higher communication speeds. Despite the fact that this radio wave

behaviour was traditionally thought to create signal interference, MIMO-enabled

antennas can take use of it to increase communication performance. Antenna

installation challenges include frequency and polarity considerations, mounting

position difficulties, safety concerns, and gain calculation [16], [17].

Because there has yet to be a 5G spectrum auction in India due to prohibitively high

prices, telecom firms are hesitant to acquire it, and development on 5G antennas

continues. The Indian government is assisting businesses in implementing Industry

4.0. Bajaj Auto, Maruti Suzuki, Ford, Hyundai, Tata Nano, and others have already

started automating their manufacturing plants in India with Collaborative Robots.

The government has pledged over $1 billion to start plants in Tamil Nadu, Himachal

Pradesh, Andhra Pradesh, Gujarat, and Rajasthan, among other states. With the help

of the IISc Centre for Product Design and Manufacturing, India's first smart industry

is taking shape in Bangalore, where machines can communicate with one another [3].

Various 5G antennas for Industrial IoT applications have been mentioned in the

literature. Paper [5] demonstrates multiband small twin band and triple band

antennas. A printed dipole with quarter wave monopole is used in this dual band

26

antenna, which is manufactured using a Rogers RT5880LZ with a height of 0.254

mm. The proposed structure operates in two bands at 5.8 GHz and 28 GHz, with

percentage bandwidths of 9.6% and 20.17 percent, respectively. Another quarter

wave monopole is put on top to provide triple band functioning. The proposed

antenna has a bandwidth of 12.71 percent, 11.32 percent, and 18.3 percent for the

3.5 GHz, 5.8 GHz, and 28 GHz bands, respectively. Variable gain is one of the

structure's key flaws. Paper [1] describes a robust dual-virtual-patch antenna array

for factory automation. The array of radiating elements uses patches of tinned steel

and substrate of a foam PVC (Polyvinyl chloride) material. Foamed PVC substrate

has a low moisture absorption rate, is lightweight, and has adequate chemical

resistance. The proposed antenna has a decent gain of 9.8 dBi, although it has a

narrowband characteristic. Paper [11] shows a compact flexible conformal antenna

built of acrylic sheet. The proposed antenna has multiband capabilities and is

inexpensive, although it has restricted bandwidth and gain. Paper [18] describes a

four port super wideband MIMO antenna. A CSRR (complementary split ring

resonator) and an L-shaped slit are carved into the sickle-shaped antenna element to

prevent Bluetooth, Wireless local area networks, and satellite communication. Its

disadvantages include its large size and limited gain. Paper [19] shows a

multifunctional MIMO antenna that can operate in several bands. To eliminate

mutual coupling between components, a two-element patch array is employed on the

top layer, and a Structure of T-shape is placed in the lower metallic layer. The

proposed antenna is made out of FR4 and has a gain of 2.7 dBi. This antenna's main

drawbacks are its large bulk and restricted bandwidth. Paper [10] describes half-

circular patch antenna with ultra wide band characteristics for IoT applications. The

proposed antenna may operate in several bands and has good impedance matching,

but its size is a major drawback. These articles give a comprehensive overview of

current antenna system research and development in the realm of industrial

automation.

In nations such as Germany, China, South Korea, the United States, and the United

Kingdom, millimeter-wave and multiband antennas are utilised for wireless

industrial automation. Due to the lack of commercial usage, the millimeter-wave

spectrum has a large amount of open bandwidth. mmWave bands have significantly

27

less interference than the busy unlicensed 2.4 GHz region, where many current

industrial wireless systems operate. Due to the high data rate and small size of

directional antennas at this range, radio-frequency identification (RFID) is also

moving towards millimetre wave identifiers (MMID). Machine-to-machine

communication with ultra-high reliability and minimal latency is possible because to

millimeter-wave antennas. The vast bandwidth of the millimeter-wave spectrum can

pave the way for a wide range of new industrial automation capabilities. The

Millimeter-wave band's huge bandwidth can open the path for a slew of wireless

factory automation capabilities. Automated visual monitoring and control,

smart logistics tracking, image guided robotic assembly, and fault identification are

all possible uses for wireless smart cameras. Robots, equipment, and other factory

automated systems having vision capabilities can interact effectively with things and

travel safely through their environments [1], [3].

Antennas employ a variety of technologies such as multiple input multiple output

[15], sophisticated beam forming [20], and smart approaches to enable multipath

communication, a high signal to noise ratio, and the ability to handle a larger

number of devices in a noisy environment. To reduce millimeter-wave losses, high-

efficiency and high-gain antennas are used. Outdoor communication is done with

directional antennas, while indoor communication is done with omnidirectional

antennas. The capacity of an antenna to operate in several bands and polarizations

needs a multi-layered design. A dependable multi-layering approach can ensure that

the substrate is used efficiently in these antennas. Ericsson, a Swedish multinational,

has announced 5G-based automated plants in China, Finland, the United States,

Sweden, Texas, Lewisville, and North America, where improved antenna will be

manufactured to power 5G networks for wireless automation. For IoT, Nokia teamed

up with Omron and NTT Docomo. Telia and Nokia worked with Intel to test a 5G

smart factory at 28 GHz. In its Nanjing factory, Ericsson collaborated with China

Mobile to test 1,000 high-precision screwdrivers that were automatically connected.

Ericsson's facility in Estonia is experimenting with broadband IoT [1], [3].

Various antennas for 5G-based IoT and industrial automation have been reported in

the literature. Paper [10] describes a multiband shared aperture antenna. The

proposed antenna works in the sub-6 GHz and millimeter-wave bands. The antenna

28

is made up of concentric slots on the ground plane that are fed via an open ended

microstrip feed line. The antenna in question can operate in the octaband frequency

range. The antenna's gain variation is a significant constraint. Maintaining a constant

gain throughout a wide range of operations is difficult. In paper [8] a 2*2 high gain

slotted patch array was shown. The proposed structure has a high gain of 23.9 dB

and efficiency of 99.9%, although it has a huge volume and limited bandwidth.

Paper [21] describes a dual band meandered line monopole antenna. Low gain is a

key shortcoming of the discussed antenna, which has a very small size of 0.250.3

mm2 and operates in dual bands at 28 GHz and 60 GHz. Paper [7] discusses a

textile-based electromagnetic band gap antenna. The proposed antenna has a flexible

design and is acceptable for operation, although it has a significant gain limitation.

Paper [16] describes an eight-port, four-element MIMO antenna. Between inter-

elements, spatial diversity is exploited. Although the presented antenna covers a

wide range of 5G NR bands, it has a low gain. Performance comparisons of various

antennas for 5G based industrial automation is shown in Table1.

Table 1: Literature Survey of 5G Antennas for Industrial Automation

Technique Merits Demerits a printed dipole with quarter wave monopole which are fabricated on Rogers RT5880LZ with thickness of 0.254 mm [5]

Resonates at 3.5 GHz, 5.8 GHz and 28 GHz band with bandwidth of 12.71%, 11.32% and 18.3% respectively.

Variable gain is major limitations of this structure

A durable dual-virtual-patch antenna array [1]

low moisture absorption, light weight and sufficient chemical resistance properties, good gain of 9.8 dBi

Narrowband operations

compact flexible conformal antenna made on acrylic sheet [11]

multiband operations and low cost

limited bandwidth and gain

CSRR (complementary split ring resonator) and an slit of L-shape are carved into the antenna of sickle-shape [18]

super wideband MIMO operation

Large size and limited gain

Two element patch array is used on top layer and T shaped element is introduced in ground plane [19]

multiband MIMO operation, reduced mutual coupling

Large size and limited bandwidth

29

semi-circular patch antenna [9]

multiband operation with good impedance matching

large size

concentric slots designed on ground plane and feed by open ended microstrip feed line [10]

octa-band operations Variation in gain

2*2 high gain slotted patch array [8]

high gain of 23.9 dB and efficiency of 99.9%

large volume and limited bandwidth

meandered line monopole antenna [21]

very small size of 0.25×0.3 mm2 and operates in dual band at 28 GHz and 60 GHz

low gain

Textile based Electromagnetic band gap antenna [7]

flexible design and suitable operation

limited gain

Eight port four element MIMO antenna [16]

covers various 5G NR band low gain

Challenges of 5G antenna for Industrial Automation

High connection losses, small scale fading and shadowing impact of millimeter

Wave band, narrow coverage range, reflections due to metallic body and

intermediate barriers, and other factors are major challenges in 5G antenna design.

Because the wavelength of 5G millimeter wave frequencies is so narrow, diffraction

around obstructions is a concern. Non-line of sight propagation is influenced by

considerable attenuation and minimum multipath connectivity, while line of sight

propagation is attenuated due to absorption by barriers. There have been very few

works on MIMO antennas at millimeter wave frequencies to far, with the majority of

work planned in the sub-6 GHz or microwave spectrum. MIMO antennas operating

in the millimeter wave range are required to develop for 5G in order to enable wide

bandwidth and high data rates with improved SNR and capacity. Very low side lobe

levels in the antenna radiation pattern are necessary for steerable beam

characteristics. There have been very few studies on 2D beam steering. At the mm

wave band, 2D beam steering is essential to enhance the signal quality, suppress

noise and reduce link losses. Circular polarized antennas will be more beneficial in

reducing polarization mismatching losses. At the mm wave bands, there are very few

works on circularly polarized antennas. Although only a few studies have reported

30

antennas with gains of 19 to 22 dB, there are other drawbacks such as big size,

complex design, limited beam scanning range, and low bandwidth. There have been

reports of 5G antennas with broadband properties in the literature, but they are

constrained by low gain [7], [16], [21].

Conclusion

With millimeter-wave 5G antennas, machine-to-machine communication with ultra-

high dependability and lowest latency is conceivable. The millimeter-wave spectrum's

huge bandwidth can open the path for many other modern factory automation services.

Automated visual tracking and surveillance, smart logistics inventory control, image

guided robotic assembly, and fault identification are among uses that can benefit

from wireless smart cameras technology. Robots, machines, and other factory

automation systems having vision capabilities can engage meaningfully with things

and travel safely through their environments. Antennas use a range of technologies

to provide multipath communication, a high signal quality, noise suppression, and

the capacity to manage a higher number of devices in a noisy environment,

including multiple input multiple output, advanced beam formation, and smart

solutions. Antennas which have good gain and efficiency are utilized to decrease

millimeter-wave losses. Directional antennas are used for outdoor communication,

whereas omnidirectional antennas are used for internal communication.


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Things Applications. In: 2021 7th International Conference on Advanced

Computing and Communication Systems (ICACCS). Vol 1. ; 2021:557-560.

doi:10.1109/ICACCS51430.2021.9441696

20. Ansari M, Zhu H, Shariati N, Guo YJ. Compact Planar Beamforming Array

With Endfire Radiating Elements for 5G Applications. IEEE Trans Antennas

Propag. 2019;67(11):6859-6869. doi:10.1109/TAP.2019.2925179

21. Burasa P, Djerafi T, Wu K. A 28 GHz and 60 GHz Dual-Band On-Chip

Antenna for 5G-Compatible IoT-Served Sensors in Standard CMOS Process.

IEEE Trans Antennas Propag. 2021;69(5):2940-2945. doi:10.1109/TAP.

2020.3025236

33

TAPAN NAHAR is presently Ph.D Scholar in Electronics and

Communication Department, Manipal University Jaipur, India. He

graduated with Bachelor of Engineering (BE) in Electronics and

Communication from Global Institute of Technology, Jaipur, India

in 2009. He did his M.Tech degree in the Digital Communication

from Poornima College of Engineering, Jaipur, India in 2015. He is having

experience of many years in teaching to undergraduate disciplines. He is pursuing

PhD degree in the research area of planar antennas for 5G mobile technology. He

has to his credit several technical papers in International & National Conferences

and books on various subjects.

SANYOG RAWAT is presently associated with Electronics and

Communication Engineering Department, Manipal University

Jaipur. He has been into teaching and research for more than

seventeen years. He graduated with Bachelor of Engineering (B.E.)

in Electronics and Communication, Master of Technology (M.Tech)

in Microwave Engineering and Ph.D in the field of Planar Antennas.

Dr. Rawat has been engaged actively in the research areas and published more than

80 research papers in peer-reviewed International Journals, Book series and

IEEE/Springer conferences. He has organized several workshops, seminars, national

and international conferences. He has been empanelled in the editorial board of

various national and International Journals. He has supervised nearly 30 M.Tech

Dissertations and 05 Ph.D’s and guiding 06 scholars for Ph.D. He has also edited the

books on proceedings of the International conference on Soft Computing Theories

and Applications (SoCTA-2016, 2017), proceedings of International Conference on

Smart Systems, Innovations and Computing (SSIC-2017) and International

Conference on Engineering Vibrations, Communication and Information Processing

(ICoEVCI, 2018) for Springer publication. His current research interests include

reconfigurable RF printed circuits, passive and active microwave integrated circuits.

He has visited countries like Japan, Thailand, Malaysia, UAE and Indonesia for

academic and research work. He is also a member of several academic and

professional bodies i.e. Fellow IETE, Member IE and ISLE.

34

CHAPTER 4

KATHPUTLI

The Art of Rajasthan

Satakshi Bisen

Student, Master of Fashion Management

National Institute of Fashion Technology

Kolkata, West Bengal- 700098, India

[email protected]

******************************************************************** Abstract: The Rajasthan Kathputli, an attractive art because it’s a part of Rajasthan’s

rich culture and the style of presentation that this craft offers. Music plays a very

important role. It is always amusing to see brightly colored puppets dancing on stage

in quirky steps, making squeaky noises. There are different stages of making

Kathputli and many people involved in it. The research highlights the decline in this

art and the economic status of market and artisans. The research looks into the

making of Kathputli and also looking into the problems and solutions.

Keywords: Kathputli Making, Kathputli Market, Livelihood, Supply Management,

Artisans Productivity, Community, Factors Analysis.

******************************************************************** Introduction

Embellished with beautiful bright coloured clothes, large beautiful eyes, nimble

limbs that dance in from of fascinate audience, it is not a dance performance

performed by living beings, but the puppets move with the fingers of puppeteer [2].

The Kathputli in Rajasthan are one among the well- known sources of amusement

within the state. Kathputli is an ancient sort of artistic expression that’s a variation of

storytelling or human staging. It is a well-known form of folk entertainment. In this

art a drama is created that’s primarily acted out by specific character and

manipulated by a puppeteer. Kathputli dance and performance is one among the

main tourist attractions of Rajasthan [10].


35

The word Kathputli made from two words “Kath” that means wood and “Putli”

which means toy. Traditionally it was earlier promoted by kings and well-off

families [22].

Figure 1: Kathputli

Roots of Kathputli

The community involved in craft belongs to nomadic community known as Putli

Bhatt or Nat. Originally, they belonged to Nagaur area in the Marwar region. Bhatt’s

are the performing artists who moved from cities to countries to showcase the Art

[18]. They entertained gatherings by narrating the stories and achievements of

mythological characters and social traditions. They gained popularity among the

royal kingdoms of Rajasthan and received great honour and appreciations from the

Royal Courts of Kings and Queens [21].

Figure 2: Traditional Kathputli [21]

Today Bhatt community settled in different parts of Rajasthan and are still practicing

this traditional art. The language they speak is Marwari. Rajasthan is the hub of

Kathputli tradition.

36

Figure 3: Kathputli Show

Figure 4: Kathputli show organized by the people of Helping Hand, NGO

Design Inspiration

The inspirations for the design of Kathputli taken from the culture and tradition of

the stories supported by the legends of the specific region/ locality. These are

moulded by the regional costumes and ornaments. The Rajasthan historical tale or

local legends were the themes to play [11]. The most common characters involved

are Raja, Rani, Anarkali, Jogi, Sapera, Jadugar (King, Queen, Court Dancer, Saint,

Snake Charmer, Magician)

Figure 5: Common faces of Kathputli [2]

37

Figure 6: The Kathputli face depicting dancer [2]

Figure 7: Faces of Mughal Emperor [2]

Figure 8: The Chinese Kathputli face [2]

Figure 9: Image of Rakshasa (Demon) [2]

38

Figure 10: Face of Yogi (Saint) [2]

The Kathputli are very famous art around the world and are related to royal families

and native rulers. Other characters involved in the performance are Kacchi Ghodi,

Came, Elephant, hangings, etc. The another most famous characters are Amar Singh

Rathore, Maharana Pratap, Heer Ranjha, etc. [7] and nowadays the Kathputlis

helped in creating awareness through their performances on the themes like AIDS,

Polio, Child Marriages, Dowry and many other social evils [2].

Figure 11: Heer Ranjha

Apart from the basic Kathputli made, there are many other types that were not used

for shows, they are promoted as gifting and installation.

39

• Out of all, the one of the interesting one was Sunday-Monday, it is like one

head on either side of the wooden log. While marketing this product they

used a slogan -

“Sunday ho ya Monday, roz khaao aande, kal se jaao school, nahi gauge toh

roz padenge dande.”

Figure 12: Sunday- Monday Kathputli

• 4 faced puppet - It is like head is carved in all four sides of the wooden log.

Figure 13: 4 Faced Kathputli

40

• Life sized puppet- The puppet is sized up to 10ft tall. and is made of

thermocol. This are only used for installation.

Figure 14: Life Sized Kathputli

Making of Kathputli

The Kathputli are highly embellished with colourful, make-up made up of Adoo or

Mango wood stuffed with cotton. A very attractive features of these Kathputli is

their elongated and stylized eyes. These are generally one and half feet in height.

There is difference in male and female Kathputli. Female Kathputli don’t have legs

while the male ones have legs with footwears. They have free body movement; with

their hands, neck and shoulders.

Figure 15: Kathputli (Near Hawa Mahal, Jaipur)

41

Kathputli are made up of wood, the wood used is Adoo, this is a plant on which

goats fed and the other wood used is mango tree wood. There is legible weight

difference in both of the wood. The Kathputli made by Adoo wood is comparatively

lighter in weight then mango wood. Mango wood is soft and easy to be carved, but

the availability of Adoo is more and is cheaper. After carving the Adoo wood is left

to dry in the sun, it shrinks and creates dents but that does not happen in mango wood.

Figure 16: Cutting of Adoo wood and basic carving

Tools and raw material used to make Kathputli-

1) Wood

2) Cloth pieces

3) Stuffing material like cotton

4) Needle

5) Paints and paint brushes

6) Woolen yarn

7) Ribbons/ laces

8) Chalk Matti

9) Sand paper

10) Carving knife

11) Touch wood (used as a top layer, to give shinning)

12) Hammer and Nails to attach strings

42

Process of Making

After buying the wood, as the wood is a bit moist, it is left to dry in the sun. (now it

depends on the season, what amount of time it takes to dry)

Figure 17: Moist Adoo wood set in sun for drying

The puppet making process starts with the making of face. The artist uses dry logs of

wood to carve out faces of the puppets. Different characters have carved differently

as per their facial expression. This process is called “Chilaai”.

Artist carving the faces of puppet with the help of carving knife.

Figure 18: Semi dried Adoo wood

43

Figure 19: Top layer of wood being removed

Figure 20: Wood being carved and basic facial feature of the

Kathputli are being carved out of it

Figure 21: Kathputli face

The wood log used to carve is semi-moist so that the shape to be carved out of it is

carved easily. After the carving is done it is left to dry properly in sun.

44

Once it is dried up, it is rubbed with sandpaper and a paste is applied on it, which is

called “Chalk Matti”. This paste is applied on the Kathputli face because when the

Kathputli is left to dry in sun after carving it shrinks and cracks and dents appear on

it, so this paste helps to fill the gap and even out the top most layer. Further, it is kept

in the open so that the moisture is worn off.

Figure 22: Chalk Matti

Figure 23: Chalk Matti applied on the Kathputli face

After that the extra dust of dried Chalk Matti is removed with the sand paper and

then the paint is applied on Kathputli.

45

Figure 24: Sand paper used to even out the top surface of the Kathputli face

Then, firstly a base coat is applied which ranges from pale white color to matching

the skin color (double coating of the base) and then eyes, lips and other detailing is

done. Then the top coat of touch wood is applied for shinning and to protect it from

damage and then it is left to dry again.

Figure 25: White base coat being applied on the Kathputli face

46

Figure 26: Detailing done on the face of the Kathputli

Figure 27: Detailing done on the face of Kathputli

Figure 28: Detailing done on the face of Kathputli

Once the head of Kathputli is dried completely, decoration of these starts with

stitching of its cloth. These clothes, are usually hand stitched and stuffed with old

rags and clothes (legs are made with saree fall). And then the ornamentation of these

47

craft is done by lacing its garment and putting a jewelry or an instrument etc. And at

the end strings are attached to it, as these are string puppet, attaching string is the

most important step. Artisan use these strings to manipulate the puppet during the

performance. They are attached to neck, shoulder hands, legs and head of the

puppet. These strings are strong thread usually woolen threads are used to bear the

load of the puppet.

Figure 29: Kathputli head and hand stuffed with materials

Figure 30: Stitching bottom of the garment to attach it to head

Figure 31: Adjusting the garment to the head of Kathputli

48

Figure 32: Fixing the garment with needle and thread to the

head of the Kathputli

Figure 33: After fixing the garments, lacing is done on the Kathputli to

give it a traditional look

Figure 34: The look after lacing

To give the bottom part of Kathputli a better look an extra layer of fabric at the inner

side of the garment is attached to give it a fluffed look.

49

Figure 35: Layer of fabric is attached

Figure 36: After stitched it is attached to the bottom fabric of Kathputli

Figure 37: The final look

Figure 38: The women is ornamented with choonri and tika and male with flute

50

Once all the garment and ornamentation work are done, finally strings are to

be attached to the Kathputli head, shoulder, hands, legs and neck to make a free

movement for the show. and now the Kathputli is ready for the show.

Field Visit

The research was done in a slum area in Lal Kothi named as Kathputlinagar,

Jaipur. It is the home for Kathputli artisans.

Figure 39: Area in front of Kathputlinagar

Figure 40: Locality

51

Figure 41: Toilets constructed by government

Figure 42: Dwellings of Kathputlinagar

Figure 43: Pre-school of the area

Figure 44: Playground in Locality

52

The above shown field once was a dump yard. A German student who was on his

research tour, reached to this place and saw dumpster and planned to change that

area into a place where children of Kathputlinagar can play, and also painted this

place with different cartoon characters, and beautified the place.

Figure 45: Painted wall of Playground

Figure 46: Top view of Kathputlinagar

Figure 47: The smiling childhood

53

Kathputlinagar Survey

Roughly 5000 people live in this slum area. Their most common source of income is

crafting Kathputli, music, snake charming, folk performances and other work

include low- skilled works. This area is in the central part of Jaipur city having the

government offices like Rajasthan Housing Board, The Rajasthan High court and the

Employee State Insurance Offices [12].

Figure 48: Map view of Kathputlinagar, Jaipur

The artists of Kathputlinagar gave very warm welcome to all the researchers and

always ready to talk about their art.

Conversations with artists

Anil Bhatt works at a hotel in Vaishalinagar, Jaipur, as a puppet show organizer. His

whole family is involved in this craft. They were 10 brothers and sisters. According

to him they earn Rs. 10,000 per 3 shows. They conduct shows in the cities of

Rajasthan as well as outside the state. They have also performed shows in Germany,

Iraq, France, New York, etc. and the expense occurred for the travel and stay is

barred by the people calling them to perform.

54

Figure 49: Anil Bhatt

Figure 50: Anil Bhatt showcase their art

A woman named Tabassum belongs to Uttar Pradesh and lived in Kathputlinagar

from the past 15 years. She has 3 children and out of them she could hardly manage

the education fees of one child.

Jagdish Bhatt is the persons who is known for the making of King and Queen

Kathputli. His Kathputli are ornamented very well.

Figure 51: King & Queen Kathputli

55

Figure 52: Carving the King's head

Figure 53: The craftsperson Jagdish Bhai

Figure 54: Head carving of King Prithvi Raj Chauhan

56

The craftsperson Bhajanlal is a person who makes the life sized Kathputli. The size

ranges from 6ft – 10ft. these are mostly used for the installation purpose only. They

are not made with wood, instead from that a thermocol is used and sticked together

with glue and paper. They are the customized Kathputli made as per the client’s

requirement. The cost of one life size Kathputli is around Rs. 60,000.

Figure 55: Life sized Kathputli by Bhajanlal

This is identified that from the one-meter saree only one garment is made and the

cost of per saree could be around Rs. 150 to 200. The overall cost of making one

Kathputli is Rs. 300-400 but as per the market rate the cost of one medium size

Kathputli is Rs. 100- 150.

From the data it is very clear that. The artisans did not get the fruitful cost for their

handworks. And as per the market research it is in highlights that the demand of

Kathputli is declining and it is limited up to the occasion decorative items which the

planners takes from the sellers on rent.

57

Literature Review

According to the study of [6] it is found that Kathputli shows are the most powerful

tool for education as it made people aware of the social problems that the country

face and try to give the possible solutions. it is hence considered to be the ancient art

of Storytelling.

[3] observed that the artisans make Kathputli to entertain people but struggle to meet

end needs.

[10] have discovered the pressure of falling demand of Kathputli due to the poor

income of artisans and their growing families which led to the artisans to taking up

alternate professions for their survival.

[20] identified that the art of Kathputli is dying because of lack of interest in modern

era and fails to take up modern social issues in its shows.

According to the study conducted by [20] it is found that the biggest reason of

decline in the demand is its competition from Electronic media which becomes the

most preferred way of entertainment. Peoples loose interest in watching these show

as they found the electronic media more appealing and comfortable. And this creates

lack of interest in buying this traditional form of art.

[1] found out that the Kathputli has descended to the level of begging as there were

no social respect for the artisans and sellers.

According to [19] it is identified that there is lack of Craze among the youth due to

the growing internet access and lacking the knowledge about our traditional arts.

According to the interviews conducted by [3] it is heard that NO ONE CARES

ABOUT KATHPUTLI ANYMORE it is found that due to less promotions of this art

there is rapid decline in the business of community and that pushes the community

to poverty.

In the research conducted by [4] the sale of Kathputli fluctuates as per seasons and is

only limited to the point of attractions only for foreigners and may be sometimes

used by some event planners.

58

Aims and Objectives

• To study the evolution of the Kathputli art form over the years

• To study the handworks required with the tools and techniques used to make

Kathputli come to life

• To study the livelihood of the artisans involved in the art and their

perspective in the art with interviews and observations

• To analyse the demand of Kathputli in Market today and also the perspective

of the sellers.

• To find out the factors affecting the steep decline in the consumer buying

pattern

• To conclude with some recommendations for the marketers and artisans

operating in this industry.

