Sponsored by
2021
Jointly Organized by
Poornima College of
Engineering, Jaipur
and
Rajasthan Technical
University, Kota
in Association with
Soft Computing Research
Society
March 27-28, 2021
2nd International Conference on Artificial Intelligence: Advances and
Applications (ICAIAA 2021)
SOUVENIR
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TABLE OF CONTENTS
Chief Patron ..................................................................................................................... 6
Patron ............................................................................................................................... 6
General Chair ................................................................................................................... 6
Organising Chair .............................................................................................................. 6
Program Chair .................................................................................................................. 6
Publicity Committee ........................................................................................................ 6
Publication Committee ..................................................................................................... 7
Registration Chair ............................................................................................................ 7
Session Management Committee ..................................................................................... 7
Advisory Board ................................................................................................................ 7
Abstract of Accepted Papers .......................................................................................... 10
An Efficient Hids System Using Machine Learning Algorithm and Evidence Theory . 10
Self-supervised Learningfor COVID 19 – An Envision to Salvage Model ................... 10
Forecast of Covid Cases Using Deep Learning Algorithm ............................................ 10
Multi-Agent Intrusion Detection System using Sparse PSO K-Mean Clustering and Deep
Learning ......................................................................................................................... 11
Malware Classification based on Various Machine Learning Techniques .................... 11
Privacy Preserving Dynamic Task Scheduling For Autonomous Vehicles ................... 12
Artificial Intelligence enabled IoT Based Smart Blood Banking System...................... 12
Reliability enhancement in harmony with prudent coding for flight critical embedded
automatic control software ............................................................................................. 13
Multi-Location Faults in Transmission Lines: Detection and Classification................. 13
Detecting depressive online user behavior during global pandemic by fusing LSTM and
CNN Models .................................................................................................................. 14
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A Quick and Single-Ended Scheme for Fault Detection and Classification on
Transmission Line .......................................................................................................... 14
Simplifying And Optimizing The Convolution Encoding Algorithm In Error Control
Codes .............................................................................................................................. 14
Deep Model for Robust Tomato Disease Detection on Low-Resolution Leaf Images .. 15
A Novel Entropy-Based FCM Algorithm Using Inverse Fuzzy Membership Framework
and Uncertainty Measure for Segmentation of Brain MR Images ................................. 15
Radar Target Recognition And Classification Using Supervised Machine Learning
Appraoches ..................................................................................................................... 16
An Attention-based Medical NER in the Bengali Language ......................................... 16
Estimation of Reflection Coefficient of Quarter Circle Breakwater Using Artificial
Neural Network .............................................................................................................. 17
Semantic Similarity Extraction on Corpora Using Natural Language Processing
Techniques and Text Analytics Algorithms ................................................................... 17
Modeling and Simulation of Supply Chain System in Stochastic Environment: A Simple
Case Study for Periodic Review Policy using Python ................................................... 18
Graph based data analysis in Big Data Computing Environment: An investigation of
Flight Network Datasets ................................................................................................. 18
Introduction of PMI-SO Integrated with Predictive and Lexicon Based Features to Detect
Cyberbullying in Bangla Text Using Machine Learning ............................................... 19
Predicting Survivability in Oral Cancer (OC) Patients .................................................. 20
Particle Swarm Optimization with Weighted Extreme Learning Machine for Software
Change Prediction .......................................................................................................... 20
Application of Machine Learning for Heart Disease Prediction .................................... 21
A Divisive Hierarchical Clustering Algorithm to Find Clusters with Smaller Diameter to
Cardinality Ratio ............................................................................................................ 21
Flood Hazard Mapping of Kuttanaad Region, Kerala ................................................... 21