Research Methodology

The research design used in the study is partly exploratory where the attention is at

discovering various ideas and providing a comprehensive presentation of a

phenomena. This particular design is used for the analysis to be more precise through

investigation. The other design used is of the descriptive type, where the study is

aimed at describing briefly the traits of the cluster and the art form which is under

study [14].

The method of research adopted was of the survey type, [23] which means that

information was collected from a group of people and was analysed to get to a

particular conclusion. A part of the study is also based on comparative study which

has been done to compare the product attributes and the overall market opportunities

available for this particular craft [5].

The research is based on quasi- quantitative data [24] which means that data was

collected by asking questions to the artisans belonging to the area Kathputlinagar.

This was done to closely identify the factors affecting the steep decline in the

demand of Kathputli craft, further affecting the overall market opportunities.

In the study, a mix of both primary and secondary data has been used [13]. Primary

data is the one which was collected through direct and first approach and on the

59

other hand secondary data is the one which was collected through other sources like

clusters records, prints in journals, magazines, books and similar sources. Secondary

data has also been collected by going through various research papers, journals, and

magazines. Primary data, for the research, was collected through non-structured and

informal interviews from the artisans practicing this craft.

The data was primarily collected through personal interviews for a better understanding

of their thinking and opinions. Descriptive research analysis is further done so that

specific patterns relating to the consumers’ purchase patterns can be identified [8]

and conclusions could be based on these findings and appropriate steps could be

taken for the artisans’ betterment.

The sampling element consists of each and every craftsperson who belongs to

Kathputlinagar, Jaipur and has been interviewed [15]. The sampling frame [15]

consists of the all people relevant to the research under study.

All the responses were collected from people working in this art. The survey was

conducted over a period of five days, in the second week of September, 2019.

Findings and Discussions

Despite of being near to the government sectors, this slum area and its people faces a

lot of problems in terms of Housing, Hygiene, Sanitation, Education, and many

more social issues.

The houses are very small and half constructed by the government. The roofs are

covered with irons and rocks. A typical family of Kathputlinagar consists of 5 to 7

family members living in small house with one or maximum two rooms, kitchen/

cooking facilities are often outside the house. A small washroom facility might be

available in just 20% of the houses, rest of the slum have to move to few steps to use

public toilets provided by the government. They also need to collect the water from

the common government tap which is only available at 5 am for 2 hours every

morning [12].

The people of Kathputlinagar struggles with disease and illness due to bad hygienic

and sanitation problems. For Kathputlinagar it is most common to go in the public

60

toilets constructed by government or in the open fields which is a garbage disposal

area. An open drainage system in the slum runs like river having water full of bacteria.

The most common illness found there are diarrhoea, cholera and malaria [12].

There were only one school up to eighth standard and after that the children have to

travel to Bankrot for further studies in Helping Hand School. The children who are

not able to manage the travelling cost, left their studies and engage in making

Kathputli.

The people of the community still not aware about the concept of family planning,

hence they have such big families and this may be one of the reasons why child

marriage still prevails in this community.

The government hesitates to give the facilities because they think that this will

acknowledge the existence of the slum [12].

With the introduce of new style, forma and techniques of artistic exchange affected

the art of Kathputli [20].

There is a gap between the Kathputliwalas to market and customer. The value of

Kathputli was ranging from low range to a very high range and they were more

inclined for home décor rather than shows. Now for a new trend people are adapting

Kathputli for events but the order for the art is directly taken from the artisans in a

wholesale rate so a proper income is not earned as expected sometimes. The sellers

sometimes don’t get the Kathputli as expected and after GST the artisans and shop

keepers do not receive the income as expected.

After market analysis then comes the area of the homes of artisans. There is

suffocating squalor and the flimsy housing structures lining both side of a string of

cramped, trashed choked alleys, the squat concrete huts had an air of solidity but the

whole family from generations lived there. These artists lived in a legal settlement

without running water, electricity or sanitation [17]. But they managed to survive

and wants to give education to their children but they lack behind in this too as they

can’t afford the cost related to education and also, they believe in child marriage

61

because of which the children of artisans had to leave the studies in between and

start earning with their parents. The main gap which was seen from this sort of

research and study is the sale of Kathputli which fluctuates as per season which

leads to the financial issues of the family, overall it is struggle for the artisans.

Conclusions

The art is imbibed in the blood of Kathputlinagar residents and they don’t see this

only as a source of living but also a pure divine for them. Every family came up with

different stories some are hopeful, whereas some motivates. A motivated story arose

when people with no place in their house, made puppets on street with other

puppeteers. People are very hard working.

The Self-Help Groups (SHG) should approach and consequently supporting the

women on their way to self- employment and also contributing to a common fund

which is then used to meet the needs during emergency on the basis of mutual help.

Lack of education and training is the most effective problem of Kathputlinagar and

for this the activities like learning and earning should be promoted by the government

for such slums. The government should provide day care centres where infants get

basic health check-ups, healthy food and education and these activities should be

formalized on the continuous basis. There is need to overall development of

Kathputlinagar by NGOs or local authorities and will initiate improvement wherever

possible in terms of hygienic conditions and the essential basic needs [12].

As the profession continues from one generation to other, Kathputli may have a

distorted future as the tradition of adapting family profession dies. But, the people of

Kathputlinagar still believes that the future generation is free to make their choices

in their career but they will always be attached to their roots [18]. Also, the other

reason of decline in the art is due to the impact of digital age.

It is very surprising to see that surrounded by all the crisis, the people of Kathputlinagar

still carrying their rich artistic and cultural heritage [1].

The importance of culture which foreigner give to Kathputli is more respectful and

appreciated and the artisans want a support to rise in their own country for the better

future and survival of their families.

62

The artisans must be recognised and rewarded which motivates them to continue the

art form.

With the adaption of modernity among youth there should be the birth of modern

themes and well as the international scenes that creates a new interest for the

traditional Kathputli to flourish [20].

References and Bibliography

1. Ali, Mir Ahammad. "Puppets of India." (2018).

2. Baral, Bibhudutta, Anisha Crasto and Anushree Kumar. "Kathputli- Making

of Puppet." n.d.

3. Chandola, Mohini. "The lost art of Kathputli." India Interior (2019).

4. Chattopadhyay, Anindita. "Puppetry as a form of Mass Communication:

Indian perspective." Community & Communication 5 (2017).

5. Esser, Frank and Rens Vliegenthart. "The International Encyclopedia of

Communication Research Methods." 2017.

6. Ganguly, Nivedita. "The ancient art of storytelling." (2014).

7. Grund, Francoise and Puran Bhatt. "Kathputli Ka Khel." (2012).

8. Kalsbeek, Reneta. "Where to Start With The 4 Types of Analytics." Iteration

Insights (2020).

9. "Kathputli." Handmade in Rajasthan (n.d.).

10. "Kathputli- Jaipur." (n.d.).

11. "Kathputli- The Puppet Dance of Rajasthan." Culture of Rajasthan n.d.

12. "Kathputlinagar, Jaipur "The Puppet Maker Slum"." Saarthak Initiatives of

Relevance n.d.

13. Kothari, C. R. Research Methodology. New Age International (P), Limited,

2004.

14. Kothari, C. R. "Research Methodology- Methods and Techniques." Kothari,

C. R. Research Methodology. New Age International (P) Limited, Publishers,

2004. 35-36.

15. Kumar, Ranjit. Research Methodology: a step-by-step guide for beginners .

New Delhi: Sage Publications, 2011.

63

16. Lundie, Philne. "Male consumers’ expectations of the fit of ready-to-wear

business apparel and the influence on the purchase decision." 2017.

17. Maverickbird. "Puppeteers, Magicians and Fire Eaters of Delhi." (2017).

18. Mishra, Vaibhavi. "Kathputli: String puppetry from Rajasthan." (2019).

19. Parmar, Atul Kumar Singh. "Tradition of Kathputli." (2020).

20. Singhania, Nitin. "Art and Culture." Secure Synopsis (2020).

21. "The Amazing History of Kathputli Art." (2015).

22. "The Story of Kathputli." (2010).

23. "Types of research." 2009.

24. Wagh, Sulbha. "Benedictine University." Monday October 2021.

researchguides. ben.edu. <https://researchguides.ben.edu/c.php?g=282050

&p=4036581#:~:text=Primary%20data%20refers%20to%20the,collected%2

0by%20someone%20else%20earlier.&text=Surveys%2C%20observations%

2C%20experiments%2C%20questionnaire,journal%20articles%2C%20inter

nal%20records%20e>.

64

CHAPTER 5

DIGITAL EDUCATION TURBULENCE AMONG EDUCATORS

AND LEARNERS DURING THE COVID-19 PANDEMIC IN INDIA

Geeta Khanwani Research Scholar Department of Psychology Manipal University Jaipur Dr. Suyesha Singh* Assistant Professor Department of Psychology Manipal University Jaipur [email protected] (Corresponding Author) ******************************************************************** Abstract: The era of digitalisation worked as a boon for the human population across the globe during the turmoil phase of covid-19 pandemic wherein the only possible and accessible way for communication was ‘digital platforms’. The gigantic hit of covid-19 pandemic brutally affected majorly all realms of human population out of which the two major areas i.e., health and economic crisis seize the attention of global organizations resulted in shadowing of other equally relevant areas out of which one of them is ‘educational turbulence’ faced by the educators and learners due the sudden grounding of ‘digital education’. The concept of digital education was emerging in the developing countries such as in India prior to the pandemic but sudden grounding of full-fledged digital educational action plan just to continue educational activities during the covid-19 restrictive phase remarkably challenged the educators and learner’s population. Hence, it is crucial to explore the educators and learner’s perspectives towards digital education especially under the light of turmoil phase of Covid-19 pandemic. Therefore, the present study aims to explore the educators and learner’s perspective towards the digital education during the restrictive phase of covid-19 pandemic in India. For exploring the challenges and issues faced by educators and learners towards the digital education system during the Covid-19 pandemic, secondary and tertiary data was retrieved from the various databases. Based on the review of studies, several gaps were identified, paradigm


65

shift from face-to-face educational system to digital education system worked as a ray of hope to trespass the educational turbulence but there is quite a limited understanding on the adaptation perspective of the Indian educators and learners towards a full-fledged digital education system which was implemented in no time after the restrictive phase of covid-19 pandemic. Henceforth, it is important to understand the Indian educators and learner’s perspective towards digital educational system during the surge of covid-19 pandemic. This study highlights the Indian educators and learner’s perspectives, challenges and adaption towards the digital education system especially during the restrictive phase of covid-19 pandemic which will also contribute towards the formation of better policies and intervention models for strengthening the grounding of digital education system in India. Keywords: Digital education, Educators, Learners, Covid-19 pandemic

******************************************************************** Introduction

The gigantic hit of covid-19 pandemic landed the global population into a health

disastrous state majorly affected all realms of human population. The sudden

implementation of Covid-19 pandemic restrictions created an imaginary fencing

over the physical movements of the social beings due to which several nations

around the globe imposed a nationwide lockdown as a drive to limit the human

transmission of coronavirus. Similarly, Indian government also announced its first-

ever nationwide lockdown on 25th March 2020 restricted all social beings under the

lockdown norms. The restrictive phase of lockdown confined full-fledged human

activities resulted in wide acceptance of digital grounding from managing economical

flow to continuing educational activities [1,2,3]. Due to the unprecedented glitch on

health and economical domain, majority of the global organizational focus turned to

uplift the health and economical downfall because of which educational turbulence

remained uncovered during the restrictive phase of Covid-19 pandemic, wherein

educational system transit from traditional face-to-face educational grounding to

full-fledged digital educational system. Digital educational system was in the

emerging stage prior to the covid-19 pandemic especially in the developing nations

like India but rapid adoption of full-fledged digital educational system showed the

dualistic nature i.e., on one side, it remarkedly offer the opportunity to the

66

technological outlook in the realm of educational system but on a contrary, sudden

transitory phase challenged the educator and learner population due to the hazy

digital educational model in the early phase of covid-19 pandemic restrictions. It has

been reported that some educational institutions temporarily seized the onsite

educational activities for the learners in the initial phase of covid-19 pandemic

lockdown while some private educational institutes in no time shifted to the digital

education system as a mode of educational continuation but the unprecedented

nature of covid-19 pandemic transitory state landed Indian educators and learners

under the baffling state. In current times, human population has adopted the ‘new

normal’ and geared-up for the continuation of human activities in all realms of

human working but only educational system is still in the wavering state wherein

most of the educational institutes remained closed for the learners as it has been

anticipated that maintaining the precautionary grounds for health and safety among

the learners will be quite challenging if the educational institutes adopt the prior

traditional educational system [4,5,6,7,8]. Therefore, it crucial to understand the

perspective of Indian educators and learners towards digital educational system so as

to formulate a better intervention model promoting the robust educational system in

India especially in the challenging times such as covid-19 pandemic.

Digital Educational System Among Educators in India

Digital education system introduced as a well-designed model for the young India

prior to the covid-19 pandemic where most of the educational institutes especially

urban areas universities/ schools, adopted smart-classroom using digital technologies

such as, artificial intelligence, audio-visual learning mode, etc, blended with the

traditional face-to-face learning method to enhance the educational quality for the

learners. While the digital educational system was in the budding stage where

several training programmes were initiated by the educational organizations to

upskill the educators towards digital education sudden invasion of covid-19

pandemic burdensome the Indian educators into an impromptu state where they were

supposed to shift towards digital teaching mode with limited training and

technological equipment’s support in no time. It has been reported that initial

restrictive phase of covid-19 pandemic rose major challenges among the Indian

67

educators such as, limited digital training, limited or no proper digital equipment’s

for online classes conduction, limited or no internet accessibility, high-internet

pricing, limited and inadequate workspace set-up, more time investment for the

preparation of presentations and lectures, over-stretched working hours, handling

technical glitches, limited support and accessibility from the technical teams,

managing online interaction with the learners over the face-to-face live interactions,

etc, in short, rapidly adopting the new normal online education system without any

adequate prior training [9,10]. No doubt, rapid transition from traditional educational

system to full-fledged digital educational system created a state of educational

turbulence among the Indian educators but in no time, Indian educators well-versed

with the online learning system and created a new opportunity for digital educational

grounding for young India. Indian educators adopted the digital educational model

as an opportunity to reach to masses at same time, record the lectures and circulate

them among learners residing in various areas of the globe, preparing the presentations

using the digitalisation, blending the traditional learning with the digital learning,

etc, in brief, unfolding the grounds of digitalisation to build the advance educational

system [11,12,13].

Digital Educational System Among Learners in India

An unforeseen digital educational system adopted as a mode of continuation of

educational activities during the restrictive phase of covid-19 pandemic which

worked as the only possible solution to overcome the obstructive wave of covid-19

pandemic and regulate the academic calendar as per the given time frame. But this

rapid overnight shifting drifted the young scholars into two board categories wherein

young learners faced the digital divide situation especially in a middle to low-

income countries like India [14,15,16]. On one side, young learner especially living

in urban areas smoothly adopted digital education system with the help of some

formal guidelines and training from their respective educational institutes, hi-tech

generation was also equipped with the hi-tech devices and high-speed internet

accessibility, etc, in brief, majorly urban scholars quick-witted to adapt digitalisation

as a mode of learning. While, on a contrary, it has also been reported that approximately

320 million Indian learners faced the rift of digital divide due to several disparities

68

such as, economical, gender differences, resources, etc [17,18,19]. Digital divide

became a major challenge in the realm of digital education system as most of the

Indian learners faced the digital drift during the restrictive phase of covid-19

pandemic as a resulted in seizing of educational activities for the under-privileged

learner’s population. Another, major challenges reported among the Indian learners

were lack of prominent digital training and guidance, limited availability of

technical resources, lack of motivation towards learning, limited attention span,

feeling of psychological distress due to disturbed academic calendar, academic

activities restricted the other extra-curriculum activities, technical glitches, lack of

learning quality, etc, in brief, majority of the learners faced difficulties in adopting

the sudden transitory phase of unprecedented digital educational grounding and

unfortunately some learners cannot even get the privileged to unfold the realm of

digital education system [20,21,22].

Indian Government Interventions Towards Digital Educational System

In the era of digitalisation, India also formerly adopted the digital platforming in

year 2015 to promote the E-networking among Indian population. Certainly, digital

divide was reported in the population due to economical and regional disparities. To

overcome from the digital drift, Government of India launched ‘BharatNet’ program

for bridging the digital gap between urban and rural Indian population. The sudden

outbreak of covid-19 pandemic drastically shifted all human activities towards the

digital grounding where the massive struggle of digital divide among Indian

population was highlighted under the reports. India being the second largest youthful

country faced the significant trouble of digital divide due to which majority of the

young scholars doesn’t even get the opportunity to access the digital world of

learning. To overcome from the hazardous effect of digital divide in Indian digital

education system, Indian government launched several digital education programmes to

promote digital education among the underprivileged population of India, several

initiatives taken by the Indian government are as follows:

National Digital Educational Architecture (NDEA): The programme was initiated

by the Indian government from the union budget 2020-21 aimed to strengthen the

69

digital infrastructure and support to the digital learning activities to promote digital

education system among educators and learners.

PM eVIDHYA Programme: Another digital education programme was launched in

May 2020 expected to benefit approximately 25 crore learners. The programme

aimed to ease the digital education system for the educators and learners by making

a more flexible and convenient e-learning platform where esteemed universities can

also offer distance/e-learning programmes to the scholars.

DIKSHA: As digital education transformation was already initiated in India prior to

the covid-19 pandemic, DIKSHA (Digital Information for Knowledge Sharing) was

launched in September, 2017, which offers e-learning of school-curriculum based

learning for educators and learners.

SWAYAM: Another major e-learning portal was launched by Indian government in

year 2017 which provides dynamic online courses at an affordable cost to all citizens

of India especially designed for underprivileged learning population.

NISHTHA: National Initiative for School Heads and Teachers Holistic Advancement

(NISHTHA) programme was launched from the union budget 2020-21 to promote

the training of schools and teachers towards digital educational system. It is

estimated that about 5.6 million educators will be enrolled under this programme to

upskill their digital educational training.

Thus, Indian government has been continuously hammering to strengthening the

roots of digitalisation in India especially digital educational system which will allow

the Indian educators and students to share the learning platforms using the advancement

of technological domain. Additionally, constant efforts have been applied by the

public and private sectors of India to equip the young scholars with the advance

realms of digital education [23,24,25,26].

Conclusion

To summarize India’s digitalisation drive has been spreading fiercely from economical

to educational grounds. Digital education drive has been significantly adopted by the

70

Indian government to establish digital infrastructure and upskill the young learners

with the advanced digitalisation. Prior to covid-19 pandemic, digitalisation was

slithering among the Indian population with the aim to reach masses by year 2025

and upskill the Indian population with strengthen digital infrastructure and

dominance. But unprecedented outbreak of covid-19 pandemic restricted most of the

human activities under the virtual boundaries. Similarly, educational institutional

activities also rolled under the covid-19 restrictive phase resulted in closure of all

educational institutes for an indefinite period due to which educational system transit

from traditional face-to-face learning method to full-fledged digital educational

system in no time with no prior formal training. The transitory phase of sudden

adoption of digital education system created educational turbulence for both

educators and learners especially in low-middle-income countries like India, where

digital education was in emerging stage prior to the pandemic.

Henceforth, the present study aimed to understand the perspectives of Indian

educators and learners towards full-fledged digital educational system especially

during the restrictive phase of covid-19 pandemic. It has been concluded that digital

education showed the dualistic nature i.e., on one side it worked as a ray of hope for

educators and learners to adopt a reform movement of digitalisation with hi-tech

quality of education but on a contrary, it triggered the challenges in front of the

educators and learners due to which one of the major concerns i.e., ‘digital divide’

issue outstands during the pandemic phase. To conquer the challenges of educators

and learners and create an equivalent opportunity for all young scholars and

educators, Indian government also initiated several digital educations drives to

promote and strengthen the digital education infrastructure and upskilling the

educators and learners population of India. A suggestive digital education

intervention model has been represented in Figure 1:

71

DIGITAL EDUCATION SYSTEM IN INDIA

PUBLIC SECTOR PRIVATE SECTOR

Government Intervention

Programmes Reaching Masses via Traditional media as well i.e., radio, TV, etc

Equipping underprivileged population with Hi-tech devices

and training. Promoting Small-groups

educational system

Facilitating formal digital training program for both learners and

educators. Promoting upskilling the new

realms of digitalisation by both learners and educators.

Educational institutes initiatives towards equipping learners and

educators with Hi-tech resources. Blended learning with e-learning

with face-to-face learning

Consistent Feedbacks from Learners and

Educators

Research Studies

Better

Intervention Model

Figure 1: Digital Education System in India Model

Limitations

A shortcoming of the present study is that it is a narrative review study based upon

existing literature not following quantitative research design. Another limitation is

that this study has majorly focused on perspective of Indian educators and learners

towards digital education system in India especially during the restrictive phase of

covid-19 pandemic. In addition, majorly digital education system domain has been

discussed under the present review due to specificity of a board digitalisation

domain doesn’t get much light.

72

Research Implications

To our best of knowledge, this is the first investigation to understand the perspective

of both Indian educators and learners towards digital education system especially

during the restrictive phase of Covid-19 pandemic. Furthermore, this review supports

the existing literature on the perspective of educators and learners towards digital

education system especially during the unprecedented events like Covid-19 pandemic.

For the future researches, it is recommended that consideration of cross-sectional

and longitudinal study can contribute towards an in-depth understanding of

perspective of educators and learners towards digital education system especially

during the varying timeline of the covid-19 pandemic situations. Additionally,

cultural and regional dynamics can also be considered so as to understand the

dynamic perspectives of educators and learners in India under the dynamic grounds.

Furthermore, studies can also explore the void of ‘digital divide’ prevailing in Indian

culture due to various disparities.


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CHAPTER 6

INFLUENCE OF SOCIAL MEDIA ADDICTION ON

ANXIETY, DEPRESSION, AND STRESS

Regina Bahl

Department of Psychology


[email protected]

Dr. V. Vineeth Kumar

Associate Professor

Department of Psychology



******************************************************************** Abstract: Today social media has become an essential medium of human interactions.

Many individuals spent a significant amount of time interacting with others on

various social media platforms. Routinely spending too much time on social media

platforms or social media addiction can lead to adverse psychological consequences

among human beings. Thus, the study's objective was to assess social media

addiction's role in stress, anxiety, and depression among adults. For assessing this, a

sample of 140 participants between eighteen to forty years of age was administered

Bergan Social Media Addiction Scale and Depression, Anxiety, and Stress Scale.

The data was collected online. Descriptive analysis indicated the time spent by

social media users on different platforms. Similarly, Pearson correlation and linear

regression analysis were conducted on the data using SPSS 21. The results revealed

a significant association of social media addiction with stress, anxiety, and depression.

However, there was no significant difference between males and females on social

media addiction and other psychological problems. Thus, the current study highlights

the role of social media addiction in various psychological problems in individuals.

Keywords: Social Media, Facebook, Instagram, DASS, Disorders, Addiction

********************************************************************


77

Introduction

Social media addiction is the abuse of social media platforms. Just like substance

abuse, the entire work and social life of the addict are ruined. The person becomes

aloof and withdraws from society. This leads to depression and stress. But [1] found

that using social media was not related to both depression and stress. Anxiety is

another phenomenon associated with social media addiction. Social media addicts

check their mobile phones as soon as they get up. This is the same as nicotine

addicts who smoke cigarettes when they get up. The individual develops anxiety.

But contradictory findings by [2] suggest that social media can be used to manage

anxiety positively during the Pandemic situation.

Depression is also linked with social media addiction. It is the persistent sadness and

low mood. People compare their own social life with others and they start feeling

depressed. The social media addicts feel lonely and believe that they are missing out

on social activities. People compulsively browse the profiles of others. In opposition

to this idea, [3] found that social media use was not related to depression for each

person. Another phenomenon, the fear of missing out (FOMO) is seen in social

media addicts. The addict fears losing their friends when they look at the pictures of

their online friends enjoying themselves without them. This also leads to anxiety and

insecurities. Opposed to this fact, [4] conclude that social media abuse led to a

decline in depression. According to the recent literature, [5], [6], and [7] also suggests

that depression was strongly associated with social media.

Recent evidence shows that social media causes a lot of stress. When the social

media addict faces online rejection, particularly through dating websites, the person

faces psychological stress which is a gateway for further mental health problems like

anxiety and depression. Recent evidence suggests that Twitter also leads to sleep

disturbances and stress. The same is the case with other platforms like Facebook and

Instagram. Social media addiction is also the same as other addictions. Stress,

depression, and anxiety are in a reciprocal relationship with social media addiction.

They aggravate each other. Contrary to this aspect, [8] did not find any significant

associations between social media and stress.

78

Earlier, there was a time when newspapers and magazines were the only sources of

information and entertainment. The mental health of the people was better in those

times. There were more social gatherings, people read books and engaged in outdoor

activities. Then came the television. People became more inclined towards a

sedentary lifestyle. There was less outdoor activity and thus more strain on mental

health. Now, the present is dominated by the internet. It is an obstacle to the

psychological growth of the people. Adult health, as well as adolescent psychological

health, suffers. Excess of anything can become worse. Mental health problems are

increasing for which one of the causes is the world wide web. The reward system of

the brain is highjacked which leads to the never-ending cycle of internet abuse.

Methodology

The methodological details of the research entitled “Influence of Social

Media Addiction on Anxiety, Depression, and Stress” are elaborated.

Objectives

The primary objective is to study the covariation among the variables anxiety,

depression, and stress concerning social media addiction.

• To study the impact of social media addiction on anxiety.

• To study the role of social media addiction on depression.

• To study the impact of social media addiction on stress.

Hypotheses

• H1 There is a significant relationship between social media addiction and

anxiety.


stress.


depression.

Research Design

Data collection was done using the method of snowball sampling. The voluntary

consent of the participants was obtained. The sample included 140 individuals

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falling in the age group of 18 to 40 years. The instructions were provided in each

online questionnaire.

Sample of the Study

140 individuals with an age range of 18 to 40 years participated in the study. There

were 91 females and 41 males. People from varied educational backgrounds,

professionals, and self-employed took part.

Tools Used

The self-report psychological questionnaires administered are discussed below:

• Depression, Anxiety and Stress Scale (DASS 21)

• Bergan Social Media Addiction Scale

Socio-demographic details were collected which included questions on daily time

spent on the internet and social media, time of checking phone first time in the

morning, and type of social media platform used.

Depression, Anxiety and Stress Scale (DASS 21) developed by S.H Lovibond and P.

F Lovibond has 21 items and three scales for anxiety, depression, and stress. The

Cronbach's Alpha Reliability for each scale was computed. They were .806, .845,

and .869 for the stress, anxiety, and depression scale respectively.

Bergan Social Media Addiction Scale is a six-item questionnaire by Cecilie Schou

Andreassen. It is used to assess whether the individuals are social media addicts. The

Cronbach's Alpha Reliability was computed and found to be 0.642.

Procedure

A google form that included DASS 21 and Bergan Social Media Addiction Scale

was created and sent through WhatsApp and Facebook messenger. The voluntary

consent of the participants was obtained. The instructions were provided in each

online questionnaire. There was a positive response from the participants who were

from various age groups and educational backgrounds. The data collection method

used was snowball sampling and 166 responses were collected. According to the

inclusion and exclusion criteria, 140 people falling in the age group of 18 to 40 years

80

were selected. A few responses of those who were below 18 years and above 40

years of age were discarded. There was an unequal number of male and female

responses, so the final sample consisted of 49 males and 91 females. There were

both professionals and self-employed. Once all the responses were received, a

google spreadsheet was created and scoring was done as per the given procedures.

The raw scores were then put to statistical analysis using IBM SPSS 21 software.

Pearson correlation, descriptive statistics, t-test, and regression analyses were applied.

Statistical Analysis

The raw scores were analyzed with the help of IBM SPSS 21. Pearson correlation,

descriptive statistics, t-test, and regression analyses were applied.

Results and Discussion

The purpose of this research work was to study the combined impact of stress,

anxiety, and depression on social media addiction along with its impact on these

variables.

The graph given below compares the number of males with females in the total

sample. There are 49 males as compared with 91 females in a sample of 140

participants.

Figure 1: Total Number of males and females

The pie chart below shows that in the total sample of 140 individuals, the maximum

uses Instagram (41%) while Facebook is used by 24% of the participants. Twitter

49

91

140

0

20

40

60

80

100

120

140

160

Males Females Total

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and Snapchat have almost the same number of users that is 16% and 15% respectively.

But dating websites have the least 4%, users.

Figure 2: Type of social media used

The graph given below compares the number of hours spent daily on social media

with the hours spent on the internet. Most of the sample fits in the category of

spending less than an hour to 6 hours on social media. But in comparison with

internet time, the majority spend around 6 hours which can stretch to above 9 hours

in a day. This can be due to the internet-based work individuals do.

Figure 3: Hours spent on the internet and social media

Addiction is when the person does that activity right after getting up in the morning.

For example, the case of nicotine addicts who smoke in the morning. This happens

122, 41%

73, 24%

47, 16%

45, 15%

12, 4%

Social Media Type

Intagram

Facebook

snapchat

twitter

dating websites/ apps

32

57

29

6 4 0

21

55

101

126

0

20

40

60

80

100

120

140

less than 1 1 to 3 3 to 6 6 to 9 above 9

Hours Spent

social media internet

82

with social media addiction also. Information on the morning time for checking

phone was collected and the following graph shows that many participants check

their phone between 5 am to 10 am. Participants check their phones after 10 am also

but the data was collected during the Covid 19 pandemic and the individuals are

working or studying from home, so they do not get up early on their usual time.

Figure 4: Time for Checking Phone

Statistical Analysis of the data using the SPSS software shows many interesting

findings. Tables 1 and 2 show the Descriptive Statistics for gender differences and

variables age, stress, anxiety, depression, and social media. Table 3 depicts the

results for the Pearson correlation among the given variables.

Table 1: Descriptive Statistics for Variables Under Study

Minimum Maximum Mean Std. Deviation

Statistic Statistic Statistic Std. Error Statistic

Age 18 40 23.03 .4 4.74 Social Media Addiction 6 25 14.25 .34 4.06

Stress 0 21 6.66 .38 4.52 Anxiety 0 21 5.16 .4 4.83

Depression 0 21 5.90 .44 5.18

48

60

34

0

10

20

30

40

50

60

70

B/w 5-8am B/w 8-10am After 10am

Time for Checking Phone

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Table 2: Gender-Related Statistics

Frequency Percent Valid Percent Cumulative Percent

Valid

male 49 35.0 35.0 35.0

Female 91 65.0 65.0 100.0

Total 140 100.0 100.0

Table 3: Correlation between

Variables Age Social media addiction Stress Anxiety

Social media addiction 0.5

Stress -0.15 0.29**

Anxiety -0.14 0.24** 0.75**

Depression -.029 0.26** 0.78** 0.75** *P<.05, **p<.01

Table 3 shows the Pearson Correlation findings for the variables age, anxiety, stress,

depression, and social media addiction. While evaluating the findings, it was found

that except age, all the other variables, namely, anxiety, stress, depression, and social

media addiction are positively correlated with each other. This shows that there is a

significant positive relationship between each variable. The correlation among them

was found to be significant at the 0.01 level.

A significant positive relationship was found between social media addiction and

stress (r = 0.29, p < 0.01). This shows that as the time spent on social media

increases, stress increases. Since the variables are positively correlated, it can also be

considered that as the stress increases, social media addiction also increases. The

reason is that when one is under stress, he turns to addictive things like social media.

When under stress, people try to express themselves with social media. This

increases the frequency of social media and ultimately leads to addiction. The

negative activities on social media, like rejection and bullying, all cause stress. The

work that that is left behind due to this addiction also causes stress. Social media

addiction leads to dissatisfaction, obsession with self, and communication problems

which is a cause of stress. This finding supports the second hypothesis.

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anxiety (r = 0.24, p < 0.01). The reason for the variables to be positively correlated

is that excessive exposure to social media leads to anxiety. Otherwise, also, any

addiction causes anxiety. Sometimes people fear that they are missing out on friends.

This phenomenon is also called fear of missing out (FOMO). Also, the person feels

inferior, becomes aloof, and isolates himself. There is a vulnerability to cybercrime.

The body image issues which arise after looking at pictures of others lead to anxiety.

People browse through the profile of friends and the smiling pictures of travel and

social gathering cause a feeling of lacking. So, social media is a source of anxiety.

This result proves the first hypothesis.


depression (r = 0.26, p < 0.01). This is because social media addiction aggravates

depression and depression makes an individual more prone to addictions. Social

media usually causes unhappiness. While looking at the happy photos of others, the

individuals feel sad and believe that their life lacks the same cheer which others

have. Nobody knows what is behind the curtain. This leads to sadness, feeling empty

and people withdraw themselves from friends and family. Also, the anxiety that is

again, increased by social media addiction, combines with depression and has a

double impact. The entire routine, interpersonal relationships, and work-life get

disrupted which is also a diagnosis for depression. All this shows that depression and

social media addiction are positively correlated which is proof for the third hypothesis.

Tables 4.1, 4.2, and 4.3 describe the result of linear regression analyses. The primary

objective was to study the combined relationship of anxiety, depression, and stress

with social media addiction. The result shows that social media addiction is a

predictor for all three dependant variables, namely anxiety, depression, and stress.

Social media addiction is the independent variable and any variation in it causes

significant changes in the other dependent variables.

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Table 4: Regression Analysis Summary for S0T Predicting AT

Variable B β t p

Constant 1.165 0.8 0.425

Social media addiction 0.281 0.236 2.853 .005 Adjusted R2 = 0.49, F (2,229) = 8.139, p = .005**

In table 4.1, social media addiction is the independent variable and anxiety is the

dependant variable. Here, p = 0.005 (significant at 0.01 level.) This shows that social

media can predict anxiety. The adjusted R2 shows that 49% variance in anxiety is

explained by social media addiction. The results support the first hypothesis.

Table 5: Regression Analysis Summary for S0T Predicting ST

Variable B β t P

Constant 2.02 1.506 0.134

Social media addiction 0.325 0.236 3.595 0.000 Adjusted R2 = 0.79, F (2,229) = 12.922, p = .000**

Table 4.2 represents, again, social media addiction as the predictor variable and

stress as the dependent variable. Since p = 0.001, the model is significant. It shows

that social media addiction predicts stress levels. Adjusted R2 = 0.79 which proves

that 79% variance in stress levels is explained by social media addiction. The results

prove the second hypothesis.

Table 6: Regression Analysis Summary for S0T Predicting DT

Variable B β t p

Constant 1.147 0.739 0.461

S0T 0.334 0.262 3.184 0.02 Adjusted R2 = 0.062, F (2,229) = 10.135, p = 0.02*

Table 4.3 describes that depression is the dependant variable that can be predicted by

social media addiction. According to the results, p = 0.02 which shows that it is a

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statistically significant model. So, social media addiction can predict depression.

Since the adjusted R2 is 0.062, it means that a 6.2% variance in depression can be

explained by social media addiction. The third hypothesis is proved. Given below is

Table 5 which describes the independent t-test results for gender differences among

the variables anxiety, depression, stress, and social media addiction.

Table 7: t-test comparison between genders

Variable Gender N M SD SEM t-test p

Social Media Addiction Male 49 14.12 3.74 0.534 -0.27

-0.28

0.79

Female 91 14.32 4.242 0.445 0.78

Stress Male 49 6.43 4.993 0.705 -0.44

-0.42

0.66

Female 91 6.78 4.299 0.451 0.68

Anxiety Male 49 4.33 4.249 0.607 -1.51 0.13

Female 91 5.62 5.079 0.532 -1.596 0.11

Depression Male 49 5.33 5.524 0.789 -0.96 0.33

Female 91 6.21 4.986 0.523 -0.93 0.35 An independent t-test was applied to compare the differences in anxiety, depression,

stress levels, and social media addiction among males (N= 49) and females (N= 91).

The Levene’s test for social media addiction (F = 0.600, p = 0.44) is insignificant.

Also, the result for t-test is insignificant (t = -0.27, p = 0.79). This shows no gender

differences exist for social media addiction. Similarly, the Levene's test for stress

levels (F = 1.33, p= .25) is not significant. The t-test result is also insignificant (t = -

.44, p= .66) which shows no gender differences exist among stress levels. Levene’s

test numerical value for gender differences in anxiety is F= 1.89, p= .17 (statistically

insignificant). Also, t = -1.51, p=.13 is not significant which proves no gender

differences exist in anxiety levels. In the case of the variable depression also,

Levene’s F = .52, p= .47 which is not significant and t = -.92, p =.34 which is again

not significant. So, there are no differences among males and females in depression

levels. Therefore, the independent t-test results prove that no gender differences exist

in the study entitled ‘Anxiety, Depression, and Stress in Social Media Addiction.’

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The statistical analysis results are supported by [9] who found that anxiety, stress,

and depression were in a significant positive relationship with social media,

particularly Facebook.

Conclusion

Looking at the results, it has been found that an association exists between anxiety,

depression, and stress concerning social media addiction. The obtained graphs reveal

that on average, people spend around 1 hour stretching to 6 hours and even more on

social media platforms with a majority using Instagram. It had been found that many

participants check their phones right after getting up in the morning. Pearson

Correlation results prove that a significant relationship exists between anxiety,

depression, stress, and social media addiction. Regression Analysis findings also

reveal that social media addiction can predict anxiety, depression, and stress. But t-

test results show that no gender differences exist among any of the variables.

Therefore, it can be concluded that there is a combined relationship of anxiety,

stress, and depression with social media addiction.

Limitations

• The number of females was more than the number of males in the sample.

• Individuals below the age of 18 and above the age of 40 were not included.

• The study was not tested concerning specific social media platforms

• WhatsApp was considered a messenger app and not included as a social

media platform.


1. Aalbers, George, et al. "Social media and depression symptoms: A network

perspective." Journal of Experimental Psychology: General 148.8 (2019):

1454.

2. Cauberghe, Verolien, et al. "How adolescents use social media to cope with

feelings of loneliness and anxiety during COVID-19 lockdown."

Cyberpsychology, Behavior, and Social Networking 24.4 (2021): 250-257.

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3. Aalbers, George, et al. "Social media and depression symptoms: A network

perspective." Journal of Experimental Psychology: General 148.8 (2019):

1454.

4. Rodriguez, Micaela, George Aalbers, and Richard J. McNally. "Idiographic

network models of social media use and depression symptoms." Cognitive

Therapy and Research (2021): 1-9.

5. Haand, Rahmatullah, and Zhao Shuwang. "The relationship between social

media addiction and depression: a quantitative study among university

students in Khost, Afghanistan." International Journal of Adolescence and

Youth 25.1 (2020): 780-786. DOI:10.1080/02673843.2020.1741407

6. Culpepper, Morgan. Exploring the Relationships of Social Media Usage and

Symptoms of Anxiety and Depression in Adolescents. Diss. Abilene Christian

University, 2020.

7. Yoon, Sunkyung, et al. "Is social network site usage related to depression? A

meta-analysis of Facebook–depression relations." Journal of affective

disorders 248 (2019): 65-72. https://doi.org/10.1016/j.jad.2019.01.026

8. Ngien, Annabel, and Shaohai Jiang. "The Effect of Social Media on Stress

among Young Adults during COVID-19 Pandemic: Taking into Account

Fatalism and Social Media Exhaustion." Health Communication (2021): 1-8.

9. Hughes, Sean. "Effects of social media on depression anxiety and stress."

(2018).

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CHAPTER 7

ANALYSIS OF PREDICTIVE PATROLLING USING

EXPLORATORY ANALYTICS

Anurag Bhatnagar

Pranshu Gupta

Shreay Mittal

Venkatesh Gauri Shankar*


Bali Devi

Nikhar Bhatnagar

Kuntal Gaur

School of School of Computing and IT


Jaipur, Rajasthan, India

******************************************************************** Abstract: In recent years, the police force has strengthened its traditional crime

reporting methods. New technological advances that increase production through

crime recording aid your investigations. The data is not the only record of a crime.

They also contain valuable information. The crime scene can be linked according to

the offender's modus operandi (MO), suggest which offenders may be responsible

for the crime, and identify those who work on the team. Perpetrator (criminal network).

We cannot predict crime because it is neither systematic nor random. Furthermore,

modern technology and high-tech methods can also help criminals to commit their

crimes. According to the Criminal Records Office, serious crimes such as robbery

and arson are on the decline. Although criminal activities such as murder, sexual

abuse, and gang rape are on the rise. Even if we cannot predict who may be the

victim of a crime. We can infer the possibility of it happening in this place. The

prediction of the results cannot be 100% accurate.

Keywords: Machine leering, data analysis, python

********************************************************************

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Introduction

Crime is dangerous and faces common social problems around the world. They

affect the quality of life, economic growth, and national reputation. In recent years,

the crime rate has increased significantly.

We cannot predict crime because it is neither systematic nor random. Furthermore,

modern technology and high-tech methods can also help criminals to commit their

crimes. According to the Criminal Records Office, serious crimes such as robbery

and arson are on the decline. Although criminal activities such as murder, sexual

abuse, and gang rape are on the rise. Even if we cannot predict who may be the

victim of a crime. We can infer the possibility of it happening in this place. The

prediction of the results cannot be 100% accurate.

In recent years, the police force has strengthened its traditional crime reporting

methods. New technological advances that increase production through crime

recording aid your investigations. The data is not the only record of a crime. They

also contain valuable information. The crime scene can be linked according to the

offender's modus operandi (MO), suggest which offenders may be responsible for

the crime, and identify those who work on the team. Perpetrator (criminal network).

It's not easy for law enforcement analysts to understand the inherent complexity of

law enforcement data, and the problem is even more complicated. When a team

analyzes, because each member does not have all the relevant facts, the relevant

information can be lost. By generating millions of records, human reasoning will

fail. Therefore, a set of tools is needed to help analyze the data to make the best use

of limited resources.

Predictive police analysis and crime analysis focused on time and space have

increasingly attracted the attention of different scientific circles and have been used

as effective police tools. The purpose of this article is to provide an overview and

assessment of the latest technologies in space crime prediction in terms of investigation

design and technology.

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Data Used

A. Data set and source

Boston government data is used in the project because it comes from a site that is

run by the city government (data.boston.gov). It has a total of 16 things. These are

the things that are important for the study: the type of crime that happened, where it

happened, when it happened, and how long it took. From 2015 to 20 years, the

history of crimes will be used as the training dataset in this phase. It's the first step.

Data that can't be used to predict crimes is removed in the pre-processing phase.

This includes data that doesn't have the correct values or is redundant. This will be

used in the next modules.

METHODOLOGY

To make predictions, a method called predictive modelling is used. This method can

build models and predict what will happen. When you use this method, you use

different algorithms for machine learning to figure out how things work. The

algorithms learn from the data that was used to train them (used to generate

predictions). There are two parts: regression and how the model is classed, which are

broken down into two separate parts: These models are based on studying how

trends and variables work together to predict constant variables. The task of

categorization is to label the data values that were thought up based on the data, so

they can be found. There are two ways to learn how to classify patterns, supervised

learning, and unsupervised learning, both of which work. It is already known which

class labels will be used to build the classification model in supervised learning, so

there is no need to figure that out. They are not known in unsupervised learning, so

this is how these class names are. Here, we talk about supervised learning.

Figure 1: Methodology of Work

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B. Data collection and Preprocessing

When you get information from a lot of different places, you can use it to make

machine learning models. https://data.boston.gov/dataset/crime-incident-reports-

august-2015-to-date-source-new-system. In this case, the data storage method

should be able to deal with what is going on. Remove infinite or blank values from

the data that could make your model not work as well as it should. This is called

"data preprocessing." Step 1: In this stage, the data set is changed to make it easier

for the machine learning model to work with it.

Figure 2: Bar Chart of statistical intervention

Figure 3: Area Plot of statistical intervention

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Figure 4: Correlation graph of statistical intervention

C. Model Assortment

● Support Vector Machine:

Using support vector technology is very good for regression, forecasting time series,

and classifying challenges, among other things. Compared with recurrent neural

networks, the performance of support vector machines can be measured. Therefore,

support vector machines have been used to predict crime hotspots, and to predict

diseases such as diabetes and prediabetes. Because we can prototype non-linear

relationships consistently. It works well for the time series that you want. Crime data

set K grouping should be used to choose a subset of the data set. It will use this

algorithm to figure out what to call each data point in the selected set. When the

crime rate is lower than a certain rate, it's called the "hot spot." When the crime rate

is higher than a certain rate, it's called the "hot spot."

● K-nearest Neighbour:

The test set and the train set are linked by this tool. A class label will be used if a

given test set is very similar to the training set. If that is the case, the class label will

be used. A lot of people have trouble when the training set has a lot less information.

To make it better, different techniques have been used, like the K-NN algorithm.

This method is in the field of supervised learning. We can use data mining, intrusion

94

detection, and pattern recognition in these fields, too. In this case, the result is that

the person joins a class. Categorize a thing based on the votes of your next-door

neighbours. There, the object is placed with its closest or most familiar neighbour.

This is what "the object" means in English

Figure 5: SVM notation

● Decision trees:

It is one of the most popular ways to classify and predict things. Decision trees are

one of them. Its structure is like a tree, with each branch representing a characteristic

test, and each leaf node representing the category label. There is a test for each

intermediate node, and each branch shows the final product of the test and each leaf

node has the category label. This is how it works: Most of the time, the target

variable is a group of things. Decision trees can be used to figure out how likely it is

that a given record fits into each category. They can also be used to classify records

(assigning records to the most similar classes).

Figure 6: Decision Tree

● Extra Tree classifier:

This is how to combine the results of many different decision trees that were found

in the forest. The random forest classifier looks a lot like this one, but it isn't the

95

same thing at all. Only this is different: the way the decision tree looks in a forest,

which is the only thing. In the new forest, each decision tree is made from the same

training set that was used in the first forest, so it looks the same. This is how it

works: It picks a random feature set from the common feature set and adds it to its

own decision tree at each test node of its own decision tree. Decision trees must

choose the best characteristic from this set of randomly chosen traits, based on some

math rules. People come up with a lot of different decision trees because of this.

● Artificial Neural Network:

Artificial neural networks are made with the help of biological neurons. They mimic

the process of deciding in the human brain. It has a lot of parts that work together to

deal with and solve the problem. A lot of old data is used to figure out what will

happen when we look at it. It can do more common and flexible things than traditional

statistical methods can. It can also be used better than other statistical methods. Each

time we changed the weights, we looked at these to figure out how the input and

output were linked when we did it again. Ann can do things and look for patterns to

learn more about the world around her. A connection is shown between the input

neuron and the output neuron. This is how it works: Neurons come in different

weights. It is made by multiplying the input by multiplying the entrance and then

looking at how close it is to a certain amount. In this case, the output comes out this

way. In this case, it is called a result or a result.

TRAINING AND TESTING

In this step, after verifying the hypothesis of the algorithm, we choose. Train the

model based on the training samples provided. After training, model performance

will be verified based on error and precision. Finally, use some invisible data to test

the trained model and verify the performance of the model according to various

performance parameters based on the problem.

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Figure 7: Dataset Before Preprocessing

Figure 8: Dataset After Preprocessing

There are two ways to use the sklearn library when you're training. You can divide

the data into two groups: one for training and one for testing. Our training size was

about 78,000 points and our test size was about 19600. This is how it worked out:

We trained with a size of about 784,000 points and then we tested with about 19600

points. Here, five models, like the one in method [1], are used for training. Each

model was tested with a different set of parameters. When that was done, different

models were trained, and their f-beta scores (accuracy) were found, then. For KNN,

you need to set parameters njobs and weight. Their values are set to -1 and even. It's

also the same thing with the decision tree. The weight of each category is a

parameter for that one. In this case, this parameter's value is set to "balance," and the

same is true for all other models. There should be 100 hidden layers and the adam

solver set up for MLP to work best. The figure shows that SVC takes a long time to

learn, so it can't be used. As long as we want to look for MLP, it doesn't have a good

score for accuracy and f-beta accuracy. So, MLP isn't the best choice.

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D. Model evaluation and Metrics

Evaluation: To see how well the classification models that were used for classifying

and predicting things worked. Accuracy and beta-score are two of the metrics that

are looked at. Preciseness is a way to measure how well you can find positive cases

out of all the other possible cases.

q = tv (tv + fv)

Next, we'll talk about recall, which is a measure that correctly picks out positive

cases from all the real positive cases.

rc = tv (tv + fnv)

Accuracy is one of the most common metrics, and it only looks at the values that are

right. It doesn't care about the values that are wrong. F-beta score is used instead of

accuracy to measure how well things are going.

accuracy = tv + tnv (tv + fv + tnv + fnv)

F beta-Score is the harmonic sum of Recall and precision. It gives a better measure

of incorrectly classified cases than Accuracy Metric. This is because F beta-Score is

the harmonic sum of Recall and precision.

F beta score = (2 ∗ (rc ∗ q)) (rc + q)

Tv stands for true positive; fv stands for false positive; fnv stands for false negative;

tnv stands for true negative; rc stands for recall; and q stands for precision, which

means that each of these words refers to a different thing.

E. Identify the Headings

As per various review papers. KNN most frequently provides the best accuracy.

Hence for our study, we will not omit KNN. Metrics for KNN relating to our model

looks like the following.

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It is a way to group things. It's a class member that comes out of the K-NN classification method. Most of the people who live near an object vote for it. The object is thought to be the most common type of thing in its k [1] next-door neighbour. The algorithm can be used to look at data about crimes. Assuming a home burglary happens, the house next door is also easy to break into because criminals think security is low and the theft can be done again in the same place. Because of this, crime is more likely to happen in areas near where the last crime happened. So, location is important to think about. If you think about the date, you can also think about how important it is Because the distance factor is used to classify things, it's important to figure out how far away the test area is from the training area. Because this is the case, use the latitude and longitude as coordinates and figure out the distance factor as a coordinates and distance factor.

If the date also considers the factor, you must calculate the number of days (), and

then calculate the distance factor as

K-NN is used to solve the problem. Euclidean distance implied by the square and square root is calculated every time it is done. Each training set's calculation distance can be done in parallel with OpenMP, which is a type of parallel processing technology. Because the square and the square root are not used, the distance from Manhattan is found out. This can also be done at the same time. After you figure out the distance, use an effective classification method to figure out which neighbours are the closest. Then, assign the crime attribute type with the highest voting rate to the k [1] neighbours.

Figure 9: Shows the data that is to be tested i.e. finding what crime can

happen at a given location

99

The third phase is called a "pattern identification" phase, and it should help you

figure out what kinds of crimes there are. We use the apriori algorithm to figure out

where there are a lot of crimes. You can use Apriori to figure out which rules in the

database are most important for highlighting general trends. When this stage is done,

there is a crime pattern for a certain place. Here, think about the characteristics of

each place, such as whether or not there is a VIP there, how sensitive the area is,

how much attention it gets, and whether or not there is a criminal group there. If a

new case comes up, and if it continues with the same crime pattern, it can be said

that there is a chance that crime will happen after getting a general crime pattern for

its place. Police officers can better use resources if they know more about the

employer. In areas where crime is likely to happen, they can provide safety or

patrols, set the robbery or CCTV alarm, and use the apriori algorithm. It will cause

crime to happen more often in certain places. People who do the same thing over

and over again may do the same thing in the same place where there are other people

who do the same thing over and over again. We are looking at some factors that

could help us find criminal patterns. E.G: When you put the pattern after the

microphone, it looks like this. Before this, there was a pattern of attributes 1, 2, 3, 4,

and 5. Attributes 1, 3, 4, and 5 are the same as attributes 1, 2, 3, 4, and 5. Crimes

only happen if the same thing happens again in the same day. If any of these things

happen, it can be said that there was a crime.

Conclusion

With this project, we aim to identify some patterns in crime data collected from

Boston. The prediction may not be 100% accurate as stated earlier. Our project

provides meager assistance to authorities by visualizing their thought process when

they decide what is to be patrolled next.

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Senjyu T., Mahalle P.N., Perumal T., Joshi A. (eds) Information and

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CHAPTER 8

SENTIMENT ANALYSIS OF TWEETS TO DETERMINE

POLITICAL OUTCOMES USING MACHINE LEARNING

TECHNIQUES

Anurag Bhatnagar*


R. Raj

S. Kalikar

Venkatesh Gauri Shankar

Kuntal Gaur

Nikhar Bhatnagar

Bali Devi*


******************************************************************** Abstract: The aim of this paper is to discuss and elaborate the machine learning

techniques that have been used and are currently being implemented to extract the

sentiment of tweets that have been posted by a non-specific demographic. We have

also chosen to provide direction to this by pertaining to a specific goal, which is to

predict the outcome of the amount of support or critique a political entity gets

through the analysis of these tweets, hence the tweets pertain to only a political

influence. By the use of machine learning techniques such as polarization and

sentiment score extraction, this task is achievable, as discussed further. Twitter has

been chosen as the suitable social medium for this project as it is the most politically

charged, open platform that allows the masses to voice their opinions without any

third party influenced regulations.