3
A conceptual framework based on conversational agents for the early detection of
cognitive impairment ..................................................................................................... 22
Multi Objectives for TCSC Placement using Self-Adaptive Firefly Algorithm ............ 22
Hybrid CNN – LSTM for Traffic Flow Forecasting ...................................................... 23
Navigation App for People with Disabilities Through Store Accessibility Assessment 23
Optimization of Fractional Order PID Controller(FOPID)Using Cuckoo Search ......... 24
Impact of Overall Service Quality and Technology Factors on Intention to Use the
Internet of Things (IoT) at Bescom ................................................................................ 24
Design of AMC based Metasurface Loaded Slot Antenna for Wideband RCS Reduction
and Gain Improvement ................................................................................................... 25
A Novel Hybrid ASO-NM Algorithm and Its Application to Automobile Cruise Control
System ............................................................................................................................ 25
On the use of Machine Learning for Soil Condition Monitoring .................................. 26
Forest Fire Damage and Recovery Assessment ............................................................. 26
A Method of Micro Pixel Similarity for Lung Cancer Diagnosis using Adaboost ........ 27
Application of hybrid of ACO-BP in Convolution Neural Network for effective
Classification .................................................................................................................. 27
Face Recognition And Mobile Location Data For Class Attendance Monitoring ......... 28
Early Epilepsy Seizure Prediction using CNN............................................................... 28
Transformer Deep Learning Model for Bangla-English Machine Translation .............. 29
Time Series analysis and Forecasting on crime data ..................................................... 29
Distributed Association Mining for discovering interesting rules for Tours and Travel
Company ........................................................................................................................ 30
Advanced identification of Alzheimer’s disease from brain MRI images using
Convolution Neural Network ......................................................................................... 30
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An application of OB-MFO for Optimal Bidding Strategy in Pay-as-bid auction
environment.................................................................................................................... 31
Wearable fall-detection using deep embedded clustering algorithm ............................. 31
Nacelle: Knowledge Graph-based Conversational AI for Skills Gap Analysis to Achieve
Sustainable Learning at Workplace ............................................................................... 32
Classification of driving behaviour using machine learning methods at signalized
intersections in urban and suburban roads ..................................................................... 32
IMAGEBOT: Imagination to Quotation ........................................................................ 33
Cataract detection using textural features and Machine learning algorithms ................ 33
A Granular Intuitionistic Fuzzy Meta Clustering Algorithm ......................................... 34
Performance Evaluation and Comparison of Optical Amplifiers in Non-Linear Effects
for WDM Long-Haul Transmission System .................................................................. 34
Estimation Of Wave Overtopping Discharge At Quarter Circle Breakwater Using Lssvm
........................................................................................................................................ 35
Forward and Backward Modelling of Wire and Arc Additive Manufacturing Process
using Multiple Adaptive Neuro-Fuzzy Inference System .............................................. 35
Wireless Sensor Networks Localization by Improved Whale Optimization Algorithm 36
Early Flood monitoring Using Intelligent System ......................................................... 36
An Enhanced DBA for Supporting Maximum User with Minimum Delay .................. 37
Prediction of the Geographical Origin of Soils Using Ultra-Performance Liquid
Chromatography (UPLC) Fingerprinting and K-Nearest Neighbor (K-NN) ................. 37
Flower Classification in Videos: A HOG-PCA-NN Method ......................................... 38
Building damage detection using Discrete Wavelet Transforms and Convolutional
Neural Networks ............................................................................................................ 38
On-device ML: An efficient approach to classify large number of images using multi-
threading in Android Devices. ....................................................................................... 39
A Systematic Study of Intelligent Face Scanning in Rare Disease Detection ............... 39
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Key Exchange Using Tree Parity Machines: A Survey ................................................. 40
Adaptive Exon Prediction using Maximum Error Normalized Algorithms .................. 40
A Novel Approach for wavelength Optimization in GPON Quad play......................... 41
LoRa Based Sensing Network Setup and IoT Integration for Smart Agricultural
Management ................................................................................................................... 41