Keywords: Machine leering, data analysis, python

********************************************************************

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Introduction:

Twitter is a microblogging and social media service where users, commonly knows as “tweeters”, can upload or post descriptive messages and interact with other posts and users. It has features such as like, follow, and retweet, which allows other users to interact with each other globally. Users are able to curate what they want to see on their feed, as well as voice opinions on subjects of all kinds with a higher degree of freedom, as compared to other social media platforms. It is also the platform that has popularized the use of hashtags, which are words followed by the “#” symbol, which acts as an encapsulating word for the entirety of the tweet. These hashtags are of massive importance on this platform as they can be subjects of millions of peoples’ discourse habits, and can highlight a lot of important issue, making everything more accessible than ever. Coming to a political discourse standpoint, Twitter can both stand for emptions, or can cause more of a backlashing response, and this makes out to be one of the most integral part of twitter. It can control the rise and de-escalation of any outbreaks or disagreements, irrespective of what the subject of discussion is. Being an anonymous space, there is no fear of the consequences of censorship, which in itself is a rare event on the platform. All political entities, be it parties, leader, or members, use Twitter as a platform to interact, publicize, promote, and criticize the options available in the existing political space. Due to the diverse variety of beliefs, opinions, and people in themselves, it is the ideal platform to gather information on what different demographics of masses want, like, and dislike about political entities that are soon to govern states and countries, while also providing wholesome criticism to push the boundaries of an entity’s working. Literature Review

Twitter Sentiment Analysis Using Machine Learning Techniques.

In [1] the authors, Bac Le and Huy Nguyen have very elegantly explained about the presence of features in tweets and briefly explained the steps to follow in the procedure of feature extraction. They mentioned that before feature extraction, preprocessing is important to carry out in order to remove the usage of emoticons, informal slang and typos. They have stated that there are various feature reduction methods which can be applied to the tweets involving a two-phase process which requires feature extraction and removal to create regular text, and then further feature reduction to filter more features.

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They have defined two types of features: ‘Representative Feature’ which is the information representative of a class and, ‘Unique Feature’ which is the information that helps in creating distinction amongst classes. A feature vector stores the number of occurrences of positive and negative word instances in a tweet. The 8 different features used are: tags for speech, special keywords, presence of negation, emoticons, number of positive keywords, number of negative keywords, number of positive hashtags and number of negative hashtags. Sentiment Analysis in Twitter using Machine Learning Techniques

In [2] the authors, Neethu M S and Rajasree R have talked about the shortcomings faced during the process analyzing sentiments in tweets. They have asked to address two main issues in any type of machine-based tweet analyzer: first, the number of misspellings and slangs is much more as compared to other domains and second, Twitter users post messages and comments on an array of topics unlike blogs, news and other sites which generally refer to a particular agenda. The authors have warned about the following challenges that people face in

sentiment analysis:

a) There are more neutral tweets than positive or negative ones.

b) A tweet is limited to 14 characters, so it is very unique as a critique.

Sentiment Analysis of Political Tweets: Towards an Accurate Classifier.

In [3] the authors, Akshat Bakliwal et al have focused on describing the procedure of sentiment analysis by taking up the case study of the Irish Genral Election of February 2011. They have explained the process of sentiment analysis in a more complex setup which involves the sentiment being classified as a topic specific quantification, and not a general document, the documents being the tweets and the topic being politics. The authors have carried out a 3-step-class sentiment classification experiments on a

dataset of 2,624 tweets originated during the preliminary run-up to the Irish General

Elections in February 2011. In this, they found out that there has been study on the

usage of emoticons being used positively or negatively and hashtags being used to

express sentiment in tweets as a proxy for sentiment analysis.

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Following this, the authors performed annotation using the 6 labels as follows: pos: Tweets which possess positive perspectives towards the topic, neg: Tweets which possess negative perspectives towards the topic, mix: Tweets which carry both positive and negative perspectives towards the topic taking both factors, neu: Tweets which have an indifferent perspective towards the topic and nen: Tweets which are written in non-English languages. These are the counts of supportive, critical and indifferent words going by each of the three lexicons and the appropriate sentiment scores for the overall dataset of tweets. This data was then used to monitor the sentiment and predict the election results. Deep Learning for Hate Speech Detection in Tweets.

In [4] the authors Pinkesh Badjatiya et al have attempted to explain first the baseline

methods and then some different approaches.

Baseline Methods: As baselines, there are three extensive representations. (1) Char n-grams: It is the most efficient and precise method using character n-grams for hate speech detection. (2) TF-IDF: TF-IDF are contemporary features used for classifying text accordingly. (3) BoWV: Bag of Words Vector approach uses the net mean of the word embeddings to formulate the profile of a sentence. Proposed Approaches: A set of three neural network architectures are examined for the task, described as follows. For each method, the word embeddings are initialized with one of the two: random embeddings, or GloVe embeddings. (1) CNN: Inspired by Kim et. al’s work on using CNNs for classification and analysis of sentiment, CNNs are leveraged for hate detection. (2) LSTM: Unlike the much implemented feed-forward neural networks, recurrent neural networks like LSTMs have the ability to use their internal memory and carry out the processing of arbitrary sequences of inputs. LSTMs are used to capture long range dependencies in tweets, which may play a role in hate speech detection. (3) FastText: It represents a narrowed down result based system by average of word vectors, quite alike the BoWV model, but allows the updating of word vectors using back-propagation while in training as compared to the static word representation in the BoWV model, allowing the model to intricately adjust the word representations according to the task.

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Twitter Sentiment Analysis: A Political View.

In [5], the authors J. Pinto, T. Vijaya Murari, and S. Kelur have very elaborately put

forward that their version of sentiment classification is divided into 5 different

classes, but only the final end classifier is used to carry out the implementation. The

relevant tweets are segregated on the basis of the expressed attributed sentiment they

have. Semantic analysis is done and driven from the WordNet sentiment dictionary

subsequently after categorization and training.

They have considered two methods for dataset sentiment extraction:

a) Symbolic approach

b) Machine Learning Strategy

The authors, while testing and scraping data from the relevant dataset, used the “bag

of words” method using the Vader sentiment analyzer as medium to score the picked

tweets. Based on these scores, the tweets are considered “trained” and a new dataset

is created. There was a necessity of the conversion of upper-case letters to lower

case for the most efficient sentiment analysis result. Supervised Machine Learning

techniques were employed as they do well in sentiment categorization.

For enhancement, they plan to perform the same on Bigdata frameworks to cut down

on time for twitter data classification.

Twitter Sentiment Analysis using a Knowledge based Approach through

Machine Learning

In [6], the authors R. Suchdev, P. Kotkar, and R. Ravindran have elegantly written

about how their main concern was classifying the opinions or emotions expressed

withing generalized text. They have explained about how a sentiment summarization

system uses the documents to be analyzed as inputs, then generates a detailed

document that summarizes the opinions stated in the documents earlier.

To determine overall sentiment, the sentiment of each word is first determined

individually, assigned a value or a score, and is combined with the use of aggregation

functions. Different classifier techniques and feature vectors are used for word-by-

word sentiment analysis using the knowledge based approach.

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Personalization of Political Discourse on Social Media.

Here in [7], the author Cristina Moise suggests that online social ecosystem contains

a suite of services and applications that provide a way to summarize and outline data

found online, offline or through social media platforms, Twitter point in case. It

assists to make a monitored environment for reviewed audiences, assessing voter

behaviour during the entire time of the campaign.

The authors observed that there is a direct relationship between the following a

political entity has on social media and the following political outcome, for example,

the results of an election.

They specify that it is crucial that we take into account the informal tone of the

message, as well as specialized language commonly used on internet forums. Research

also suggests that voting substantially increases when a political entity increases it’s

presence and reach on social media.

After the author analyzed general competitor reactions, after studying how the

following they have reacts and what are their evolutions over time towards the

motivation to vote, they go through with the next step which is data correlation an

discovery.

As a conclusion, the author also includes competition analysis to get an overall idea

of how the political entity in question is placed with respect to their competition.

Natural Language Processing for Sentiment Analysis: An Exploratory Analysis

on Tweets

In this paper [8], the authors W. Chong, B. Selvaretnam, and L. Soon have presented

their preliminary experiments on tweet sentiment analysis, especially with regards

too Natural Language Processing.

NLP categorizes expressions of specific subjects, and classifies the polarity of

sentiment by assigning a quantified score to lexicon. NLP classifies by identifying

text fragments and the subject, then carries out sentiment classification separately

instead of taking all of the lexicology and assuming a generalized subject. No fixed

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template would be appropriate for a text-based sorter as the inputs may vary vastly,

with different contexts.

Sentiment analysis of texts and tweets differ, as texts assume that there is a larger

length article that talks about the subject, whereas tweets are of a shorter length, so

share a very specific subject and sentiment throughout. These methods are then used

to extract user opinion and sentiment from tweets.

In-depth read of Political Sentiment Orientation on Twitter.

The authors M. Ansari, M. Aziz, and M. Siddiqui in [9] very articulately discuss

unsupervised techniques that bank on lexicon, an array of keywords which define

boundaries of positive and negative, and estimates the outcome on the basis of the

occurrence of subsequent keywords with respect each another, or by frequency

calculation of all the terms.

The authors define a clear correlation of these said keywords to given political

parties, case in hand being the Bhartiya Janata Party (BJP) and the Indian National

Congress (INC), but also taking into account Other Parties.

They claim that it is quite viable for parties to resort to Twitter during a campaign to

publicly promote candidates, put forward their party policies, take part in stand-offs

and debates, and also call out their competition.

Some approaches the authors delved into are:

1) ID: A different ID assigned to each tweet, preferably using a numerical ID

and to keep the users anonymous. The main idea is to get the number of

people belonging to any of the campaigns.

2) Text: Displays the original tweet which had been written by the original

poster.

3) Three columns, two of them being data on to the two major parties in the

country (i.e., BJP and Congress) and one more for any other contending

party. The support for all three columns is taken together, quantified,

converted to decimal as it serves as an easy classified depiction.

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Moving onto pre-processing,

1) Unconventional lexicon is filtered out, for example hashtags, emoticons, etc.

2) Copied tweets and retweets are filtered out to maintain individuality and

uniqueness.

3) Removal of standard stop words is done as they do not contribute.

4) At the end, after the compilation, all upper-case letters are converted to lower

case for uniformity.


1. Pinto, Joylin Priya, Vijaya Murari, T. Kelur, & Soumya. Twitter Sentiment

Analysis Using Machine Learning Techniques. International Journal of

Advanced Trends in Computer Science and Engineering, 9(1), 723-729.

2. Neethu M S & Rajasree R. Sentiment Analysis in Twitter using Machine

Learning Techniques. *SEM 2013 - 2nd Joint Conference on Lexical and

Computational Semantics, 2, 450-454.

3. A. Bakliwal, J. Foster et al. Sentiment Analysis of Political Tweets: Towards

an Accurate Classifier. Proceedings of the Workshop on Language Analysis

in Social Media, 2013, 49-58.

4. Badjatiya, Pinkesh Gupta, Shashank Gupta, Manish Varma & Vasudeva.

Deep Learning for Hate Speech Detection in Tweets Pinkesh, 2, 759-760.

5. J. Pinto, T. Vijaya Murari, S. Kelur. Twitter sentiment analysis: A political

view. International Journal of Advanced Trends in Computer Science and

Engineering, 2020, Vol. 9, 723-729.

6. R. Suchdev, P. Kotkar, R. Ravindran et al. Twitter Sentiment Analysis using

Machine Learning and Knowledge-based Approach. International Journal of

Computer Applications, 2014, Vol. 103, 36-40.

7. C. Moise. Personalization of Political Discoures On Social Media.

Proceedings of the LT4DHCSEE in conjunction with RANLP 2017, September,

39-43

8. W. Chong, B. Selvaretnam, L. Soon. Natural Language Processing for

Sentiment Analysis: An Exploratory Analysis on Tweets. Proceedings - 2014

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4th International Conference on Artificial Intelligence with Applications in

Engineering and Technology, ICAIET 2014, 2014, 212-217.

9. M. Ansari, M. Aziz, M. Siddiqui et al. Analysis of Political Sentiment

Orientations on Twitter. Procedia Computer Science, 2020, Vol. 167, 1821-

1828.

10. Duncombe, C. (2019). The Politics of Twitter: Emotions and the Power of

Social Media. International Political Sociology, 13(4), 409–429. doi:10.

1093/ips/olz013

111

CHAPTER 9

ANALYSIS OF CRIME PREDICTION ALGORITHMS

Anurag Bhatnagar

Aarushi Batta

Niharika Marwah

Venkatesh Gauri Shankar

Bali Devi*


Nikhar Bhatnagar

Kuntal Gaur

School of School of Computing and IT


Jaipur, Rajasthan, India

[email protected]

[email protected]

[email protected]

******************************************************************** Abstract: "The Minority Report" was written by Philip K. Dick in 1956, and it

talked about how crime could be predicted before it even happened. There is a well-

known 2002 movie with the same name that tells the storey. The storey is about

three mutants who are in a pool and can see weeks into the future. Analysing crime

and its prevention is a methodological approach for identifying patterns and trends

in crimes committed by small populations. We studied various types of predictive

models like linear regression, logistic regression, SVMs and decision trees which

were used by different researchers. The work aims at studying the research of other

scholars and producing an output.

Keywords: crime, regression, algorithms, machine learning, datasets, data mining,

********************************************************************

112

Introduction

"The Minority Report" was written by Philip K. Dick in 1956, and it talked about

how crime could be predicted before it even happened. There is a well-known 2002

movie with the same name that tells the storey. The storey is about three mutants

who are in a pool and can see weeks into the future. As well as reading stories, it is

possible to predict what crimes will happen in the city in the next few years. There

are computer algorithms that can be used to figure out how the market works. They

use information from many sources to figure out how the market works. Crimes

happen in the same places repeatedly, and algorithms often use this to make more

money. It's also possible to make some of these inferences based on the known

factors that lead to certain crimes, as well. When police make a prediction, they will

do more patrols in cities at the right times and places where and when crimes are

likely to happen. People who are ultimate felons might be less likely to do

something that isn't legal if there are police around. This would cut down on how

many crimes there.

Crime activities are on a gradual increase in India over the past few years and is

proving to have a devastating social impact.[1] With the advancement in technology,

some people can commit various types of crimes now. This is led to a sudden

increase in the cases of murder, robbery, cyber-crime, etc. As a result of these

activities, there is an urgent need for analysis of crime and to find a way to reduce

the crime rate. Crime analysis is done to find patterns or trends for the upcoming

years by taking into consideration their previous year's data. By using the latest

technical developments to the advantage, we came up with this project to predict the

crime distribution in various states of India using machine learning algorithms.

There is the idea that when we can look through a lot of data to look for patterns that

can help us figure out what we need, crimes can be a lot easier to predict. People

who write this paper will be using a lot of different regression models. These models

are called linear, logit, support vector, support vector regression, decision tree, and

more. Using regression analysis for this project is the key to form a proper

relationship between the dependent and the independent variables that are present in

our dataset. Eventually, the conclusions that will be drawn from this project will help

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the police to deploy their resources in the concerned areas accordingly. With the help

of this project, we intend to contribute to society and help it make a better and safer

place for us as well as for our future generations.

Classes of crime in India are:

Murder – Section 302 of IPC

Dowry Death- Section 304 B of IPC

Attempt to Murder- Section 307 of IPC

Voluntarily causing grievous hurt- Section 326 of IPC

Rape- Section 376 of IPC

Literature Review

If you build a machine learning model, it's likely that you'll use a different dataset

for each city. This means that the assumption is going to be different in each case.

There are a lot of different ways classification models can be used. They can be used

for things like predicting the weather, banking and finances, and security. [1]

Classification techniques were used to help police find criminals. KNN, DBSCAN

clustering, and K means were used to classify things into groups. They were used to

predict crime. Six cities in the Indian state of Tamil Nadu were used to run these

algorithms, and they worked well.

It was important to use a dataset where the data was broken down into different

groups in [4.] To see where new sample places were, this was done. For example, it

looked at things like the time of day and year that the crime was done to see how

well the KNN algorithm worked. It turned out to be 40% correct. They used logistical

regression, decision trees, the Bayesian theorem, and the Support Vector Machine to

make their model, which is what they used to make it. Python was used to teach

people how to do multivariate analysis, build multivariate analysis, and figure out

what class they should put their data into. For example, they wanted to figure out

which option best matched the destination value. They looked at options such as

hour and longitude as well as day, week, week, and month as well as the destination

value (Divisions of Crime). This way, we turned each option value into a separate

attribute. We then turned each option value into either 0, 1, or both. To do regression

114

on the training dataset, there were a lot of different ways to do it, so it was split into

two groups: training and testing. All of the tests were done this way. To get the test

data's results, the method that caused the least damage was used.

The KNN method is used to figure out what kind of crime will happen, when, and

where.

For continuous variables, two of the most common metrics were used to figure out

how accurate they were, and they were used to see how well they worked. Two of

them are called the Root Mean Squared error (RMSE) and Mean Absolute error

(MAE).

The work successfully achieved higher accuracy in prediction which was proved by

the values of RMSE and MAE, which had reduced significantly.

Table 1: Result comparison[5]

Previous Calculated MAE 0.1598 0.0064 RMSE 0.3997 0.0802

KNN SCORE 0.9323 0.9951 Over the past several years, the crime rate has increased to such a high point that we

must start looking at methods that will help reduce this rate. It helped them write this

paper when they used machine learning algorithms like the Support Vector Machine

(SVM), K-nearest neighbour (KNN), Decision trees, and Artificial Neural Networks

(ANN) to help them. (ANN). A dataset from San Francisco will be used in this paper

when it is done. When you look at the dataset, you'll see that it has eight attributes

called Date and Description as well as 884k data points. After converting the

categorical data to numerical data, and removing the outliers, they split the dataset

into 2 parts that are the training and the test set having 80% and 20% data

respectively. This was followed by applying the various models and calculating their

accuracy and f-beta score. The conclusion drawn was that KNN, decision tree, and

the extra tree classifier were giving optimal results with an accuracy of about 79%-

81%. Whereas SVM was taking a lot of time to train the dataset so, it is not an

optimal model for this data.

115

Predicting what type of crime is most likely to happen in a specific area in the

upcoming years gives an edge to the police department and helps them to organize

various activities to prevent it from happening. To serve this purpose the authors of

this paper are trying to make a computer-based prediction system [2] that could be

suitable for the townships of Chile. The dataset is of the capital city of Chile which

is commonly known as the metropolitan region. It comprises of 37 townships.

By taking into consideration the phenomena of “repeat and near-repeat victimization”

also known as the Prospective Method [3]. By using this method, we were able to

predict the number of crimes during a single police shift. The “Dempster -Shafer

Theory Method” [4] and a method made with the help of the Wavelet Transform

known as the multi-kernel method which helps to predict the Spatio-temporal ratios

of crime risk over an area. The average prediction rate was 45.29% but, in some

areas, the prediction rate was above 50%, making it extremely useful for the Chilean

police.

To get insights into the types of crimes committed in the city of Indore, Madhya

Pradesh, and to reduce their rate they used the KNN Algorithm. KNN is a supervised

classification algorithm. After preprocessing and cleaning the dataset, an extra tree

classifier technique was used to calculate the importance of each feature of the

dataset.

In the next step, it was split into two groups: a training set (80%) and a test set

(20%). (20 percent). (20%) After that, they used the elbow method to figure out the

value of k. They then used the k-means clustering algorithm to figure out the

average value of different groups by using the elbow method. Root Mean Square

Error, Root Mean Square Error, Root Mean Square Error It was a lot of the model's

general accuracy that worked out well.

With the increase in technology over time, and all the data is available on the

internet, the crime rate has increased. Various statistical models were applied to the

data taken from the NCRB website of crime rates of India in the past 13 years from

2001 to 2013. The data from 2001 to 2011 was used to predict the rate of crime in

116

the years 2012 and 2013. The models which were used are Weighted Moving Average

(WMA), Function Coefficient Regression (FCR), Arithmetic Geometric Progression

(AGP).

After the implementation of these three models, AGP had the highest accuracy of

89% to 90%. In this model, all the terms are taken as weights of the past year and

then are used to compute the next year’s data. [6]

The general form of AGP is:

a, (a+d) r, (a+2d) r2 .

This is how Mugdha Sharma et al. came up with a new ID3 algorithm. It shows the

importance of attributes that have little value but a lot of importance, rather than

attributes with a lot of value and little importance. In this way, they could solve the

problem of having to choose between a lot of important traits. Tests show that an

ID3 algorithm that is more advanced comes up with more reasonable and simple

rules for classifying things. To look into crimes through emails, someone also

suggested using the Z-crime tool. [6] He and his team came up with a hidden link

algorithm that can find hidden links in the networks of people who have done bad

things together. They show the possible crime partner and other networks that aren't

part of the main network. Each node is also looked at in this paper. These graphs

show what each node is important for in the network and how important they are.

Find out who is the most powerful, how important this person is to the network, and

how this person fits into the network. People who read this paper talked about how

to stop crimes from happening before they happen, how to look at the network of co-

offenders in India, and how to figure out how the network of offenders will look in

the future. He and his team came up with Forensic Tool Kit 4.0, which lets you look

at data from afar and see how it looks. [7] In remote data, you can look at process

information, service information, driver information, network device, and network

information, as well as other types of information. This tool makes the file and looks

at the information in it, then sends it to you. This tool can also be used to look at the

computer where the attack is taking place, too. To solve crimes, physical and logical

memory data is looked at with the help of the police. A group of people, such as K.

117

Zakir Hussain and other people, used data processing methods to look at how

criminals acted in this case. In this paper, they came up with a way to look at crime

investigation data (CIA). Use: This tool was used by law enforcement to help them

solve crimes that were very bad. It was backed up by evidence from the crime scene

and from people who were there at the time. Both in terms of how the study was

done, and in terms of how it was done, the study was done It helped us figure out

who the unknown person was, as well as how to interview and go to court. [9]

Every state's crime rate is looked at by Shiju Sathyadevan et al. through the

collection of knowledge, the classification of it, the identification of patterns and

trends, and the visualisation of it. They do this way. It was made with Naive Bayes

classifiers. They used them to make a model that could be used to classify each type

of crime. In this case, you already know the category for a group of coaching data

points and want to know the category for the other coaching data point. Decision

trees are often used to look for crime patterns and make predictions with the help of

Apriori algorithms, as shown in [11]. Heap maps show how big each thing is. The

colour dark means there aren't many things going on. An algorithm called a choice

tree is often used to look for emails that aren't safe. Iterative Optimiser3 is a better

version of this algorithm that picks better features. [12]. It's better and faster to use

decision trees when these important things are taken into account. In this case, we

used a genetic algorithm to figure out how best to change the parameters of a

decision tree. supervised learning methods and NLP methods are used to classify

data. Machine Learning can predict crimes, and these two methods are used for this.

Another way to predict crime is to combine data with deep neural networks [13]. K-

means clustering is used to look at time and site data from the CLEAR system to

figure out how likely it is that someone will do something bad. There is a lot of

information in the data set about how often crimes happen and what kinds of things

they have, like a description or a month, day, hour, and more. It mostly talks about

the algorithm that can work with both categorical and numerical values, but it also

talks about how it works.

When you buy crime analysis software, it comes with a lot of different ways to work

with your data. Cops can use it to figure out what happened [15]. A clustering

118

algorithm can be used to look at crime datasets and see how crimes are linked, as

shown in figure 16. It makes it easier to figure out and figure out how crimes have

changed over time and how they came to be. If you start with random points, K-

means will make a group that looks like this: This makes it easier to get to the best

place in the area [17]. Because of this, the data was split into groups based on the

information axis that had the best variance for assigning the first centroid for K-

Means clustering, so this worked out well. When it comes to the clustering process,

so far it has been found that the proposed method does not have to go through as

many iterations, which speeds it up In the Hidden Markov Model (HMM), merge

sort is often used to group things together.

"Optimised K Means" is an algorithm that Krishnendu S.G and others used. They

broke the work into two parts. Using the Elbow method, the most important

centroids were found in the first phase. This method is used to find the best K value.

A set of 8 random data items was used to start the process in the first phase. A lot of

this, too. After a lot of repetition, the distance between each piece of data and the

centre is calculated, and the cluster with the closest centre is chosen for each piece

of data. After the first phase, or phase one, the space between each data item and

each starting point is calculated in a second phase, or phase two. This phase is called

phase two. Clusters with close centroids are then grouped together, as shown in this

figure. There are groups of data points that each one is linked to, and the distance to

them will be recorded. If we want to make sure that everything is working properly,

we'll have to change the cluster's centre once more. A lot of the same things are done

over and over again until the new cluster value is good enough to match the old one.

This is what the researcher used to do their work: Spyder 3.7. [19]

In [20], a crime prediction is made on a set of data from Chicago. Various machine

learning models are used during this process. Comparisons of models like KNN,

Naive Bayes, and SVM have been done in this paper, and it's done now Based on the

dataset and features chosen, it has been found that prediction can change. [20] found

that KNN had a prediction accuracy of 78%, Gaussian NB had a prediction accuracy

of 64%, and SVC had a prediction accuracy of only 31%.

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In paper [3,] algorithms like KNN and neural networks are made, tested, and used to

predict crime in San Francisco. The paper also shows how the algorithms work.

It has been found that a lot of machine learning models use datasets from different

cities with different features, so the predictions are different in each case. Classification

models are used for many different things, like predicting the weather, banking and

finances, and security [3]. Predicting crimes with data mining was Sunil Yadav et

al's main goal. They used four algorithms as the main tool. The Apriori rule, K

Means, Naive Bayes algorithm, and Correlation & Regression are all examples of

these. In the apriori algorithm, they used the result of K means as an input. This

allowed them to see if there was a connection between other traits. Before weka

could use the data, it had to be cleaned up a little bit. After that, the data was

transferred to weka and, as a result, we found an association using weka. According

to the results of apriori, there was a link between people charged with crimes and

people found not guilty in that same year. This result said that if more people are

arrested, then more people are freed, and more people end up being found not guilty.

As the rape case was finished for 10 people, they found that the number of people

who were found not guilty turned out to be 2.449. [21]

When Bradley et al. came up with a way to improve the start points of clustering

algorithms, like the k-means clustering algorithm, they came up with a way to do

this. This is what they did: They showed how to change the start line of a general

type of clustering algorithm very quickly and very quickly. All the clustering

algorithms, like K-means, used iterative techniques. The process usually leads to at

least one "local minima," but it can take a while. Because they used this method over

and over again, the algorithm was able to get to a far better local minimum value. In

order to see how well the K-means algorithm clusters a given set of data, we start

with a new set of data that has been better. In the tests, they found that it takes a lot

less time to refine a database than cluster the whole database.

When Likas et al. used K-Means to group things around the world, they came up

with a way to do it that was very easy. Method: It was a small step toward clustering

that animatedly added one cluster centre at a time to the global exploration process.

It ran the k-means algorithm from the right starting points. That's not all they came

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up with. As a result, this can often be done without having a big effect on how well

the answer is written down. Often, the best way to solve a clustering problem with

M groups is to do a lot of small searches for the best answer.

Neighbor Sharing was chosen because of this. Therefore, it was chosen. People who

came up with this method named it "NSS-AK." Yanfeng Zhang and his team thought

of it for this method. Is it possible to use this to figure out how many clusters you

need for your project? Using this method, the NSS-AK method looks for places

where there are a lot of people and then picks the first cluster centres from these

places. In order to pick the first cluster centres, AE and the NSF were used. Each one

shows the number of things there are in the world and how close they are to each

other near where they live. In this paper, these people came up with a way to do k-

means clustering that was very efficient and used influence factors to make it even

more efficient. In this way, things are put together. Has two parts. It can figure out

the right value of k and pick the right places in the dataset if it looks good. A way to

figure out if two groups of people should join. People use algorithms to make things

run more quickly. A theorem is put forward and proven, then it is used to speed up

the algorithm so it can run more quickly.

Telugu Maddileti et al., proposed a system made entirely on the basis of research

work. They made use of the following algorithm Logistic Regression, Decision Tree

classification, Random Forest classification. The dataset used in this work was that

of India and of crimes committed throughout the years of 2001-2018 which was

available on dataset world. They came out with an accuracy of 95.12 for random

forest selection, 78.955 for Logistic classification and 51.068 for decision tree

classification. After the result analysis they concluded that decision tree algorithm

was relatively poor as compared to two others as it had shown the least accuracy.[26]

There were a lot of different models used to predict crime in Mississippi, and the

results were 83 percent, 88%, and 67% for each one of them [27]. It's been found

that a lot of machine learning algorithms are used on data sets that include different

places with different features, so the predictions change in every case.

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It came from kaggle.com, and we used logistic regression, K-Nearest Neighbors

(KNN), Decision Tree Classification, and Bayesian methods to look at the data, as

shown in this video. It's done by getting rid of the empty fields and filling in the

blanks. It was 78.73 percent for the KNN and 78.60 percent for the decision tress

classifier, In this table, we show how well each of the above machine learning

algorithms does. SVC was used by 31 percent of people and Naive Bayes Gaussian

by 64.60 percent of the people. Clean and process the model before it is used to

train.

Somayyeh Aghababaei and other people came up with a way to figure out what will

happen next.: They turned trend prediction into a task that could be done by grouping

things together. To figure out how strong they thought the person was, they used a

set of dictionary books. With the last idea, they also came up with a way to get a

small number of tweets from Twitter by taking a sample. [29] Twitter content was all

they used to show that content and crime trends are linked, even though they used

only Twitter content. Philly was full of crime, but also a lot of violence and crime.

As far as I can tell, predictions seem to be based on what kind of crime was done.

Some crimes, like burglary and sex crimes, had a lot to do with what people shared

on Twitter.

Crime prediction issues were dealt with in many ways. Using traditional methods,

which are used by law enforcement, is mostly about making maps of hotspots. These

maps can only be used in certain places and can't be used in other places. Usually,

there are ways to get more information about spatial features, like how far a person

is from intersections and highways, schools and businesses, and more about the

neighbourhood [31] [32].

We can think about future crimes in terms of the crimes that have already been done,

like Mohler et al. [32, 33]. Another study looked at how the social fabric of a

neighbourhood affects crime [34, 35]. They looked at how well mobile network

activity could predict whether someone was going to get sick or not [36, 5]. Social-

behavioral factors like the personality of a community and how well-educated

people are are being investigated to see how they affect crimes. These factors are

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often used as extra tools to help make the prediction model work. If there are more

people in the world, it may not be easy to see how the huge society affects different

things.

In this modern era, where there is an increase in technology, the capability of people

to commit a crime has also increased. Nowadays, you can find information about

someone directly through the internet, just by the touch of your mobile or computer.

Since the methods by which a particular crime can be commenced are rising so

should the ability to deal with these types of crimes.

Crime Rate can be predicted based on various parameters such as the location of the

crime, age of the person committing the crime, gender of the person, by taking into

consideration if the person has any previous criminal record, etc. At first, the dataset

of a particular area is taken into consideration. Then it undergoes data preprocessing

and when the dataset is devoid of duplicate and irrelevant data then we use Machine

Learning Algorithms to predict the crime rate of the chosen area.

The accuracy of the prediction depends upon numerous variables present in the

dataset and may vary. Many researchers have also found that the prediction of crime

rate also depends on what type of dataset is used either regression or classification.

Mainly, classification is when you must predict a label whereas in the case of

regression it is about foreseeing a quantity.

In [1], crime was predicted using the geographical features of the location. The case

study of Taoyuan was considered. Taoyuan is one of the largest cities in Taiwan. The

dataset was from 2015 to 2016 having around 20,000 total crime cases. The prediction

target of this dataset was vehicle theft because it is affected by the change in

environmental conditions around it. Various machine learning models were applied

to the dataset, and it was found that deep neural networks (DNN) had the highest

accuracy, This research paper exhibits the significance of the geographic component

design for further enhancing execution and informative capacity. As stated in [2],

there is a need to find patterns and clues which can be used to predict the jewel theft

murder by analyzing various arguments present in the dataset. The proposed method

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is to use data mining as well as machine learning algorithms to build an automated

tool that can do the prediction in a comparatively small amount of time. Unified

Modelling Language was also used to virtually represent the architecture of the

crime scene also, after testing various models’ decision tree algorithm was selected

to obtain the desired results. Nowadays, many websites provide accurately and

enrich datasets of almost every place around the world. Similarly, one of such sites

named Kaggle was used to access the crime detection problem dataset in the city of

San Francisco in [3], the chosen dataset has labeled data, but it is too large so, it is

reduced to 8000 records by taking help of K-means clustering algorithm. The format

of some features was also changed, for example, the date was removed from yyyy-

mm-dd hh:mm:ss, and time was converted to cartesian coordinates. Eventually,

KNN, Parzen Windows, and Neural Networks were implemented. By analyzing the

data, they also concluded that the majority of crime was committed between midday

and midnight and also that artificial neural network was the most accurate algorithm.

R.R Shah proposed that [4] that by applying various data modeling techniques such

as KNN, SVM, Logistic regression to predict the number of crimes in Vancouver

city of Canada. The data consisted of many independent variables such as latitude,

longitude, year, month, day, hour as well as the location which helped to predict the

number of crimes (dependent variable). The approach used in this paper [5] is to use

data mining tools to their full advantage. The objective of the paper is to find

dubious emails which may contain some information about any criminal activities.

They are then put under the suspicious category with the help of a decision tree

algorithm to help us find such emails and pave a way to know a little bit more about

criminal activities. The authors of this paper [5] also named the tool “Z-Crime”

where the Z stands for Zero which in turn is their aim to minimize the crime rate and

eventually bring it close to zero. Data mining is the method involved with examining

gigantic volumes of information to find business knowledge that assists

organizations with tackling issues, reduce hazards, and take advantage of new

freedoms. Isha and Varun Jasuja [6] analyzed different ways of wheat production

given multiple factors. The reason this was important to them was that they both

lived in India where agriculture is the main culture that is followed so, they wanted

to find various methods to help this practice. In [7] the objective is to calculate

124

cyber-crime in an area using distinct methods, then the data collected using these

methods is stored in the form of a log file and saved in the database. After that

forensic toolkit is used for data analysis and helps to find the forensic evidence

which in turn helps the police department in their criminal investigations.

Criminal science is a region that centers on the logical investigation of crime and

criminal conduct and is an interaction that expects to distinguish criminal attributes.

It is quite possibly the main field where the utilization of information mining

strategies can create significant outcomes. An expansive examination of unlawful

action uncovers that all criminal conduct shares a typical arrangement of widespread

standards. This paper [8] solely focuses on observing the criminal behavior of the

culprit using many data mining models. In [9], the data is taken from various

websites, blogs, and social media platforms then the data is bonded together in the

form of a single database with the help of MongoDB. Naïve Bayes algorithm is later

used for prediction, as it is a supervised learning algorithm so the data must be

labeled. The criminal data is categorized into various types such as robbery, rape,

sex abuse, burglary, etc. The dataset had a zero-frequency problem but by using

Naïve Bayes it was also settled and was successful in predicting the crime-prone

areas in India on any given day. H. Benjamin Fredrick David and A. Suruliandi’s

[10] main aim to write this paper was to put together information that is available on

the internet along with other geospatial information and then by using NLP methods

and other data mining techniques and eventually help divide data into various

categories. The quantitative analysis [10] delivered results that show the rise in the

precision level of classification given utilizing the GA to improve the boundaries.

This happens in light of the capacity of the GA to get familiar with the ideal qualities

and afterward it is applied to set the boundary to ideal worth when performing

estimation. Additionally, the Precision, Recall, and F-esteem fluctuate from the

dataset and the framework. This shows the SIIMCO performing admirably when

characterized as far as the measurements. In this paper [11] a basic test that took the

help of an Apriori model was performed to find whether it can be used to find

similar patterns or designs following the bicycle theft dataset, as noted by the police

department. An Apriori algorithm continues by distinguishing the successive

125

individual things in the information within the database and increasing them to

larger things sets if those thing sets show up adequately frequently within the dataset

and variables related to modus operandi also played a major role.

When the total number of observations is to be divided into a particular number of

clusters, taking the help of the nearest mean is known as K- means clustering

algorithm. Similarly, in [12] K-means algorithm was taken into effect for examining

crime in England and Wales along with the rapid miner tool. The dataset that was

chosen had five attributes: Year, Homicide, Attempted Murder, Child destruction,

and causing death by careless driving. Five clusters were formed depicting the

depleting homicide crime numbers from 1990 to 2011. The dataset used in this paper

[13] was taken from Integrated Network for Societal Conflict Research (INSCR)

official website. The data is of various states of India having multiple attributes such

as year, dacoity, crimes concerning murder, etc. Test results demonstrated that the

procedure utilized for the forecast is exact and quick. The four clusters had the

following observations: C0: Crime is consistent, C1: Crime is increasing or in

motion, C2: Crime is, for the most part, expanding, C3: Few crimes are in flux. The

crime dataset used in [14] depicts all the offenses of India from the year 2013 to the

year 2018. Here, a mixture of both K- means clustering algorithm, as well as the

rapid miner tool, was used to predict the crime rate in the upcoming years and to

bring about precautions in the future to reduce it.

By using the K-means algorithm, the results which are initialized in the starting

attains the local optima. So, to beat this drawback another strategy was proposed in

this paper [15] where a specialty of the initialization segment of K-Means so that the

overall performance of clustering is enhanced so modified centroid selection method

was used. The proposed bunching method is assessed utilizing a unique dataset,

specifically, Wine, Iris, Glass, and Leukemia. Also, by using this the clustering time

was decreased. The execution has been done in Python language [16], to find which

Indian state has the greatest number of criminals by taking the help of the clustering

algorithms and then by looking at the clusters that were formed. Eventually, to help

the government to have greater awareness. The dataset used had 1053 total values,

but they were not in a particular range so, to ease the prediction process normalization

126

was performed. In conclusion, the number of male criminals was maximum in

cluster two and are from Madhya Pradesh whereas the female criminals in cluster six

were in high amounts and mostly were from Maharashtra.

For this paper [17], the data records were provided by the Chicago Police

department and the dataset was taken from Kaggle in CSV format. The dataset had

to undergo data preprocessing as it had many null values which were gotten rid of by

using this command df. dropna(), here df stands for data frame. After that, all the 10k

entries in the data set had to go through feature scaling. Then the dataset was divided

into a training set and testing set. The training set was around 70% - 80% on the

other hand the testing set was 20%-30% of the total data available. The actual

number of criminals arrested by the police department for their offenses was only

32.8%. Various algorithms such as K nearest neighbor, Support Vector Machine,

Decision trees were used. The KNN model had an accuracy of 0.78, SVC had 0.31

and the Decision Tree classifier had 0.78 as well. Many graphs such as scatter, bar,

pie and line were made visualizing all the results made by the algorithm on this

dataset.

Conclusions

The study of various research told us about the different algorithms and ways to

predict crimes based on different areas, place and time. Among all the algorithms

used, regression and decision tree algorithm were the most common. However,

decision tree gave relatively low accuracy as compared to other algorithms. KNN

algorithms was frequently used too, and the results show an impeccable accuracy.

The prediction of crimes if done through any of these methods will result in a more

efficient crime solving and a comparatively less crime rate.


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CHAPTER 10

BIO- PSYCHOSOCIAL CORRELATES OF DRUG ABUSE IN

ADULTS AND ADOLESCENTS

Regina Bahl Department of Psychology


[email protected]

Dr. Geetika Tankha* Department of Psychology



******************************************************************** Abstract: Drug abuse is a global hazard that ruins lives and families. The present review assesses the biological, psychological, and sociocultural correlates that negatively influence the problem of drug abuse in adults and adolescents. In the beginning, the concept and prevalence of drug abuse are discussed. Then the review narrows down to the adolescent-specific correlates like family background, smoking history, and parental conflicts. It was found that death in the family and divorce are the risk factors while strong parental rules reduce drug abuse. The role of psychological correlates like personality, social anxiety, depression, and individual factors that are different for every person are described. The individual-specific factors are stress, coping, motivation, impulsivity, and trauma. Followed by the psychological factors, biological correlates that are genetics, brain changes, and role of neurotransmitters are elaborated. Lastly, socio-cultural correlates of drug abuse are also discussed. The gender differences and the influence of parenting were seen while no significant urban-rural differences have been found. But a strong association between drug abuse and crime could be found. People with criminal records have a history of drug abuse. Also, people engage in crime so that they have some money to buy drugs. But, no single cause of drug abuse could be found. There is an interactive roleplay of psychological, biological, and socio-cultural factors. Keywords: Drug abuse, genetics, adolescents, biological factors, psychological factors ********************************************************************

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Introduction

Drug Abuse is using a substance in large amounts even if there are significant

problems in psychological, social, health, or work-related spheres. It leads to

maladaptive behaviors, for example, picking up fights when intoxicated. Also, drug

abuse is a hazardous problem in both adults and adolescents that interferes with

societal growth. No specific cause of drug abuse has ever been found. The available

literature points out biological, psychological, and sociocultural correlates of drug

abuse.

Abusing drugs for some time leads to dependence. Substance abuse disorders are a

result of this dependence. At the time of dependence on drugs, more and more

amount of the drug intake needs to be increased so that the ‘high’ is achieved. The

body becomes tolerant to the drug. This is when the physiological changes in the

body’s mechanisms negatively influence the removal of the drug from our bodies.

Then, if an individual does not take the drug on which he is dependent, it would lead

to withdrawal symptoms for the respective drug. For example, sweating.

According to DSM 5, there are ten substances categorized as drugs, namely, tobacco,

alcohol, cannabis, hallucinogens, caffeine, opioids, sedatives, inhalants, stimulants,

and anxiolytics. The brain reward system is activated during drug abuse. Pleasurable

feelings or ‘high’ is induced.

Drug abuse is the result of addiction to substances. The addicted individual longs for

the drug and cannot abstain from it. Mood and behavior are negatively influenced

when the drug cannot be abused. Daily functioning in the spheres of social, work,

and interpersonal growth is affected. The families are badly impacted since the

children in the house also learn to abuse the drug. The family income is also

impacted since the money earned is used in purchasing the drug. If the drug becomes

unaffordable, the addicted individual takes the help of crime. Then comes a vicious

cycle of drug abuse and crime. According to [1], 271 million population consumed

drugs in 2016. There are 188 million cannabis users, which makes it the most abused

drug. Its prevalence rate is 3.8 percent, worldwide.

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The survey by National Drug Dependence Treatment Centre (NDDTC), 2018 revealed

that 2.8% of people in India abused cannabis. Heroine became the most commonly

abused opioid, with 1.14% abusers. 1.08% and 18 lakh adults abuse sedatives and

inhalants respectively. Lastly, around 0.1% of Indians abuse cocaine.

Figure 1: Comparison of Drug abuse in India (Image: socialjustice.nic.in)

[2] found that drugs are more abused in Indian slums as compared with the rest of

the population. Tobacco is the most abused drug, 53.9% of those living in slums

abuse it.

The riskiest age range for drug abuse is 12 to 17 years. Adolescence is the

developmental period and drug abuse during this time is extremely harmful to

overall growth. Also, the academic growth of the teen is negatively affected. The

adolescent begins to miss lectures, becomes distracted and inclined towards other

risky behaviors. Delinquency is a parallel that runs alongside drug abuse.

Biological factors responsible for this hazard are neurotransmitter imbalance,

structural changes in the brain along with genetics. Also, abnormalities in the

connectivity within the brain and problems with the default mode network are

probable biological factors responsible for the problem of drug abuse.

Among the psychological factors, personality, social anxiety, depression, and those

individualistic factors that are specific to each person are correlated with drug abuse.

Personality is the unique characteristic way of responding to individuals and

situations. Social anxiety is the fear of being negatively evaluated and getting

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embarrassed in social situations. Depression is the persistent sadness along with loss

of interest in activities that were once enjoyed. It involves impairment in daily

functioning like sleep- disturbance, fatigue, loss of appetite, etc. Individual factors

are those that differ for each person and can include coping, levels of motivation,

sensation-seeking, impulsiveness, trauma, and sleep disturbance.

Social Correlates are differences in drug abuse that are related to gender. It includes

what type of drug is abused more amongst males and females. Social correlates also

include differences in terms of urban and rural areas. Data is collected to find out the

scenario of drug abuse in urban areas and rural areas. The findings are compared.

The drug abuse trends in both types of regions are studied and concluded. Also,

there is always a relationship between drug abuse and the crime done by the

individual who consumes drugs. Sometimes crime is also done to afford the drug.

Another social correlate is what impact does parenting has on the likelihood of an

individual engaging in drug abuse. Parenting strategies or styles have a huge role in

causing or preventing drug abuse. Socio-cultural correlates are basically, the impact

of society and culture on the probability of the person engaging in drug abuse.

The recent findings claim that the most abused drug is cannabis or marijuana. It is

also famous for the names ‘weed’ and ‘pot’. Drug abuse is a global hazard that

affects numerous people and is fatal especially during the developmental period. It

affects the growth of the brain particularly at this time when there are hormonal

changes also.

Drug Abuse in Adolescence

Adolescence is a time of growth in terms of physiological and psychological factors.

There are hormonal changes as well as changes taking place in the brain and any

indulgence in drugs can be hazardous.

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Figure 2: Factors affecting Drug Abuse in Adolescence

Adolescent Family Background

Findings reveal that death, divorce, higher income, and education along with parents

abusing drugs were risk factors for adolescent drug abuse. Parental rules that

disallow drugs and religion were linked with lower chances for the same. [3] found

that adolescents whose parents had a higher income and education abused more

drugs like marijuana and cocaine in their young adulthood. [4], claimed that

adolescents whose fathers abused drugs had a higher chance for the same. [5]

studied adolescents whose both or one parent abused drugs and found that death and

divorce were risk factors. [6] also found that parental practices, rules that restrict

drugs lead to lesser drug abuse in adolescents. Adolescents whose parents abused

cannabis start to abuse it themselves. [7] state that adolescents whose parents abuse

non-prescription opioids and smoke had higher chances to do the same. Religion

was associated with lesser drug abuse risks. The family background of the adolescent is

very crucial since parental practices, rules, habits in parents, income, and education

have a huge impact on adolescent drug abuse.

Smoking History and Drug Abuse

Some findings suggest that smoking is a risk factor for drug abuse, particularly

marijuana. Others claim that marijuana abuse is itself a risk factor for smoking.

Whereas, results also suggest the parallel abuse of marijuana and smoking. [8] found

that marijuana abuse is a risk factor for smoking in female adolescents. [9], claim

that smoking is associated with the risk of cocaine and marijuana abuse in

Parental Conflicts and

Adolescent Drug Abuse

Smoking History

and Drug Abuse

Adolescent Family

Background

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adolescents. [10], suggest that both adults and adolescents abuse marijuana and

nicotine side by side which strengthens nicotine dependence. [11] surveyed a sample

of adolescents and found that they use many types of tobacco products that are

linked with later marijuana abuse. Findings from the available literature, suggest that

drug abuse and smoking together increase dependence. Tobacco abuse alongside

drugs contributes to further risk in adolescence.

Parental Conflicts and Adolescent Drug Abuse

Studies claim that death and conflicts between parents are associated with drug

abuse in adolescents. According to [12], the death of a parent is a major risk for

teens to start abusing drugs, and divorce of parents is also a risk for the early onset

of drug abuse, particularly for girls. [13], claim that those teens who experienced

divorce between their parents were higher on substance abuse disorders. Conflicts

between parents, death of a parent, smoking alongside drug abuse, and the family

background of the adolescent are the crucial correlates of drug abuse during the

developmental period.

Psychological Correlates of Drug Abuse

Personality, social anxiety, and depression along with individual factors that are

different for each person are the psychological factors associated with drug abuse.

Role of Personality

Findings suggest that drug abuse is comorbid with a borderline personality disorder.

Conflicting relations with others and problems with self-image, mood, and impulsivity

are the characteristic problems in this disorder. Aggression, hostility, and neuroticism

are associated with drug abuse. Aggression is the behavior aimed at harming others

and destroying resources around them. Hostility is openly showing that one is

opposed to someone or something. Neuroticism is one of the Big Five Factors. The

person high on neuroticism is overly anxious and worried.

[14] studied medical students who abused drugs and found that they were high on

aggression and hostility. [15] report that drug abuse-related disorders are comorbid

with a borderline personality disorder. According to DSM 5, unstable interpersonal

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relations, problems with self-image, emotions along impulsive behavior are

characteristics of borderline personality disorder. [16] found that females who

abused methamphetamine were high on neuroticism. The person high on this trait

worries a lot and is anxious. They were not conscientious, agreeable, or open to

novelty. Personality is the unique characteristic way of responding to people and

circumstances. As seen from available studies, some personality factors are positively

correlated with drug abuse.

Depression as Correlate

Recent Evidence suggests that depression is positively associated with drug abuse.

According to DSM 5, substance abuse is usually linked with depression. [17]

conducted a literature review and found that people smoke as a way to cope with

depression. [18] stated that major depression and opioid abuse were correlated.

Chronic feelings of sadness, loss of interest in enjoyable activities, and impairment

in daily functioning are features of depression. The present findings match that of

DSM 5 and support the concept of depression being correlated with drug abuse.

Social anxiety as Correlate

Some studies claim social anxiety to be positively associated with drug abuse while

few suggest that there is no association. According to DSM 5, people use substances

to deal with social anxiety. [19] found that a higher need for cannabis was felt during

social anxiety. [20] studied a sample of 56 adolescents who smoked and abused

marijuana. They found that social anxiety was not associated with drug abuse. [21],

carried out a longitudinal study and found that more social anxiety leads to lesser

interaction with the peer group that in turn leads to lesser cannabis abuse. [22]

studied fMRI results of drug abusers with social anxiety disorder and found that

there were structural abnormalities in the brain. [23] found that people smoked

cigarettes to avoid social anxiety. The fear of being negatively judged by people and

being embarrassed in situations involving people is social anxiety. Most studies but

not by all have found a correlation between drug abuse and social anxiety.

Individual Factors

The findings reveal that trauma, ways of coping, lower levels of motivation, and

higher impulsivity have a role in drug abuse. Sensation seeking also results in drug

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abuse. Sleep Deprivation is a result of drug abuse along with stress being

significantly correlated.

Trauma as the Trigger

Studies claim that trauma is positively correlated with drug abuse. Trauma is a

severe emotional response after a major stressor. [24] studied people who abused

opioids and found that the trauma they faced could predict drug abuse and the

intensity of trauma meant more drug dependency. [25] found that abuse during and

after childhood was positively linked with consuming drugs. The above studies lead

us to the conclusion that drug abuse comes after trauma and the severity of drug

abuse is linked with trauma severity.

Role of Motivation

Motivation is the willingness and enthusiasm to fulfill our psychological, physiological,

work-related, or any other need. The recent finding by [26] suggests that people who

abused drugs have low levels of motivation.

Table 1: Individual Factors as Psychological Correlates

S.No. Individual Factors as Psychological Correlates

1 Trauma as the Trigger

2 Stress as the Correlate

3 Drug abuse and Coping

4 Role of Motivation

5 Drug Abuse: A result of sensation seeking

6 Impulsivity: A Predisposing Factor Stress as a Correlate

The recent findings claim that stress and drug abuse are positively related. Stress

negatively affects those brain areas that are responsible for addiction. Incentive

sensitization creates a predisposition for stress and drug abuse as comorbid

conditions. [27], the need for drugs and stress is positively correlated. [28] studied

recent evidence and suggested that more stress leads to more tobacco abuse. [29]

suggests that childhood and later life stressors result in drug addiction and relapse.

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[30] claims that stress affects those brain circuits which play a role in addiction. [31]

state that stress and drug abuse become comorbid conditions because of the

predisposition due to the reward being uncertain leading to incentive sensitization.

According to the incentive sensitization theory of substance abuse, drugs create a

neuron sensitivity in the brain and lead to further craving. [32] claimed that cannabis

abuse and being unable to tolerate stress are positively correlated.

The given studies emphasize the fact that stress hampers the functioning of those

brain regions that have a strong role in addiction. Stress and drug abuse are

comorbid conditions. Incentive Sensitization theory also suggests the same.

Drug abuse and Coping

The recent evidence suggests that ways of coping and drug abuse are positively

correlated. Coping is a physiological and psychological way to deal with environmental

stressors. According to the available studies, people who use maladaptive ways of

coping also engage in drug abuse.

[33] found that the females who use strategies for active coping engage more in

drugs abuse. [34] found that people who had drug abuse disorders engaged in

maladaptive coping and disengagement. It can be concluded that drug abuse during

coping with situations is more evident in females with active coping and those with

drug abuse disorders.

Drug Abuse: A result of sensation seeking

Available literature suggests that there is a major role played by the sensation-seeking factor in drug abuse. This is the will to experience thrill, excitation, and novel experiences. [35] studied drug abusers and found that they were high on sensation seeking while their siblings were not. [36] found that sensation seeking could predict nicotine, alcohol, and marijuana abuse. [37] suggest that the correlation between sensation seeking and smoke was more intense in teenagers. [38] claim that sensation seeking was a risk factor for abusing drugs. [39] conducted a study among 300 medical students. They found that sensation seeking was correlated with alcohol abuse and smoke. Evidence suggests that the individualistic factor of sensation seeking has a role in people abusing drugs like nicotine, marijuana, alcohol, and others.

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Impulsivity: A Predisposing Factor

The evidence suggests a strong correlation between impulsivity and drug abuse.

Impulsivity is the lack of control or doing things suddenly. The consequences for

lack of control are not considered. [40] claim that there is a significant correlation

between impulsivity and drug abuse, particularly heroin and opioids. [41] found that

impulsivity could be observed in those who abused methamphetamine. [42] found

that there is a role of dopamine receptors in the correlation between impulsivity and

drug abuse, mainly, methamphetamine abuse.