Evaluation of Machine Learning Models for Sign Language Digit Recognition .......... 42
Parking Lot Occupancy Detection Using Hybrid Deep Learning CNN-LSTM Approach
........................................................................................................................................ 42
Chili leaf disease detection using texture features of image and classification by SVM
and KNN ........................................................................................................................ 43
Chronological Sine Cosine Algorithm Based Deep CNN for Acute Lymphocytic
Leukemia Detection ....................................................................................................... 43
Malarial Parasite Detection Based On Smartphone Microscopic Imaging Using Deep
Learning Approach......................................................................................................... 44
Linguistic Data Analysis using Nagel Point based Ranking Fuzzy Numbers for Financial
Risks Management ......................................................................................................... 44
A Dynamic Web Data Extraction From Srldc (Southern Regional Load Dispatch Centre)
And Feature Engineering Using Etl Tool....................................................................... 45
Unlocking the potential of Natural Language Processing and Healthchatbots in Health
care management ............................................................................................................ 45
Discrete Wavelet based Multi-classifier Approach for Recognition of Offline
Handwritten Hindi Numerals ......................................................................................... 46
Sentiment Analysis through Machine Learning: A Review .......................................... 46
RAFI: PARALLEL DYNAMIC TEST-SUITE REDUCTION FOR SOFTWARE ..... 46
Memetic spider monkey optimization for spam review detection problem ................... 47
Best Practices of Machine Learning Methods in the Field of Cybersecurity: A Review
........................................................................................................................................ 48
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Chief Patron
Prof. R. A. Gupta, Vice-Chancellor, Rajasthan Technical University,
Kota
Patron
Ar. Rahul Singhi, Director, Poornima Group, Jaipur
Prof. Dhirendra Mathur, RTU (ATU) TEQIP-III Coordinator
General Chair
Mahesh Bundele, Principal & Director, PCE, Jaipur
Mahendra Lalwani, Rajasthan Technical University, Kota, India
Nilanjan Dey, JIS University, Kolkata
Organising Chair
Harish Sharma, Rajasthan Technical University, Kota, India
Pankaj Dhemla, PCE, Jaipur, India
Kusum Kumari Bharti, IIITDM, Jabalpur, India
Program Chair
Garima Mathur, PCE, Jaipur, India
S. D. Purohit, Rajasthan Technical University, Kota, India
Prashant Singh Rana, Thapar University, Patiala, India
Publicity Committee
Surendra Yadav, PCE, Jaipur, India
Sandeep Kumar, CHRIST (Deemed to be University), Bangalore, India
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Deepak Bhatia, Rajasthan Technical University, Kota, India
Publication Committee
Irum Alvi, Rajasthan Technical University, Kota, India
Himanshu Mittal, Jaypee Institute of Information Technology, Noida,
India
Virendra Sangtani, PCE, Jaipur, India
Registration Chair
Tarun Mishra, PCE, Jaipur, India
Meenakshi Awasthi, AKGEC, Ghaziabad
M.L. Meena, Rajasthan Technical University, Kota, India
Anila Dhingra, PCE, Jaipur, India
Session Management Committee
Soniya Lalwani, BKIT, Kota, India
Nirmala Sharma, Rajasthan Technical University, Kota, India
Payal Bansal, PCE, Jaipur, India
Sanjay Bhargav, PCE, Jaipur, India
Advisory Board
A S. Sundaram, IISc Bangalore
SushmitaDas, NIT, Rourkela
Suneeta Agrawal, Motilal Nehru National Institute of Technology
Allahabad
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Kusum Deep, Indian Institute of Technology, Roorkee, India
Aruna Tiwari, Indian Institute of Technology Indore, India
Ashvini Chaturvedi, NIT Suratkal, India
Ayan Kumar Bandyopadhyay, CEERI, PILANI, India
Debasish Ghose, IISc Bangalore, India
Deepak Garg, Bennett University, India
Jagdish Chand Bansal, South Asian University, New Delhi
Prena Gaur, NSUT, Dwarka, New Delhi, India
Neetesh Purohit, IIIT Allahabad, India
R. P. Yadav, MNIT Jaipur, India
Vimal Bhatia, IIT Indore, India
Swagatam Das, Indian Statistical Institute, Kolkata, India
Preetam Kumar, IIT, Patna, India
Nishchal K. Verma, Indian Institute of Technology Kanpur, India
Atulya K. Nagar, Liverpool Hope University, UK
Sandeep Sancheti, SRM University, India
Kamran Iqbal, University of Arkansas at Little Rock, Little Rock,
Arkansas, United States
Mahfuzul H Huda, Saudi Electronic University
K. S. Nisar, Riyadh, Saudi Arabia
Dan Simon, Cleveland State University USA
Costin Badica, University of Craiova, Dolj, Romania
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Mohd Muntjir, Taif University, Kingdome of Saudia arabia
Aboul Ella Hassanien, Cairo University, Egypt
Nooritawati Md Tahir, University Technology MARA (UiTM), Malaysia
Rana Khudhair Abbas Ahmed, Alneelain University, Khartoum, Sudan
Abhishek Mukherji, AI Principal Research Scientist, San Francisco
Wan young chung, Pukyong National University Busan, South Korea
Marcin Paprzycki, Polish Academy of Sciences, Warsaw, Poland
Carlos E. Palau, ETSI Telecommunication, UPV, Camino de Vera, Spain
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Abstract of Accepted Papers
An Efficient Hids System Using Machine Learning
Algorithm and Evidence Theory
Surbhi Solanki, Chetan Gupta, Kalpana Rai and Minal
Saxena
Sagar Institute of Research Technology and Science Bhopal, India
Abstract. Today, the most rising trend in our society is Intrusion Detection System.