[43] suggest that adolescents who engage in tobacco abuse, marijuana abuse, and

alcohol abuse are high on the factor of impulsivity. People who are high on

impulsiveness abuse more drugs. There is a link between impulsivity and abusing

drugs like methamphetamine, heroin, opioids, tobacco, etc.

Self Esteem- Drug Abusers

Studies suggest that self-esteem has a role in drug abuse. Self-esteem is the

individual’s belief in his/ her abilities. [44] found that the development of self-

esteem in teenagers promotes alcohol and cannabis abuse. Self-esteem depends upon

the quality of parenting. [45] conducted a comparative study and found that those

who did not abuse drugs had higher self-esteem than the drug abusers. [46], suggest

that self-esteem has a role in drug abuse. [47] also claim that self-esteem is strongly

linked with drug abuse. To conclude, that self-esteem, particularly at the time of its

development during adolescence has a huge role in drug abuse.

Biological Correlates of Drug Abuse

According to available literature, drug abuse has a strong genetic base along with the

interplay of neurotransmitters and changes in the brain structure and volume of grey

matter. Connectivity in the brain regions and default mode network also impacts

drug abuse.

Genetics

The findings claim that genes do have some role to play in drug addiction. Research

has identified some variants of genes. [48] claim that some genetic variants have

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been found to play a role in opioid addiction. A very small role for these genetic

variants has been identified so far. According to [49] some genes have an effect the

addiction to drugs. [50] revealed that there is a role of some genes in the brain’s

reward system. According to recent evidence, some genetic variants that are

responsible for drug abuse influence the brain reward system.

Brain Changes

The available findings state that there are abnormalities with the volume of grey

matter in regions of the brain along with structural abnormalities and connectivity

between the regions of the brain. Also, abnormal connectivity within the default

mode network of the brain could be seen. [51] conducted fMRI of heroin abusers

and compared it with those who did not take the drug. They found that there were

abnormalities in the connectivity for different regions of the brain in those who

abused heroin. [52] carried out a meta-analysis of the available MRIs for drug

abusers. They found that there was a reduction in the grey matter for the regions of

the prefrontal cortex. [53] have found that there are abnormalities with the volume

of the Para hippocampus and orbitofrontal lobe. Also, the size of the limbic striatum

is increased. [54] studied cocaine abusers and compared them with non-abusers.

They found structural abnormalities and changes in the grey matter volume of

various parts of the brain in the cocaine abusers in comparison with non-abusers.

[55] conducted fMRI of heroin abusers and compared it with the non-abusers. They

found that abnormal connectivity in the default mode network of the brain for the

individuals who abused heroin.

Neurotransmitters

Using the available evidence, it can be concluded that the levels of various

neurotransmitters affect drug addiction. [56] found that dopamine and serotonin

have a role in drug abuse, particularly cocaine. [57] state that receptors of N-methyl-

D-aspartate play a role in drug abuse. As studies suggest, there is a primary role of

neurotransmitters mainly, dopamine and serotonin, and N-methyl-D-aspartate receptors

in drug abuse.

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Socio-Cultural Correlates of Drug Abuse

Looking at the available literature, it can be concluded that the quality of parenting

impacts drug abuse. No significant differences between the drug intake of urban and

rural people could be seen. A strong correlation between crime and drug abuse has

been found. Lastly, in terms of gender differences, the adult drug-abusing population

comprises more males than females. The young population of drug abusers does not

show any such significant differences based on gender.

The Influence of Parenting

Studies claim that adolescents of a demanding parent will be less likely to abuse

drugs while the teen of a responsive parent is more likely to do the same.

Adolescents of authoritarian parents were the most prone to drug abuse. The teens of

indulging parents were the least prone.

According to [58] the parenting dimension of demandingness meant lesser drug

abuse, while more responsiveness meant more drug abuse in teenagers. A demanding

parent is the one who controls every aspect of the child’s behavior while the

responsive parent is the one who provides a lot of warmth. [59] found that there

were more chances to engage in alcohol abuse if the parents were authoritarian while

lesser chances for the same if the parents had adopted an indulging style of

parenting. Those parents who neglected their teens or were authoritarian were found

to be even worse than the authoritative parenting style. Authoritarian parents are

those who are too strict and display lesser warmth, indulgent parents are controlling,

while ignorant parents provide full freedom without rules. Lastly, authoritative

parents are those who provide both freedom and warmth in combination with strict

rules.

According to [60] claimed that adolescents of parents who abused drugs themselves

were facing the maximum risk for the same. Quality parenting practices lowered the

risk for drug abuse while no effect could be seen in those teens who had open

conversations about drugs with their parents.

[61] suggest that teens whose parents did not provide them adequate attention were

at the highest risk for marijuana abuse. Growing age reduced the effect of protection

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for the authoritative parenting style, while there was no link between abusing

marijuana and how much attention was given to the teen by parents.

Gender Differences in Drug Abuse

Recent evidence points out that in the adult population, there are more drug abusers

in males but these differences cannot be seen in the young population of drug

abusers. [62] claim that drug abuse differences in terms of gender can be found only

in adults where males are two to three times more prone than females to abuse

drugs. This cannot be seen in teens. [63] suggest that males start drug abuse before

females but, they abuse more methamphetamine than males. Also, the onset of

methamphetamine is later in males than females. [64] point out that female adolescents

are ahead of male adolescents in the non-prescription use of psychotherapeutics.

Whereas, male adolescents abuse more marijuana. In young adults also, females

engage more in cocaine abuse and again non-prescription psychotherapeutic abuse.

In the total drug abuse population, males are more than females and they are more

prone to abuse non-prescription psychotherapeutics along with marijuana.

Association Between Drug Abuse and Crime

The studies reveal that there is a strong correlation between crime and drug abuse.

People who abuse drugs engage in criminal activity side by side. [65] claim that

drug abuse is positively correlated with social reputation, which is the beliefs held

about a particular group or individual in society, and moral disengagement, which is

the way some people make themselves understand that the immoral they do is also

moral so that they can perform that immoral task. According to [66] most of the

people accused of a crime are engaged in drug abuse. [67] found that people engage

in crime so that they could get some monetary benefit to afford drugs. To conclude,

it has been seen that crime and drug abuse go hand in hand. People with criminal

records have been seen abusing drugs also.

Drug Abuse: Urban-Rural Differences

Most findings suggest that there are no major differences in the trends of drug abuse

in urban and rural areas. Few studies claim that people living in urban areas abuse

more drugs. [68] claim that people living in urban areas engaged in more drug abuse

145

than those who lived in rural areas. [69] state that people living in both urban and

rural areas had similar trends of drug abuse. Looking at the above findings, no

urban-rural differences exist in terms of drug abuse. People living in both places do

consume drugs in similar trends.

Conclusion

Drug abuse is a widespread global hazard. There is an interplay of biological,

psychological, and socio-cultural factors in the growing problem of drug abuse.

During adolescence, smoking history has a positive correlation with drug abuse.

Also, the family background that includes parental education and income is again

correlated with the problem of drug abuse. Parental conflicts, parental substance

abuse, and death have a negative impact and increase the likelihood of teen drug

abuse. Strong parental rules lower the chances of drug abuse. In adults, psychological

correlates are personality, social anxiety, and depression along with individual

factors that are specific to the person. There are mixed findings concerning the

relationship between social anxiety and drug abuse. Depression is positively

correlated with the same. Individual factors linked to drug abuse are, for example,

trauma, sensation-seeking, coping, motivation, and so forth. Genetics, structural

brain changes and connectivity, neurotransmitters, grey matter volume, and default

mode network are responsible biological correlates in drug abuse. Concerning socio-

cultural correlates, parenting quality and crime are associated with the problem of

drug abuse. No significant urban-rural differences could be found. Lastly, in terms of

gender, more adult males abuse drugs, but again, no such differences are prevalent in

the young population.

Suggestions

• People should be aware of the harmful impact of cannabis as it is the most

abused drug. It causes memory-related problems, the risk for bronchitis and

asthma. Also, cannabis predisposes an individual towards schizophrenia.

• Psychological help should be provided to those families of adolescents who

have faced death, divorce, or any other conflicts. These conflicts make the

teen more vulnerable to drug abuse. So, psychological help is needed to

prevent the likelihood of the occurrence of this hazard.

146

• Parental substance abuse should be curbed as it badly impacts adolescents.

Then, they start abusing substances themselves. It encourages smoking,

which is further correlated with drug abuse. Early exposure to substances

during adolescence, particularly, is very harmful.

• Most importantly, treatment for drug abuse should be given on an immediate

basis. Early intervention can prevent further deterioration and can stop the

problem from going out of control.

• Empirical data can be used to study the effect of personality traits on drug

abuse. Personality has an impact on each aspect of our behavior. So, using

empirical data, more information can be gathered about its impact on drug

abuse. It can aid in prevention and treatment.

• Family support should be encouraged. It ensures security for the client after

recovery. Strong family support can prevent the individual from beginning to

abuse drugs again. It also helps the client to return to normal life.

• Lastly, to reduce teenage drug abuse, there should be workshops in schools

that emphasize the harmful effects of drugs and help to spread awareness.

Prevention is always better than early intervention.


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CHAPTER 11

FACTORS INFLUENCING RESPONSIBLE CONSUMPTION

DURING POST COVID 19

Anmol Mehta-Research Scholar*

School of Business and Commerce



Dr. Meenakshi Sharma

Asst. Professor

School of Business and Commerce


Dr. Umesh Solanki

Associate Professor

Tapmi School of Business


******************************************************************** Abstract: Covid 19 has disrupted the world in many ways. In this era, it is very

important to study the responsible consumption as it has been observed that during

this time a lot of people have hoarded the daily necessities and also the consumption

of masks and PPE kits was highly exceeded. This paper will throw a light on how

responsible consumption could help us reach a better future for all.

Keywords: Responsible Consumption, COVID 19, responsible consumption behavior

******************************************************************** Introduction

The Sustainable development goals are also known as millennium goals have been

adopted by the United Nations way back in 2015 focusing on 2030. They are aimed

to end the poverty, to protect the Earth in best possible manner and to ensure that all


155

the people all over the world enjoy peace and prosperity. These are interconnected

goals and acting on one goals can help in achieving the other goals as well.

The goal no 12 is Responsible Consumption and Production. It is aimed at achieving

more with the less options. It can help significantly in poverty elevation and lead to

greener economies. Earth has given us abundant resources. We should act in a

responsible manner that we are able to meet the current needs without compromising

the needs of the future generations.

Consumption in a Responsible Manner Behavior is described as acting in a manner

that it has the least influence on the environment in order to meet human needs not

only now, but in the future as well. People try to practice sustainable consumption,

which in turn helps them in making better use of resources which leads to less waste

and pollution. The most common way to achieve this is by doing more with less. To

put it another way, we can meet our needs and desires without diminishing the

planet's natural resources.[5]

We have studied variables that have affected responsible consumption behavior post

covid-19.

Review of Literature

According to cf. Stern, 1992a; Kruse, 1995, there is a problem globally of less

natural resources, the growing population and pollution. Psychology is working

towards developing societies that are least exploitive in the use of the natural

resources present on planet Earth.

According to Foppa et al., 1995; Guagnano et al., 1995, Vining& Ebreo, 1992; ,

Hines et al., 1986r87; Granzin & Olsen, 1991; such sort of behaviours influences

could be seen in the ecological domain. [1]

According to (Sharifah A. Haron et al.) in the past research has been done on

environmentally conscious behavior and they have examined that various factors

tend to influence such kind of behaviour. They are environmental knowledge and the

ability to understand and to evaluate the impact that the society is causing on the

whole of ecosystem.

156

According to (Othman et al.) who has studied the Malaysian teenagers’ the

environmental attitudes and knowledge have been found to influence gender and

ethnicity. It was noticed that the girls during their teenage had developed habits

towards the environmental issues. The author also found that the teenagers in China

were also more knowledgeable about the environmental issues compared to Indians

and Malaysians. The author further mentioned that the overall knowledge of

teenagers belonging to Malaysia was low especially in students belonging to arts

stream when compared with students belonging to science stream.

1. Objective of the study

• To study the influence of environmental value on responsible consumption

behavior post Covid 19

• To study the influence of environmental awareness responsible consumption

behavior post Covid 19

• To study the influence of environmental knowledge on responsible

consumption behavior post Covid 19

• To identify if any difference exist in responsible consumption behaviour post

Covid 19 between male and female students

Hypothesis

• H1A : Environmental value has positive influence on Responsible

Consumption Behavior post Covid 19

• H2A : Environmental awareness has positive influence on Responsible


• H3A : Environmental knowledge has positive influence on Responsible


• H4A : Gender will have significant relation with Responsible Consumption

Behavior post Covid 19

Research Methodology

1. Scope of study :- Final Year University students (Young Consumer}

2. Questionnaire :- Environmental Value:-Franz X. Bogner, 2018

Environmental Awareness:- Arba’at Hassan, et al, 2010 [3]

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Environmental Knowledge :- Sharifah A. Haron, et al. 2005 [4]

Responsible Consumption Behavior:- The General Ecological Behaviour

(GEB) scale by Kaiser, (1998) [2]

3. Type of Study:- Descriptive

4. Sampling technique:- Convenient Sampling Method

5. Sample Size:-30

6. Sample unit:- University Students

7. Analysis:- ANOVA, Regression.

Tables

Data Analysis

Descriptive Statistics

Mean Std. Deviation N

Responsible Consumption Behaviour 3.872605 0.494578 30

Environmental Awareness 3.955556 0.512879 30

Environmental Knowledge 3.9 0.380302 30

Environmental Value 3.815171 0.369467 30 (Bases on calculation by the authors)

Summary of the Model

Model B Std. Error

T Sig. 95.0% Confidence

Interval for B

Low Bound

Upper Bound

Constant 4.711759 1.163912 4.048209 0.000412 2.319304 7.104214

EA 0.13485 0.211121 0.63872 0.528591 -0.56881 0.299117

EK 0.18773 0.274493 0.683915 0.50079 -0.3765 0.751959

EV -0.27205 0.301928 -0.90103 0.375842 -0.89267 0.348575 (Bases on calculation by the authors)

RCB = 4.711759 + 0.13485 EA+0.18773 EK - 0.27205EV

158

● H1A:- Environmental Value is negatively related to Responsible consumption

behaviour.

● H2A:- Environmental Awareness is positively related to Responsible

consumption behaviour.

● H3A:- Environmental Knowledge is positively related to Responsible

consumption behaviour

Relationship of Responsible consumption behaviour and Gender

Sum of Squares df Mean Square F Sig.

Between Groups 0.210355 0.210355 0.210355 0.826327 0.371095

Within Groups 7.127864 7.127864 0.254567

Total 7.338219 7.338219 (Bases on calculation by the authors)

H0 is not rejected and hence, it can be said that responsible consumption behaviour

does not depend on gender.

Findings

• All the three factors are found to be least significant predictor of responsible

consumption behavior post Covid 19.

• Out of the three predictors environmental knowledge has the most influence

on responsible consumption behavior post Covid 19

• Whereas environmental value has the least influence on responsible

consumption behavior post Covid 19

• During the study it is also found that gender does not have an effect on

responsible consumption behavior post Covid 19

Conclusion

Researcher has studied the University students as they are the ones who will take up

the job and become the earning members of the family. They will be the ones who

will be the decision makers in the coming times. The researcher has identified that

the young consumers have not been inclined towards responsible consumption

159

considering the studied factors during and post covid times. It has also shown that

gender does not have effect on responsible consumption behavior post Covid 19

In the literature review it was found as per (VANTAMAY , 2019, Kasetsart

University, Bangkok) it was discovered that it is important as the young consumers

are going to be the decision makers in the coming future. The author focusses on

making early habits for such consumers. If they form sustainable habits this would

help in achieving the responsible consumption goal. Hence for the study young

consumers specially students in the age group of 18-25 were studied.


1. Hines, J. M. et al. “Analysis and synthesis of research on responsible

environmental behavior: a meta-analysis”. Journal of Environmental Education

18 (1986): 1 8. Print.

2. Kaiser, F. G. “A general measure of ecological behavior”. Journal of Applied

Social Psychology 28 (1998): n. pag. Print.

3. Albarracín, Dolores et al. “Theories of Reasoned Action and Planned

Behavior as Models of Condom Use: A Meta-Analysis”. Psychological bulletin

127.1 (2001): 142–161. Web.

4. Arbuthnot, Jack. “The Roles of Attitudinal and Personality Variables in the

Prediction of Environmental Behavior and Knowledge”. Environment and

behavior 9.2 (1977): 217–232. Web.

5. Martin, en Titouan Chassagne. “Sustainable Consumption and Production”.

N.p., 13 Jan 2015. Web. 11 Des 2021.

160

CHAPTER 12

FINANCIAL LITERACY LEVELS ACROSS

POPULATION GROUPS

Pranav Vashista*

Founder

Millenials on Money LLP

Faridabad Haryana


MitaliMadhu Salklan

Student (MBBS, 3rd Proof),

Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences,

Rohtak - 124001, Haryana

[email protected]

******************************************************************** Abstract: People are expected to make a variety of financial transactions and

decisions on savings, investment, borrowing on a daily basis. However, the process

is complex in the current environment in an array of financial instruments and

options available. Financial literacy of common people is therefore an area of

interest and is an imperative skill for grass root consumers. Enhancement of

financial literacy should, therefore, be based on the existing knowledge of the

consumers and form the basis of development of training and skill enhancement

methodologies. An online survey was conducted using a validated combined

Cumurovic-Lusardi scale to identify financial literacy levels across genders, educational

status, occupation, and income groups. Individual responses were collated across the

socio-demographic characteristics and analysed. The percentage of correct response

was more than 60%, being highest for the question on fluctuation in the financial

instruments (90.33% correct responders) and lowest (61%) for investment funds.

Mean score identified was 4.433+1.43 with non-significant difference between the

sociodemographic groups. Difference between the groups who have undergone

previous training and those who find it to be important as compared to those who do

161

not find it important was statistically significant. Most affirm that financial literacy

is much needed but their primary source is their family or rely on information

available online rather on expertise and curated programmes. Financial literacy

levels do not differ between genders, age groups, education levels or work profiles.

Change in attitudes towards is needed to enhance literacy levels.

Keywords: Cumurovic-Lusardi scale, financial training, investment funds, stocks

******************************************************************** Introduction

The digital era has opened many options and avenues for money management which

people are expected to do. Persons are expected to decide their options for saving,

spending and investment. The task is even more important in India where the social

security is meagre and a high percentage of population is below the poverty line.

Financial literacy is a relatively new concept [1] and focuses on enabling

management of income by an individual [2].

It is a vast subject requiring processing economic information and make informed

decisions about financial planning, wealth accumulation, debt, and pensions3.

Requiring an understanding and knowledge of day to day financial dealings

affecting behaviour towards money including earning, spending, and generation of

income. A number of survey studies have been conducted on different population

groups focussing on different facets of the literacy[4,5,6].

The present study was undertaken to identify financial literacy in urban India

population across all age groups using a standardized questionnaire7. This

instrument named as Combined Cumurovic-Lusardi (CL) questionnaire, has a

Cronbach’s Alpha of 0.62 and has good correlation with other existing scales

proposed in literature[8-15].

Methodology

Survey population

A cross sectional online survey was conducted from 30th November 2021 to 12th

December 2021. The survey questionnaire prepared was circulated on the social media

sites (including WhatsApp®, Facebook®, Telegram®, etc.) to students, entrepreneurs,

162

working professionals individually and various groups requesting them to forward

the questionnaire to their colleagues and peers.

All individuals, above the age of 18 years, irrespective of gender, education,

occupation were invited to participate in the survey. A sample of minimum 300 was

proposed for the study. The attempt was to reach to as many individuals as possible

through social media.

Questionnaire

A Questionnaire was created on Google Forms comprising of 3 parts. First section

was a brief about the objectives of the survey, and the consent of the participants.

Second section was socio-demographic information including age, gender, educational

qualification, occupation and previous financial knowledge. Third section was the 6-

item questionnaire (CL scale) with fixed options. Fourth section contained 4 questions

as to if they think financial literacy is important for them and how much are the

willing to spend to enhance their knowledge. Two questions where they have learnt

the financial know how and when a person should be exposed to financial

knowledge had multiple options.

The face validity of the questionnaire was assessed by an economics educator and

researcher about 20 years of experience and another a researcher in public health

with 20 years of experience.

Statistical analysis

Frequencies of responses was counted. Correct response scores to the questionnaire

were counted for the CL questionnaire. Mean and SD of the score was calculated

and compared between genders, age group, occupation, financial training and

perception of importance. Comparison according to demographic characteristics was

done using independent samples t-test or one-way analysis of variance (ANOVA), as

appropriate. The statistical significance level was set at p < 0.05 (two-sided).

Descriptive responses to questions on source of knowledge and stage when a person

is exposed were counted.

163

Results

Responses received

The survey was conducted from 30th November 21 to 13th December 21. The

response rate was approximately one response per hour.

Socio-demographic profile

Age of the participants was between 18 to 75 years, with 55% below the age of 30

years. The number of male participants was more than female participants, with 90%

being graduates or above (Table 1).

Table 1: Socio-demographic profile of responders

Socio-demography Variable Number Percentage

Age

18-25 126 42%

26 – 30 40 13.33%

31 – 35 15 5%

36 – 40 21 7%

41 – 45 31 10.33%

46 – 50 35 11.67%

51 – 55 14 4.67%

56 – 60 14 4.67%

61 – 65 1 0.33%

66 – 70 2 0.67%

71 – 75 1 0.33%

Gender

Male 194 64.67%,

Female 105 35.00%

Prefer not to say 1 0.33%

Qualification

Schooling 22 7%

Diploma 8 2.67%

Bachelors degree 140 46.67%

Masters degree 117 39%

PhD 13 4.33%

Occupation Student 51 17%

Entrepreneur & self employed 78 26%

164

Socio-demography Variable Number Percentage

Private Job 83 27.67%

Family Business 48 16%

Government Job 32 10.67%

Homemaker 6 2%

Retired 2 0.67%

Previous financial training Yes 54 18%

No 246 82% Response to financial literacy questions

Response to the questions was mandatory in the survey and had fixed choice (Table

2). The percentage of correct response was more than 60%, being highest for the

question on fluctuation in the financial instruments (90.33% correct responders) and

lowest (61%) for investment funds.

Table 2: Response to questions

Quest no.

Query Response options Number of responders

Percentage

1 Buying a single share is safer than buying an equity fund.

True or false?

True 78 26% False 222 74%

2 You have Rs 100/- on your savings account with 2%

interest per year. How much will you have after 5 years if

you let your money grow

109.56

23

7.66%

110.41 200 66.67% 111.43 77 25.67%

3 Your savings account earns 1% interest per year, and

inflation amounts to 2% per year. How much can you buy

after one year with the money in your savings

account?

More Than Today 26 8.66% Same as today 36 12%

Less Than Today 238 79.33%

4 Which investment normally has the largest fluctuations?

Savings Account 04 1.33% Fixed Interest 05

1.66%

Securities

Shares 271

90.33%

165

Mutual Funds 20 6.66% 5 Which of the following

statements best describes the main task of the stock

market?

The stock market predicts stock profits

11 3.66%

The stock market leads to an increase

in stock prices

18 6%

The stock market leads to an increase

in stock prices

225 75%

None of the above 46 15.33% 6 Which of the following

statements is correct? Once you have

invested in a mutual fund, you cannot

withdraw the money in the first year

32 10.66%

Investment funds can invest in several assets, e.g., shares

and bonds

183 61%

Investment funds pay a guaranteed

return, which depends on the past

performance

39 13%

None of the above 46 15.33%

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Total score of participants

The demographic variables did not affect the scores and there was no significant

difference between the various groups. Prior financial knowledge and importance of

training were the only factors affecting the scores (Table 3).

Table 3: Score of participants with respect to demographic variables

Variable Score + SD

Significance

Age

18-25 (n=126) 4.67+1.40

0.068* 26 – 40 (n=76) 4.05+1.41

41 – 60 (n=98) 4.43+1.43

Gender Male (n=194) 4.52+1.45

0.962 Female (n=105) 4.28+1.39

Qualification

Schooling (n=22) 4.12+1.71

0.935* Bachelors degree (n=148) 4.45+1.42

Masters degree (n=117) 4.45+1.37

PhD (n=13) 4.31+1.84

Occupation

Self employed, business (n=126) 4.48+1.33

0.301* Job & retires (n=117) 4.28+1.57

Students & home makers (n=57) 4.62+1.33

Previous financial training (Independent t test)

Yes (n=54) 5.06+1.09 0.002

No (n=246) 4.30+1.46

Importance of training

Yes (n=278) 4.47+1.39

0.000 No (n=06) 2.00+1.26

May be (n=16) 4.69+1.35 *Independent t test $One way ANOVA

Importance of financial literacy

Since importance of training was identified to be a variable affecting level of

financial literacy, the responses received were further segregated as per the

demographics (Table 4).

167

Table 4: Importance of financial literacy

Importance of financial training

Amount that participants can spend in a year to gain financial knowledge

Variable Yes No Maybe No spending

Less Than Rs. 1000

More than Rs 1000

No fixed limit

Male (n=194 ) 180 4 10 52 25 30 87

Female (n=105) 97 2 6 32 21 14 38

Age

18-25 years (n=126) 117 5 4 39 21 14 52

26 – 40 years (n=76) 70 1 5 22 13 13 28

41-75years (n= 98) 91 1 6 23 12 17 46

Previous financial training

Yes(n=54) 52 1 1 5 9 8 32

No(n=246) 226 5 15 79 37 36 94 Financial know how

In response to the question on where they get their financial know how from, 132

(44%) responded that they got their financial knowhow from their family, and for

42% online videos was an important source (Table 5). One participant mentioned

bank officials and one mentioned asset manager as their source of information.

Table 5: Source of information

Variable Number* Percentage Family 132 44% Friends 71 23.67% Books 26 8.67% Self enrolled courses 38 12.67% Seminars 46 15.33% College education 62 20.67% School education 18 6% Online videos 126 42% Online blogs 55 18.33% News / Newspapers 06 2% *multiple responses

168

As regards the age at which the person should be exposed to money concepts almost

all the participants were of the opinion that it should be at the education

Table 6: Age of exposure

Variable Number* Percentage

Middle school 130 43.33%

Senior school 133 44.33%

College 113 37.67%

Young professionals 79 26.33%

Middle age 34 11.33%

Pensioners 29 9.67%

Jab jaago 3 1% *multiple responses

Discussion

The new Combined Cumurovic-Lusardi(measure is a 6-item instrument developed

by correlation and multidimensional scaling of 6 previously existing instruments to

give a better representation of financial literacy than the scales being used

separately. The scale was identified to be more reliable with high external validity.

Considering that an online survey was being conducted, this scale was found to be

most suitable requiring minimal time. Additional questions were added to identify

previously existing financial know how and perception of importance of financial

literacy by the participants.

There is a significant difference between the participants attitudes towards training

and if they have taken previous trainings in their scores for financial literacy levels.

Scores across age, gender, occupation were not significantly different.

Although almost all participants affirmed that financial training is important, half of

them would not want to spend any money on gaining it and most of the participants

rely on family, friends and information available online rather on relying on experts.

The level of literacy though present however, may not therefore lead to an actual

change in behaviour and attitudes towards money in such cases.

169

It is imperative that measures are taken to improve attitudes towards money and

making the participants understand the importance of financial literacy before

measure are taken to enhance literacy levels.

The survey had a mass outreach by electronic media, required only a few minutes of

time and the participants were ready to complete the survey. The strengths of the

study are that real time data could be collected in a short period of time, on digital

platform from all age groups and different service profiles.

The study limitations are that the survey was conducted only for a period of 2 days,

the sample was not statistically calculated and the sampling was non-probabilistic.

The persons who were active on social media, are more likely to be responders,

rather than those who have limited activity on social media. Although, presently

there are no means to identify the same, there is a possibility that these persons have

a larger exposure to economic and financial information available on social media.

Also, though participants were from all age groups, number of participants dwindled

with age, which could be due to technological challenges, or other practical limitations.

The study also does not assess how literacy actually translates into behaviour and

attitudes towards money for which larger studies are required.

Conclusion

Participants surveyed in the study had a reasonable level of financial literacy. Most

affirm that it is needed but their primary source is their family or rely on information

available online rather on expertise and curated programmes.

170


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172

CHAPTER 13

SYNTHESIS AND MÖSSBAUER SPECTROSCOPY OF

MARCASITE FES2 NANOPARTICLES

Govind Singh Chandrawat

Department of physics, ISR, IPS Academy Indore, Indore, India

Department of physics, Manipal University Jaipur, Jaipur, India.

Jitendra Tripathi


Jaiveer Singh


Anupam Kumar Sharma*

Department of physics, Manipal University Jaipur, Jaipur, India.


******************************************************************** Abstract: Seeing the possible applications in solar cells and Batteries, we reported

the synthetization and characteristics of FeS2 Marcasite nanoparticles (NPs). Iron

sulfide nano particles were prepared using a Polyol method. X-ray diffraction

(XRD) confirmed the crystal structure of FeS2 nnaoparticles that produced a mean

crystal size of 15.4 nm. The FeS2 NPs were also investigated using Mössbauer

Spectroscopy. Due to large energy and small line widths of Gamma rays, Mössbauer

Spectroscopy gives higher resolution and for this reason this technique is extensively

used here. FeS2 Marcasite sample shows magnetic (Fe2O3) phase as confirmed from

Mossbauer technique. Raman spectroscopy used for vibrational properties and

EDAX was used to find out the chemical composition.

Keywords: Iron sulfide, Nano Particles, Mössbauer Spectroscopy, Polyol method.

********************************************************************

173

Introduction

Much effort has been devoted to the development of advanced anode materials for

lithium-ion batteries (LIBs) to meet the ever-increasing demand for future energy

storage applications [1]. Transition-metal sulfides have received much interest

because of their applications in simplistic producibility, high capacity and

environmental friendly nature [2, 3, 4]. It is reported in the literature that FeS2, exists

in two phases one is pyrite and another one is marcasite. Pyrite-FeS2 has cubic

crystalline structure, while marcasite-FeS2 possesses the orthorhombic structure.

Marcasite shows large electrical conductivity as compared to pyrite due to lower

energy gap [1, 5, 6].

Previous work carried out on Iron Sulfide (FeS2) system, largely depends on X-ray

inspections and analysis of their electrical and magnetic characteristics. From the

Mössbauer Spectroscopy it is found that (FeS2) system has diamagnetic or

paramagnetic properties. For structural and chemical properties and find out the

stability, Rietveld refinement, EDAX and Zeta potential also performed. Vibrational

Properties also carried out using the Raman and FTIR Spectroscopy. For topographical

image FESEM also performed in previous study [7-15]. Iron sulphides are

considered as advanced inorganic materials in relevant applications, such as high-

energy density batteries, photo-electrolysis, solar energy conversion, precursors for

the synthesis of superconductors and chalcogenides [16-20].

Experimental Techniques

Synthesis of FeS2 Nanoparticles and Materials Used

In the synthesis method, analytic reagent grade chemicals were employed.

FeCl3·6H2O (Ferric Chloride), NH2CSNH2 (Thiourea) were purchased from Thomas

baker and C2H6O2 (Ethylene Glycol) from Merck, India

Synthesis of FeS2 Nanoparticles

FeS2 NPs were synthesized by using the well known Polyol method [15]. 1.0 g

(3.699 mmol) of FeCl3·6H2O and 0.75 g (3.2842mmol) of thiourea (NH2CSNH2)

were mixed in 50 ml of ethylene glycol (EG). This mixture was annealed at 180°C

for 3 hours. Due to the production of FeS2 nanocrystals, a dark precipitate resulted.

174

This solution was brought to 27 °C and cleaned using ethanol. This procedure was

repeated 4-5 times to unsheathe nanoparticles. Different techniques for example X

ray diffraction (XRD), Energy Dispersive X-Ray Analysis (EDAX), Raman and

Mossbauer spectroscopy measurements employed on the particles dried at 60°C.

Characterization

For chemical composition analysis Energy dispersive x-ray spectrometer (EDXS) of

Model INCA Oxford was used which is attached with Scanning Electron

Microscope FModel JEOL JSM 5600. For the XRD, the Bruker D8 Advance

diffractometer of UGC DAE-CSR, Indore was used. The Cu-Kα x-ray source with

wavelength 1.54 Å used in the present study. The 2θ range was taken from 20-70°.

The Raman spectroscopy done on the Lab RAM HR Visible instrument (Horiba

Jobin Yuvon) using a Ar+ ion laser at room temperature using λ =488 nm in the

wave number range from 200 to 450 cm-1.

The Mössbauer spectrum was recorded using a proportional counter at room

temperature with the self-assembled multichannel analyzer (controlled unit) in a

constant acceleration. Co/Rh used as a source. The source is vibrated using an

electromagnet as a drive unit. This instrument is capable to perform transmission

and conversion electron modes. Using a superconducting magnet, the system can

provide temperature from 4.2 to 1000 K using an external magnetic field of 7 Tesla

(both parallel and perpendicular geometries). The Excitation energy (Eγ) 14.4 KeV

was used and the velocity range (drive unit speed) was -12 to +12 mm/s (48.075 neV

=1 mm/s). Normos Software, a DOS-based programme, was used to fit the

Mössbauer data properly. For calibration, a natural iron spectrum was used [21].

Results and Discussion

EDAX

Figure 1 shows the EDAX of synthesized Marcasite FeS2 nanoparticles,

demonstrating standard emission maxima of Fe and S elements. The computable

examination depicted that the atomic % ratio of Fe to S is 46.58:53.42, that provides

the FeS2 Marcasite form. According to EDAX maxima, it was seen that Fe lines Kβ3,

Kα1, Kβ1 correspond to energy 7.0850, 6.4038, 0.7185 KeV and S lines Kα1, Kα2,

175

correspond to energy 2.3078, 2.3066 KeV is observed (see table 1 for different

parameters) [5]. Apart from desired elemental presence, one peak of chlorine K

corresponding 0.2020KeV with 0.00 atomic percentages was present in the sample

as shown in figure which is negligible [1].

X-ray Diffraction

For the structural characterization X-ray Diffraction was performed. Figure 2

represents the X-ray diffraction layout of FeS2 NP. Various Bragg peaks can be seen

in the recorded diffraction pattern corresponds to crystal planes (111), (200), (211),

(300),(222),(230) and (231) at 2θ values ~25.59°, 30.06°, 36.32°, 47.72°, 52.48°

respectively. They are related to FeS2 Marcasite and matches with JCPDS No.

370475 [1, 22].

The average crystalline size (L) calculated using Scherrer formula:

)cos(9.0θλ

BL =

(1)

In the above relation, λ=X-ray wavelength, B= FWHM of diffraction maxima, K =

correction factor and θ= Bragg angle. The mean crystal size was observed to be~

15.39 nm. Homogeneous and well adherent NPs have been obtained, exhibiting a

black colour to the naked eye. The lattice constant was found of Marcasite with

Orthorhombic structure (a≠ b ≠ c) is a=5.97Å, b=6.03Å, c=5.94Å.

Raman Technique

To study the vibrational properties of FeS2 Marcasite NPs, the Raman measurement

was performed. The micro-Raman spectrum of FeS2 NPs is depicted in Figure 3.

Three well-known peaks may be seen at 218.4 cm-1, 282.3 cm-1 and 316.3 cm-1 that

are agree well with the literature reports of FeS2 Marcasite structure [1, 23-24].

Mossbauer Technique

To study the magnetic properties, Mössbauer technique was employed on FeS2

Marcasite NPs. The Mössbauer spectrum of FeS2 Marcasite NPs can be seen in

figure 4. Normos Software, which runs on DOS, was used to fit the spectrum and

176

yielded variables such as I.S (Isomer shift), Q. S. (quadrupole splitting) and H. S.

(hyperfine splitting) [21].

Mössbauer spectra of the Marcasite FeS2 NP are very similar in case of doublet to

pyrite. We observed that the magnetic structure produced from H. S. (Hyperfine

Splitting) and non-magnetic compound produced from Q. S. (Quadrupole splitting).

The Mössbauer parameters are given in Table 2, indicating no significant change in

the doublet configuration of the Marcasite FeS2 NP. However, this does not mean

that there are no impurities phase present in the samples.

2nd doublet at room temperature the mean electric Q. S. (∆EQ), mean I.S. (δ), of the

Marcasite NPs are 0.7199±.0208 mm/s and 0.3755± 0.0128 mm/s respectively and

Line Width 0.6345±.0313 mm/s and Area (%) 37.34 [25-27]

In innate iron, the induced magnetic field was found to be 33 Tesla. At 1st Sextant

due to the Induced Magnetic Field (BHF) 30.68±0.094 (Tesla) the mean electric Q.

S. (∆EQ), and mean I. S. (δ), of Marcasite FeS2 NPs are 0.861± 0.032 mm/s and

0.4385± 0.0163mm/s respectively and line width 0.7583± 0.0466 mm/s and Area

(%) 62.66. The hyperfine splitting shows that impurities Fe2O3 Phase, in the Marcasite

lattice. In contrast, no evidence of a singlet peak corresponding to FeS [28, 29].

Acknowledgments

The authors are thankful to UGC-DAE CSR Indore for furnishing experimental

amenities. We are particularly thankful to Dr. Mukul Gupta (XRD), Dr. V. Reddy

(Mossbauer) and Dr. V. Sathe (Raman) for their help in the measurements.

Conclusion

FeS2 Marcasite NPs successfully synthesized using the Polyol method. The mean

crystal size of FeS2 Marcasite NPs was found 15.4 nm. Using the EDAX it is found

that in the sample negligible carbon is present. Raman spectra show the Ag vibration

mode of FeS2. From Mössbauer it is found that Marcasite was found diamagnetic

but due to the Fe2O3 impurities Phase the induced magnetic field was found the

30.68±0.094 Tesla.

177


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181

Figures

Figure 1: EDAX spectrum of prepared Marcasite FeS2 NP

Figure 2: XRD patterns of prepared Marcasite FeS2 NP

182

Figure 3: Raman Spectra of prepared Marcasite FeS2 NP

Figure 4: Mössbauerspectra ofprepared Marcasite FeS2 NP

183

Tables

Table 1: EDAX Parameters of Marcasite FeS2 NPs

Element Weight% Atomic%

C K 0.00 0.00

S K 39.70 53.42

Fe K 60.30 46.58

Table 2: Mossbauer parameter of Marcasite FeS2 NPs

Doublet Signal Marcasite Sextant Signal Marcasite

I. S. (mm/s) 0.3755± 0.0128 I. S. (mm/s) 0.4385 ± 0.0163

Q. S. (mm/s) 0.7199± 0.0208 Q. S. (mm/s) 0.8610 ± 0.0326

Line with (mm/s) 0.6345± 0.0313 Line with (mm/s) 0.7583 ± 0.0466

- - BHF (Tesla) 30.68 ± 0.094

184

CHAPTER 14

CHEMICAL CHARACTERIZATION AND BIOLOGICAL

ACTIVITY OF SILVER NANOPARTICLES BIOSYNTHESISED

FROM EXTRACTS OF CISSUS SP.: NATURAL BONE

HEALING HERB

Smita Purohit

Department of Botany

The IIS University, Jaipur

Rajasthan, India

Divya Pancholi



Rajasthan, India

Niranjan Kunwar



Rajasthan, India

Swati Gupta

Department of Biosciences


Rajasthan, India

Rohit Jain*

Department of Biosciences


Rajasthan, India



185

******************************************************************** Abstract: Green synthesis of AgNPs from leaf and stem extracts of Cissus sps. and

their characterization was performed in the present study. Soxhlet method was used

for extraction of metabolites from stem and leaves of Cissus quadrangularis and

Cissus rotundifolia using two different solvents (methanol and ethyl acetate).

Phytochemical profiling of the extracts confirmed the presence of flavonoids and

tannins in all the extracts except in ethyl acetate extract of C. quadrangularis stem

and C. rotundifolia leaves. Presence of alkaloids and tannins was detected in ethyl

acetate extract and dicholoromethane extract of C. quadrangularis stem and C.

rotundifolia leaves, respectively. Saponins were present in both methanol and

dicholoromethane extracts of C. rotundifolia stems and ethyl acetate extract of C.

quadrangularis stem. Ag NPs were biosynthesized from both leaf and stem extracts

and were characterized through SEM and FTIR. Antimicrobial assays of all the four

crude extracts of C. quadrangularis and NPs derived from leaf and stem extract

revealed that none of the crude extracts showed antimicrobial potential against either

Proteus vulgaris or Micrococcus, whereas nanoparticles derived from stem extracts

showed significant antimicrobial potential, with higher (~2.5 times) activity against

P. vulgaris than that against Micrococcus. On the contrary, all extracts of C.

rotundifolia except dichloromethane leaves extract exhibited antimicrobial activity

against both pathogens. Nanoparticles biosynthesized from leaf and stem extracts of

C. rotundifolia exhibited higher activity than the crude extracts against P. vulgaris,

while it was similar to the crude extracts against Micrococcus. This study therefore

indicates that the rich phytochemical profile and antimicrobial potential of C.

rotundifolia extracts can further be used to develop efficient and non-toxic herbal

drugs formulations against various pathogenic microbes.

Keywords: hathjod, antimicrobial activity, phytochemical analysis, Micrococcus, P.

vulgaris

******************************************************************* Introduction

Nanotechnology has emerged as a revolutionary concept in past few decades and has

markedly extended its applications in agriculture industry by facilitating the

development of various strategies for plant disease management and enhancement in

186

crop productivity [1, 2]. High surface area, activation of novel reactive groups and

unique physicochemical properties of nanomaterials has drawn significant attention

towards development and use of nanomaterials for management of plant diseases [3, 4].

Various types of metal and metal oxide nanoparticles have been developed for

remediation of many plant pathogens including Phoma glomerata in pea, Sunhemp

rosette virus in bean and Meloidogyne incognita in okra and tomato [2]. Among all

metal nanoparticles used so far, silver nanoparticles are most effective due to their

broad-spectrum antimicrobial efficacy. However, toxicity of AgNPs in plants has

been reported due to release of toxic Ag+ ions and stress caused by these NPs,

application of AgNPs at large scale is still limited [2].

Synthesis of silver nanoparticles using plant extracts have gained lot of impetus in

past few decades due to faster synthesis rate, higher stability, more diversity in size

and shape, lower synthesis cost, and environment friendly approach [5]. Some of the

common plants that have been used for green synthesis of silver nanoparticles

include Moringa oleifera, Azadirachta Indica, Xanthium Strumerium, Tinospora

Cordifolia, Brassica Rapa and Red onion [3, 6-11].

Cissus, member of grape family Vitaceae is known as natural reservoir of an

important medicinal compound, resveratrol. This compound has been well reported

to exhibit wide range of pharmaceutical properties including cardioprotection,

chemoprotection, and many more (Bertelli and Das 2009; Das et al. 2011).

C. qudrangularis L. and C. rotundifolia (Forsk.) Vahl are two important medicinal

plants of the grape family known for their diverse medicinal applications in Indian

medicinal system. These are known as natural bone healing plants and have been

used for the purpose since ancient times [12]. Different plant parts of both the plants

have been reported to exhibit therapeutic properties against various disorders

including asthma, syphilis, tumors, haemorrhoids, menorrhagia, leucorrhoea, scurvy,

obesity and gout [13-17]. Apart from being used as nutritional supplement, Cissus

sp. also exhibit pharmaceutical properties including antioxidant, anti-inflammatory,

analgesic, anti-diabetic, anti-neurodegenerative, analgesic, gastroprotective and

antimicrobial [18-20].

187

Therefore, in the present study, antimicrobial potential of different plants parts of

both C. quadrangularis and C. rotundifolia has been studied. Further, the extracts of

different plants parts have also been used to synthesize silver nanoparticles and for a

comparative assessment of their antimicrobial activity with that of the respective

crude extracts. This study provides new insights into some important medicinal

properties and phytochemical composition of these two invaluable medicinal plants.

Materials and Methods

Collection of plant material

The stem and leaf samples of C. quadrangularis and C. rotundifolia were collected

from Smriti Kulish Van Biodiversity Park, Jaipur, Rajasthan, India. Samples were

thoroughly washed under running tap water, air dried, crushed into fine powder and

stored at 4oC till further use.

All the reagents and chemicals used in the study were procured from Himedia, India

Extraction of metabolites

Dried plant material (stem - S; leaf - L) was extracted using Soxhlet apparatus

(Borosil, India) for 24 h in different solvents (Methanol (M) & Ethyl acetate (Ea) -

C. quadrangularis (CQ); Methanol (M) and Dichloromethane (Dm) - C. rotundifolia

(CR)). The resulting extracts were filtered, concentrated upto 1 ml volume and stored at

4oC till further use.

Qualitative phytochemical profiling

All the extracts were analysed for the presence of alkaloids, flavonoids, saponins,

tannins and phenols using standard Hager’s test, lead acetate solution test, foam test

and ferric chloride test, respectively.

Biosynthesis of silver nanoparticles (AgNPs) using Cissus extracts

5 ml of extract was mixed with equal volume of 0.1 M AgNO3 solution and the

mixture was incubated at RT for 20 min or until the color of the mixture changed

from green to dark brown. The nanoparticles were extracted from the mix by

centrifugation at 8000 rm for 30 min. The pellet was resuspended in double distilled

water and stored at 4oC till further use.

188

Characterization of AgNPs

The chemical composition and shape of the synthesized AgNPs was determined

using FTIR and SEM, respectively as per method reported by [21]

Antimicrobial activity

Antimicrobial activity of all the crude extracts and biosynthesized AgNPs was

performed using disc diffusion method. Briefly, sterilized filter paper discs of 6 mm

diameter were dipped in different extracts and AgNP solution and transferred onto

nutrient agar petri plates pre-inoculated with 24 h culture of Micrococcus and P.

vulgaris. The plates were then incubated at 37oC for 24 h and the zone of inhibition

were recorded.

Results

Yield of extracts

Yield of all four extracts has been provided in Table 1. Maximum yield from CQ-S

and CR-S was obtained in methanolic extracts and that of CQ-L and CR-L was

obtained in methanolic and dichloromethane extracts, respectively.

Table 1: Yield of C. quadrangularis and C. rotundifolia extracts

obtained in different solvents

Solvent Yield (g/20 g sample)

CQ-L CR-L CQ-S CR-S

Methanol 1.08 0.67 0.87 1.53

Ethyl acetate 0.77 0.66

Dichloromethane 1.78 0.49

CQ: C. quadrangularis; CR: C. rotundifolia; L: leaf; S: stem Phytochemical constituents of different extracts

Tannins were present in all the extracts of both CQ and CR, while alkaloids were

present only in ethyl acetate extract of CQ-S and dichloromethane extract of CR-L.

The detailed information about the metabolite presence has been summarized in

Table 2.

189

Table 2: Phytochemical profile of different extracts of C. quadrangularis

and C. rotundifolia

Type of Secondary Metabolite

Type of Extract

CQ-L (M)

CQ-L (Ea)

CR-L (M)

CR-L (DM)

CQ-S (M)

CQ-S (Ea)

CR-S (M)

CR-S (DM)

Alkaloids - - - + - + - -

Flavonoids + + + - + - + +

Tannins + + + + + + + +

Saponin - - - - + + + + Morphological characteristics of biosynthesized AgNPs

The SEM images confirmed the size of AgNPs to be ~200 nm, and were majorly

spherical in shape. The AgNPs were present in aggregated form (Figure 1).

Figure 1: SEM image of AgNPs biosynthesized from methanolic leaf

extract of CQ at 50,000 x magnification

Chemical composition of biosynthesized AgNPs

The FTIR spectrum of the biosynthesized AgNPs from different extracts were

almost similar (Figure 2). The peaks at 1624 cm-1 and 1521 cm-1 represents C=O

(carbonyl group) and C–N groups, respectively. Presence of peak at 1748 cm-1

confirms presence of medium stretch from C=O bond due to dimer formation.

190

Wavenumber 2851 cm-1 corresponds to weak carbonyl (C=O) vibrations, wherein

1458 2851 cm-1 corresponds to C=C vibrations. Further, presence of peak at 1248

cm-1 and 791 cm-1 confirmed presence of N–H bond. The C–N bond stretching

represented by peak at 1035 cm−1 is probably due to aliphatic amines. A downward

shift in the FTIR spectra of AgNPs indicates binding functional groups at their

surface. Such downward shift was recorded in C=O and C-N peaks and an upward

shift was recorded in O-H peak.

Figure 2: FTIR spectrum of AgNPs biosynthesized from methanolic extracts of

(a) CQ-L, (b) CQ-S, (c) CR-L, and (d) CR-S

Antimicrobial activity

Only AgNPs biosynthesized from stem and leaf extract of CQ showed significant

antimicrobial activity against both the microbes, while all the crude extracts and

AgNPs biosynthesized from CR exhibited antimicrobial activity (Table 3). CQ-S

synthesized AgNPs showed maximum activity against P. vulgaris and that against

Micrococcus was noted for methanolic extract of CR-L & CR-S and CR-S AgNPs.

a b

c d

191

Table 3: Antimicrobial activity exhibited by different crude extracts and

biosynthesized AgNPs against Micrococcus and P. vulgaris

Extract Zone of Inhibition (mm)

P. vulgaris Micrococcus

CQS Methanol 0 0

CQL Methanol 0 0

CQS Ethyl acetate 0 0

CQL Ethyl acetate 0 0

CQS Nanoparticle 22 9

CRS Methanol 10 10

CRL Methanol 11 10

CRS Dichloromethane 10 8

CRL Dichloromethane 0 7

CRS Nanoparticle 10 10

CRL Nanoparticle 15 9 Conclusion

The present study was aimed to explore the biomedical potential of different extracts

derived from stem and leaf of C. quadrangularis and C. rotundifolia. Phytochemical

profiling of the extracts confirmed the presence of flavonoids and tannins in all the

extracts. Antimicrobial assays of all the four crude extracts of C. quadrangularis and

NPs derived from leaf and stem extract revealed that none of the crude extracts

showed antimicrobial potential against either Proteus vulgaris or Micrococcus, whereas

nanoparticles derived from stem extracts showed significant antimicrobial potential,

with higher (~2.5 times) activity against P. vulgaris than that against Micrococcus.

On the contrary, all extracts of C. rotundifolia except dichloromethane leaves extract

exhibited antimicrobial activity against both pathogens. Nanoparticles biosynthesized

from leaf and stem extracts of C. rotundifolia exhibited higher activity than the

crude extracts against P. vulgaris, while it was similar to the crude extracts against

Micrococcus. This study therefore indicates that the rich phytochemical profile and

antimicrobial potential of C. rotundifolia extracts can further be used to develop

efficient and non-toxic herbal drugs formulations against various pathogenic microbes.

192


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194

CHAPTER 15

PROSTITUTION IN INDIA-SOCIO-LEGAL PERSPECTIVE

Damyanti Kanwar Rathore

Research Scholar


Dr. Sunita Khatana*

Assistant Professor



******************************************************************** Abstract: The term Prostitution is ordinarily utilized in enactment ordered in the

nineteenth and twentieth hundreds of years to allude to sex work. The terms'

prostitution' and 'whore' have negative connotations and are believed by backers of

sex worker s to defame. It is a result of this explanation that the term sex work/sex

specialist is liked here, and the term prostitution/whore is utilized just when

straightforwardly citing the source. Here has consistently been a discussion about

the bearing that the nation should move in, all things considered. There's a clear

division between hostile to dealing associations from one perspective and sex

worker s' privileges associations on the other which has prompted methodologies

that advocate the nullification of sex work rather than ensuring the freedoms of

ladies in sex work. India has neglected to perceive the underestimation, weaknesses

and common liberties of sex workers.

Keyword: Prostitution, Socio-Legal Perspective

********************************************************************

Introduction

Characterizing every one of the terms pertinent to the review is significant as it eliminates all questions and sets the heading of the paper. The accompanying meanings of 'sex worker' and 'prostitution' are embraced by the UNDP report named 'Sex Work and the Law in Asia and the Pacific', distributed in October 2012[1]:

195

The term 'sex worker' alludes to "all grown-ups who sell or trade sex for cash, labor

and products (e.g., transport). It is utilized to allude to sex worker s, including

consenting female, male, and transsexual individuals who get cash or merchandise in

return for sexual administrations, either routinely or periodically. They incorporate

consenting to youngsters who are eighteen years or more established. If an

individual has been pressured into selling sex and is selling sex automatically, the

inclination isn't to allude to the individual as a 'sex specialist'. Sex work is named

'immediate' or 'circuitous'. Direct sex worker s recognize themselves as sex worker s

and make money by selling sex. Circuitous sex worker s as a rule don't depend on

selling sex as their first kind of revenue and may function as servers, beauticians,

knead young ladies, road merchants, or lager advancement young ladies and

supplement their pay by selling sex. For the most part, they don't recognize as sex

worker s."

The term Prostitution is ordinarily utilized in enactment ordered in the nineteenth

and twentieth hundreds of years to allude to sex work. The terms' prostitution' and

'whore' have negative connotations and are believed by backers of sex worker s to

defame. It is a result of this explanation that the term sex work/sex specialist is liked

here, and the term prostitution/whore is utilized just when straightforwardly citing

the source.

This paper sees sex work in India, extensively zeroing in on the social perspective

and attempts to distinguish the connection between the occupation and the Indian

culture.

Goals

• To comprehend the discussions about the legitimization of sex work in India.

• To observe the advantages and disadvantages of sex work for individuals

locked in.

• To see how hostile to dealing estimates make sex worker s more helpless.

196

History

Different sources talk about the 'devadasis' or 'Vishkanyas' or 'Nagarvadhus' which

demonstrates the presence of a framework fairly comparative if not by and large as

old as one existing today. These sources likewise talk about the further corruption of

the current framework with the passage of the Britishers who presented the idea of

casual sexual encounters prompting a decrease in sanctuary moves. Expanded

utilization of sex worker s started during the Company rule in India. Military-run

whorehouses obtained ladies and young ladies from rustic regions and were paid by

the military straightforwardly.

As of now, there are around 20 million sex workers in India, out of which 35% are

underneath the age of 18 years (Human Rights report). As per gauges, there are a

large portion of a billion youngsters in massage parlors in India. These kids are sold

by their folks for cash or are survivors of misuse. Countless young ladies brought for

body exchanging are from Nepal. India is home to numerous seedy areas of town

which are available in pretty much every state. The accompanying guide shows the

area of eight of the biggest seedy areas of town in the country.

Sex work and related laws

The Immoral Trafficking (Prevention) Act, 1956 and section 372 and 373 of the

Indian Penal Code[2] are the essential laws in India that arrangement with sex work

in India. ITPA targets abrogating and restricting sex work at last. Section 372 and

373 confine the selling and buying of minors for sex work.

Prostitution is legitimate in India however requesting, pimping and massage parlors

are illicit. Purchasing sex is legitimate here, however selling is restricted. In an

article distributed in The Hindu on October 24, 2014, named NCW Chief for

sanctioning sex exchange, the then NCW Chief, Lalitha Kumaramangalam, upheld

legitimizing sex exchange. She said that without any guideline, sex worker s are

compelled to serve customers in unhygienic conditions influencing their wellbeing.

She said that enactment would cover different angles like working hours, medical

services, compensation, and training and monetary choices for their relatives.[3]

197

Umberto Bacchi in a Reuters article distributed on December 12, 2018, discussed a

report as per which legitimizing Prostitution would bring down brutality and

infection. The examination, distributed in a diary PLOS Medicine, investigated

information from in excess of 130 examinations on 33 nations - from Britain to

Uganda - distributed in logical diaries between 1990 to 2018. It found sex worker s

who had been presented to severe policing like capture or jail were multiple times

bound to encounter sexual or actual viciousness by customers, accomplices and

other people.[4]

There has consistently been a discussion about the bearing that the nation should

move in, all things considered. There's a clear division between hostile to dealing

associations from one perspective and sex worker s' privileges associations on the

other which has prompted methodologies that advocate the nullification of sex work

rather than ensuring the freedoms of ladies in sex work. India has neglected to

perceive the underestimation, weaknesses and common liberties of sex workers.

Amazingly, the ITPA in real life impacts the existences of sex worker s by

condemning work, family and the option to bring up kids; the right to protection;

uncalled for or constrained ousting and expulsion from homes or some other spot;

and by nullifying grown-up assent.[5]

Pros of sex work for the sex workers

Prostitution goes about as a type of revenue for certain individuals where an

individual participates in sexual demonstrations in return for cash. Germany and the

Netherlands are models where Prostitution is controlled as a calling. Prostitution

goes about as a wellspring of fulfillment for individuals who are purchasing as well

as for some who are selling. A few investigations guarantee that a few whores feel

approved and engaged, countless indoor sex worker s are accounted for to encounter

an increment in confidence. Now and again, Prostitution has been giving a similarly

better way of life to individuals engaged with Prostitution than previously. In any

case, this has been for the most part found in the space of authorized prostitution

zones. One late review has shown that Rhode Island observed the assault pace of

state decay fundamentally because of the presence of legitimate indoor Prostitution

from 2003 to 2009. Hence if this review is to be accepted, one might say that the

198

presence of Prostitution (legitimate) prompts a decrease in assault. Yet, it can't be

said without a doubt if the diminishing in assault rates in Rhode Island was

unadulterated due to Prostitution and not different measures/activities. Prostitution

has been utilized as a wellspring of sex for individuals who are impaired, which has

demonstrated supportive in settling their requirement for sexual relations.[6]

Cons of sex work for sex workers

sex worker s may find leaving from the calling troublesome. For ladies who are dealt

from Nepal to India, there is an exceptionally insignificant shot at leaving. The

business of Prostitution energizes misuse, persistent stockpile of injury, concealment

of ladies engaged with Prostitution. There is a high danger of physically communicated

sicknesses, for example, HIV for individuals who trade sex as a kind of revenue. The

nations where Prostitution is occurring underground, unprotected sex prompts

undesirable pregnancies. [7] Therefore, whores are being compelled to end their

pregnancies. Prostitution prompts illegal exploitation, business sex ventures and

youngster dealing alongside compromising the well being of whores. As per an

article in the World Development in 2012 that nations with sanctioned Prostitution

have detailed having a measurably more extensive frequency of illegal exploitation

inflows. It is a generally held view that Prostitution debases the ethics of a person as

individuals who utilize this assistance can't handle their sexual cravings, which is

unfortunate and inadmissible by society. The disgrace corresponded with Prostitution in

the public arena has been there, the local area consistently peers down on individuals

engaged with Prostitution.

Conclusion

As shown by different sources, wasteful enemy of dealing laws have made sex

worker s considerably more powerless. The sex worker s confined when the

whorehouses are struck are shipped off restoration and amendment homes. After the

salvage, they are placed in 'safe guardianship' and made to sign an oath expressing

they won't ever return to the calling. [8] The sex worker s are constantly checked out

with hatred by the general public or casualties who should be protected. Nobody at

any point thinks about imagine a scenario in which some sex worker s must choose

the option to stay in the work essentially on the grounds that there could be no other

199

type of revenue. Defective laws are far and away more terrible than no mediation by

any stretch of the imagination. The police treat them without risk of punishment. In

a meeting, a sex worker in Songachi said that when she fled from her first

whorehouse and reached a cop, he assaulted her alongside three different men.

Eventually, she returned to one more house of ill-repute in a similar region.

Recommendation

1. Sex specialists have a top to bottom information on the sex business and are

the only ones to have encountered life as a sex workers. They ought to be the

main impetus behind enemy of dealing measures, not the inadvertent blow-

back of decides that mean to save them.

2. Laws pointed toward giving sex workers one more occupation after they are

saved from houses of ill-repute should appear.

3. Society requirements to acknowledge individuals occupied with this work

eventually on schedule. Talks or workshops can be led to sharpen the general

population about the equivalent.

4. There are various areas of society to see how they see sex workers and

afterward the discoveries can be utilized to additionally figure out how the

connection among local area and sex workers can be improved.

5. The police should be the ones who assist with sex workers and not exploit

them. The choice of rebuffing such work force can be investigated to

advance the circumstance.


1. Godwin John, “Sex Work and the Law in Asia and the Pacific Laws, HIV

and human rights in the context of sex work”, October 2012, file:///C:/

Users/91978/Downloads/HIV-2012-SexWorkAndLaw.pdf.

2. Ratanlal Ranchhoddas. Ratanlal & Dhirajlal’s the Indian Penal Code (Act

XLV of 1860). New Delhi :Wadhwa & Co., 2007.

3. The hindu, ramachandran smriti, “NCW chief for legalising sex work,”

October 28, 2014, https://www.thehindu.com/news/national/ncw-chief-for-

legalising-sex-trade/article11086511.ece.

https://www.thehindu.com/profile/author/Smriti-Kak-Ramachandran-395/

200

4. Bacchi Umberto, Thomson Reuters Foundation, “Legalizing prostitution

lowers violence and disease”, December 12, 2018, https://www.reuters.com/

article/us-global-women-prostitution-iduskbn1oa28n

5. ibid

6. Anand Sonali , Gandhi Bhawna, “Prostitution in India: Sociological Aspect

and Judicial Response”,International Journal of Law Management and

Humanities volume 4,Issue 5,Page 1863-1870, https://www.ijlmh.com/paper/

prostitution-in-india-sociological-aspect-and-judicial-response

7. Kumar Ravi , “Understanding the Socio- Economic and Legal Context of

Prostitution and Sex Trafficking on Women in India”,Indian Journal of Law

and HumanBehavior, Volume 5 Number 3 September December 2019,

https://www.rfppl.co.in/subscription/upload_pdf/KV%20Ravi%20Kumar%2

04_10181.pdf

8. Dkhar Anamika, Gupta Avik, “Socio-Legal Obliteration Of Sex Workers”,

October 01, 2020,International Journal of Advanced Legal Research

https://www.reuters.com/journalists/umberto-bacchi

https://www.reuters.com/article/us-global-women-prostitution-iduskbn1oa28n

https://www.reuters.com/article/us-global-women-prostitution-iduskbn1oa28n

https://www.ijlmh.com/paper/prostitution-in-india-sociological-aspect-and-judicial-response

https://www.ijlmh.com/paper/prostitution-in-india-sociological-aspect-and-judicial-response

https://www.rfppl.co.in/subscription/upload_pdf/KV%20Ravi%20Kumar%204_10181.pdf

https://www.rfppl.co.in/subscription/upload_pdf/KV%20Ravi%20Kumar%204_10181.pdf

201

CHAPTER 16

GENDER NEUTRALITY VIS-A-VIS STATUTORY PROVISION

Ms. Neha Jain Research Scholar Manipal University (Jaipur) Dr. Sunita Singh Khatna Assistant Professor Manipal University Jaipur [email protected] (Corresponding Author) ******************************************************************** Abstract: We have been brought up in a culture where society directs that men ought to be brave, dominant and strong, while women are expected to be fragile and submissive. Women tolerate a number of assaults, aggressive behavior at home, harassment, and so on, but it would be oppressive towards men if we don't recognize that they go through these outrages, shocks and monstrosities as well. Abusive behavior at home and forceful conduct at home against men is now not a deviation, but rather an authentic issue. The domestic violence and aggressive behavior at home against men is at this point not a deviation, but a genuine issue. The term 'manliness ' and 'masculinity ' burdens men throughout of their lives. The possibility of sexual impartiality inside the crime thing alludes back to the possibility of correspondence inside the prevalence of the freedoms surprisingly paying little heed to their sex. At the point when we endeavor to search for equity of these men through our Constitutional arrangements or Indian Penal Code or some other law, we understand that there's seldom any arrangement which shields the honor and character of men in our general public. Remembering, the vulnerable state of all kinds of people in the general public, being a newborn child or a grown-up or even a senior resident, it is important for the public and law to take all the genders on impartial laws. There is an urgent requirement for a Gender Neutral Society. Uniformity and equality among gender should be in genuine expressions, and not simply to us and books. Sexual impartiality is inherently associated with sustainable development economical, academic fulfillment and political establishment. There is a need to make the world a better place for each and every gender irrespective of any biasness. Keywords: Domestic violence, men casualties, India, unbiased and gender-neutral laws. ********************************************************************

202

Introduction

Gender equity Rights of one, Revocation or Abrogation of another?

The legal structure of a country is built totally on its society. The idea of gender

neutrality inside the felony thing states back equality as a concept inside the

popularity of the rights of all people regardless of their genders. The meaning of

equality in words of Aristotle's dictum is that ‘equality approach treating likes alike,

in contrast to, unalike’. The Oxford Dictionary defines ‘Gender Neutrality’ as an

adjective which is suitable for, or relevant to, each character irrespective of gender,

but it's far slightly practicable in modern-day global where legal guidelines are

misused which might be meant for protection.

Apparently, the law seems, on one hand is gift and blessing for individuals in

abusive and violent relationships. Nonetheless, a cautious assessment shows that,

underneath the ploy of "women welfare", is emphatically one-sided towards ladies

and somewhat offers little or no importance to men.

The laws concerning maltreatment of women beginning from sexual to monetary

doesn't recognize the maltreatment a male deals, showing a difference inside the

criminal mechanism. Albeit (despite the fact that) females are in more range of

sufferers paralleled to guys, the figure is slowly falling in the face of false claims

against innocuous men.

In the prehistoric development and culture young women have been given the very

standing as that of men, they were managed in basically the same manner and

carried on with an honorable and deferential life. Afterward, as we progressed and

our civilization developed and enlarge, provisions for women fall. Females, who

have been managed as equivalents, begun generalized and begun losing their respect

and the position they occupy in society. Women had been taken advantage through

men and tagged as vulnerable segment or the "significantly less honorable" segment

of the general public. The mistreatment towards women developed to such a volume

that a revolt was essential as a method for keeping the equilibrium among nature's

two imperative manifestations, male and females; later which there has been the idea

203

of woman's rights known as ‘Feminism’ which assisted with increasing vertical push

the distinction of ladies. Other than the legitimate structure, a change in the public

eye's pattern toward the philosophy of woman's rights has also assumed a central

part. ‘Feminism’ by Merriam-Webster “idea of the political, economic and social

equality of the sexes.” Still it is painful truth, despite society's shift, the country is

unsuccessful to embrace a realistic method to it, misinterpreting it as for benefit of

single gender, namely women, resulting to faux feminism i.e women leading to

pseudo feminism. Our society has finally arrived at a point where women have equal

rights and protection, despite the fact that we still see examples in our daily lives

where women take advantage of this socially, economically, and in all other parts of

life.

But if we look around, we are able to identify the indistinguishable matters where

men too harassed, by women and now and again through different men. At the point

when we endeavor to search for equity of men by our Constitutional arrangements or

Indian Penal Code etc, one can conclude that there's rarely any arrangement which

protects the honor and role of men in our general public. Is modesty exclusive to

women, just they only confront provocation in the public arena? Does a man haven't

any modesty in any respect? It isn't truthful to preconceive that only females face

violence? It is not good to pre-establish that only women suffers harassment? Isn't it

untrue to assume that only women are subjected to mental or physical torture or

other forms of violence, while males are spared?

Keeping in mind the hopeless state of all kinds of people in the general public, being

a newborn child or a grown-up or even a senior resident, it is vital for the general

public to take on sexually unbiased laws or gender neutral laws. The impact of these

regulations places an undue advantage to women for intimidation and extortion in

marital disagreements, causing irreparable harm to spouses and their relatives and, in

some cases, leading to their death [1]. Why, then, the regulations pertaining to these

horrible criminalities exclusively safe females, not males?

The idea of equality, fairness and discrimination cherished under - Article14 [2],

Art.15 [3], and in Art15 (3) “state is empower to make special provision for women

204

and children”. Article 39(a) [4] giving its residents, people, equivalent right to a

sufficient means to livelihood. The article 39(d), article 42 [5], and Article 51A (e) [6].

Similarly preamble and article 7 and 8 of UDHR, 1948 guarantees that “individuals

are equal before law and are authorized equal protection without discrimination”.

Article 3 of the Covenant on Economic Social Cultural and in Civil and Political

Rights 1966, “contributes that parties to contract to assure equivalent freedoms for

people, provided in agreements”.

'Because of the simultaneous action of Article 15(1) and (3), the State may

discriminate in favor of women against men, but it may not discriminate in favor of

men against women,' according to a leading case [7]. Discrimination would be

acceptable as long as it was not solely based on gender.' The Indian Penal Code,

provides shields to women against wrongdoing and unfairness for example, ‘Section

294, Section 326, Section 354, Section 376 [8], and section 498A’ [9]. Examining

the constitutional legitimacy of I.P.C. Sec. 498 A. If a wife has a statute to protect

herself from her husband's cruelty, why doesn't a husband have one?

Presently the equilibrium of scale has shifted towards women [10].

The Criminal Law Amendment Act of 1983 (Chapter XX-A) [11] included a

segment 498A (Chapter XX-A). This precaution, however, has evolved into a just

and multifunctional blade against man. According to the Malimath Committee

Report [12], when a grievance or FIR is filed in 498A of IPC, it provides a simple

instrument to police to capture, otherwise take steps for capturing man and his

family members termed in the complaint devoid of inherent worth of the accusations

or conducting an initial investigation [13].

In 237th report on 'compounding of IPC offences,' the Law Commission of India

strongly suggested that section 498A, be made compoundable with the court's

consent. This isn't for first time the organization has made a suggestion like this; it

did so in its 154th and 177th reports, respectively, in 1996 and 2001, Justice

Malimath Committee's report on ' Reform of Criminal justice System' also suggested

the same. There was a constant request to bring relaxation in the provision [14].

205

In renowned instance [15] the Apex Court expressed that such complaints were

made with sole object to harass the companion (husband) and offspring of spouse

and "allowing or permitting the complainant to pursue this grievance could be an

abuse of the method of regulation” and observed the complete renewal of section

498A of Indian Penal Code is required. A criminal complaint was recorded charging

that the wife attacked at Mumbai by all accused named in complaint and a demand

for a luxury vehicle was made.

Why do all the legal guidelines pertaining to such horrible crimes protect women but

not men? The only section which safeguard men’s modesty is 377of IPC.

The Domestic Violence Act, does not recognize male a victim/perpetrator of abuse

and violence, only accepts that females can be victim of violence. According to this

law, only a women can file a complaints as victim, against her husband and his

family members. In ‘Harjinder Kaur and others v. Territory of P&H,’ a grievance

had been recorded by wife u/s 498A against husband's relatives, including his 5

sisters who were the petitioners in the current criminal proceeding. The high court

observed the way that one of the sisters had been married and dwelling somewhere

else beginning around 1994, when his brother got married making their involvement

unlikely and went on to observe that “it appears that a wider net has been knitted so

as to rope in the present petitioners”. It is plainly noticeable that a man, who is a

survivor of aggressive behavior at home, has no privileges under this law.

Another fundamental flaw in this regulation is that it facilitates abuse, making it

difficult for women to resist impulse for "teaching a lesson" to their male companion

by filing frivolous and dishonest lawsuits. Reflecting degree of its misapplication,

the 'Crime in India 2012 Statistics'- NCRB,2012 Report takes note of that the pace

of charge-sheeting u/s 498A is pretty much as high as 93.6% while conviction rate is

just about as low as 15%.

A similar pattern is as of now being seen in the cases of ‘Anti-Dowry’ provisions

provided in section 304B ‘Dowry Death’ [16], which is being abused to the point

where the Supreme Court has had to intervene and named it "Legal Terrorism".

206

Section 498A [17] of the Indian Penal Code, otherwise called "anti-dowry

harassment law”. Further it clarifies ‘cruelty’, a dubious and vague term, to think

about even normal activities and action by the husband’s family as harassment for

dowry. The wife's complaint is enough to make a man arrest. Consequently trivial

conjugal disagreements added up as dowry harassment cases, as the risk of an arrest

gives seriously negotiating power to a women confronting dissolution of marriage.

There has been condemnation for such laws by legislators through the Law

Commission reports, from men's freedoms groups, some women' rights groups,

youngsters and children’s right groups, senior citizen groups and even human right

groups. The Supreme Court has named broad abuse of this law as "legal terrorism”

and has mentioned such outrageous observable facts over and again in a few of its

decisions, but surprisingly this law still operates in practice and not been demolished.

The answer is, because it serves the interests of the “bail industry” and the women in

civilization who suffered a lot and still suffer believing that they are the only victim

in society, irrespective of this law absurdity.

The legislators and legal framework doesn't safeguard men from these embarrassing

and shamming claims resulting in self-destruction as suicide. However, while

delivering land mark judgment in [18], the Apex Court decided "the cruelty is of two

kind one is mental and other is physical. It might be true that physical cruelty is

generally by husband being a strong one but at the same time this can’t be said to be

universally true. It is also vice versa in the case of mental cruelty. But in the

majority of cases of mental cruelty it is almost the wife who causes mental cruelty to

the husband."

Females may misinterpret a casual brush of the shoulder as eve-teasing/prodding,

and others may go so far as to purposefully startle a man that if he doesn't pay cash,

she will bring him to prison for something quite similar, according to social media

recordings. Some females have begun to use extortion in various forms with the goal

of receiving money. The primary goal of this provision was to create a society free

of family conflicts. Indeed, in divorce disputes, even an untrue statement of cruelty

can become a reason for cruelty. Different High Courts have labelled bogus

instances of polygamy against spouse [19] or in the Dowry Prohibition Act, 1961, as

207

well as the IPC [20] as cruelty. The intention, according to the Supreme Court, to

remove at the very underpinnings of the dowry risk. However, by abusing the

clause, novel form of legal terrorism has emerged.

The Hon'ble Apex Court [23] held in a case which challenge constitutionality of

provision was challenged has observed that New Legal Terrorism might release if

clause is abused. Is it possible that a legal provision has become synonymous with a

highly undesirable concept such as terrorism? It is a doubtful and debatable query

hence requires a clear inspection of socio-legal situation. In ‘arnesh kumar v state of

bihar’ 2014, SC directed to “police and magistrate that fair treatment and true

process to be followed before arrest and detainment and rules of D.K Basu ought to

be followed”. It decided that no automatic arrest and detainment be made in such

offense". Accordingly arrest under sec.498A raged down to 2.20lakh to 1.86lakh in

2015.

The observation of Hon'ble Apex Court that "Tragically countless of these false

complaints have overflowed the courts as well as led to social agitation and unrest

swaying harmony, peace, bliss and happiness of the society" [24], evidently shows

the regulation are producing outrageous dread in civilization.

Conclusion

With the modern development inside the socio-criminal system, the need to realize

the rights of the men in pursuance of all legal provision the affirmation and

acknowledgment of these rights and privileges has become unavoidable.

Another component of these laws being constantly violated is undeniable. According

to a report released by Delhi Commission of Women (DCW), "53.2 percent of the

rape cases registered in Delhi between April 2013 and July 2014 were judged to be

'false and fake' [25]."

Once more, Justice Verma Committee of 2012 had leaned toward “gender-neutral

laws totally in its report and the Criminal Law (Amendment) Ordinance 2013 was

published in the gazette of India which upheld the Committee's view. In the spirit of

impartial laws, inappropriate behavior, sexual harassment, voyeurism and stalking

208

were added to the Indian Penal Code and certain alterations and cancellations were

made to Indian Penal Code, Code of Criminal Procedure and Evidence Act. But the

ordinance on making all laws gender neutral lasted for 58 days and was repealed and

replaced by The Criminal law (Amendment) Act 2013 which again brought to light

gender specific laws”[26].

It's disheartening and sad that gender-neutrality is seen as a "response against

women's freedom" or a "backlash against feminism," when it is actually footstep

toward achieving equality for ending the never-ending clash amongst genders that

serves as a barrier for growth, per one class's rights trumping another's.

The Society needs gender-neutral laws immediately. Equality and equity must exists

in actual phrases, not just merely in minds or to papers. Gender neutrality is

intrinsically associated with maintainable events, sustainable development,

scholastic achievement and political strengthening. The need is to make the world a

better place for generations by eliminating all legal distinctions about violence. It

will be legal when all people are treated equally, and we will be able to achieve a

great future for both men and women in society.

As a result, the state has reached to a stage where gender neutral legislation are

required to protect the sanctity of Fundamental Rights.


1. 498a.org, http:// www.498a org/legaltorture. html, 20 November 2010.

2. Shukla, V N, and Mahendra P. Singh. V.n. Shukla's Constitution of India.

Lucknow: Eastern Book Co, 1990, edition13th (2017), art14‘equality before

law and equal protection of law’.

3. Art15 “Prohibition of discrimination on grounds of religion, race, caste, sex,

or place of birth”.

4. ibid.

5. Jain, Mahabir P. Indian Constitutional Law. Bombay: N.M. Tripathi, 1962,

article-39‘with equal pay for equal work’, article-42‘just and humane

conditions of work and maternity relief’.

209

6. Renouncing the practices derogatory to the dignity of women’.

7. Vijaylakshmi vs. Punjab University, AIR 2003 SC 3331.

8. (Punishment for obscene acts or words in public) Ratanlal Ranchhoddas.

Ratanlal & Dhirajlal's the Indian Penal Code (Act XLV of 1860). New Delhi

:Wadhwa & Co., 2007, Section 326 (Protection of women against Acid

Attacks), Section 354 (Outraging modesty of Women), Section 376

(Punishment for rape),

9. Section 498A’ [9] (Husband or relative of husband of a woman subjecting

her to cruelty).

10. Legal India ,Dhawesh Pahuja, “Cruelty against husband in India”

www.legalindia.in/cruelty-against-huband.

11. Relating to cruelty by husband or relatives of husbands to combat social evil

of dowry and matrimonial atrocities against married women, Act 46 of 1983.

12. Government of India, https://lawcommissionofindia.nic.in/reports/report

243.pdf

13. Government of India Law commission of India, https://lawcommission

ofindia.nic.in/reports/report237.

14. Dr Partha Partim Mitra, “A new look on matrimonial cruelty with criminal

law”, Indian Bar Review, Vol XL(1)2013, p.87.

15. Preeti Gupta Vs. Province of Jharkhand, AIR 2010 SC 3363.

16. Ratanlal Ranchhoddas. Ratanlal & Dhirajlal's the Indian Penal Code (Act

XLV of 1860). New Delhi :Wadhwa & Co., 2007.

17. Section 498A “Husband or relative of husband of a woman subjecting her to

cruelty– Whoever, being the husband or the relative of the husband of a

woman, subjects such woman to cruelty shall be punishable with

imprisonment for a term which may extend to three years and shall also be

liable to fine.

18. Dr. N. G. Dastane Vs. Mrs. S. Dastane, AIR 1975 SC 1534.

19. Raj Vs. Raj, AIR 1986 pat. 362.

20. Kalpana Vs. Surendra, AIR 1985 All 253.

21. Sushil Kumar Sharma Vs. Union of India, AIR 2005 SC 3100.

22. Ashok Kumar Vs. Vijay Laxmi, AIR 1992 Del. 182.

http://www.legalindia.in/cruelty-against-huband

210

23. Shushil Kumar Sharma vs. Association of India, AIR 2005 SC 3100.

24. Preeti Gupta Vs. State of Jharkand, AIR 2010 SC 3363.

25. India today, https://www.indiatoday.in/india/north/story/false-rape-cases-in-

delhi-delhi-commission-of-women-233222-2014-12-29.

26. Government of India, Ministry of Home Affairs, Committee on Reforms on

Criminal Justice System https://www.mha.gov.in/sites/default/files/criminal

_justice_system.pdf.

https://www.indiatoday.in/india/north/story/false-rape-cases-in-delhi-delhi-commission-of-women-233222-2014-12-29

https://www.indiatoday.in/india/north/story/false-rape-cases-in-delhi-delhi-commission-of-women-233222-2014-12-29

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