This simply monitor network traffic and will alert the network administrator of any
unusual activity. IDS System does their work by further looking for deviations of
normal activity or signatures of known attacks. While there are some disadvantages
of IDS such as high false alarm rate and low detection rate. In this paper a hybrid
IDS (HIDS) method based on support vector machine (SVM) and evidence theory
(ET) has been proposed as well various attack detection technique to minimize the
low false alarm rate and improve accuracy.
Self-supervised Learningfor COVID 19 – An Envision to
Salvage Model
Anjali Jivani, Hetal Bhavsar and Kshitij Gupte
The Maharaja Sayajirao University of Baroda, India
Abstract. This paper explores how the gravity of the Corona Virus Disease of 2019
(COVID-19) calamity can be appropriately handled considering the options in the
world of Artificial Intelligence (AI) and specifically Self-Supervised Learning.
Starting from the outbreak of the disease to the enormous outburst of its spread, the
detection and the appropriate treatment, the containment of the disease and the
subsequent prevention process, each is a classic case where AI can be implemented.
The discussion here is related to each and every aspect of COVID-19 and how a
researcher or scientist can at every stage try to develop an AI related application
wherein there would be some options and ideas to predict such an outbreak, suitably
restraint its spread and most importantly handle the massive task of treating the
patients and caring for the medical staff who would be at the highest risk of
contamination.
Forecast of Covid Cases Using Deep Learning Algorithm
Nidumolu Vijaya Anand and Gunturi Chandra Mouli
P V P Siddhartha Institute of Technology, India
Abstract. This paper is a result of a Deep Neural Network (DNN) trained to predict
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the growth of cases tested positive for COVID19 and this concept can be extended
to any disease capable of spreading on global scale. These predictions can enable
the governments to foresee the results based on different scales of lockdown applied
and give them proper insights based on which they can decide what percent of the
working population should present at work at any given time without the risk of
spreading of the virus rapidly which is essential to keep the economy up and
running. This paper we used a DNN model comprising of 5 layers out of which one
is the input another is the output layer with the rest 3 being the hidden layers. Here,
the aim is to achieve a DNN that can reliably forecast the possible number of total
cases for a week.
Multi-Agent Intrusion Detection System using Sparse
PSO K-Mean Clustering and Deep Learning
Tanushri Jain and Chetan Gupta
Sagar Institute of Research Technology & Science, Bhopal, India
Abstract. Multi-agent architectures have been successful in attaining considerable
attention among researchers. This is so, because of their demonstrated capabilities
such as autonomy, embedded intelligence, learning and self-growing knowledge-
base, high scalability, and fault tolerance. These characteristics have made this
technology a de facto standard for developing ambient security systems to meet the
open and dynamic nature of today’s online resources. Although multi-agent
architectures are increasingly studied in the area of computer security, there is still
not enough empirical evidence on their performance in intrusions and attacks
detection. In this paper deep learning-based multi-agent architecture is proposed
which can identify and generate an alarm at the protocol level. The result analysis
shows enhancement over existing work.
Malware Classification based on Various Machine
Learning Techniques
Vinay Gautam
Chitkara University, India
Abstract. Malware is an executable file which is stored on the target computer and
which when executed might harm the target computer. It has been acknowledged that
there is a drastic growth in the volume of malicious software in recent years which
compromises the digital security of individuals, financial institutions, businesses and
government firms. The malware is classified into nine different families. The aim of
this paper is to identify class of malware as per given convention. This problem
belongs to a multiclass classification problem and our objective is to minimize the
multiclass log-loss error and to predict the probability estimates for each class for a
given file in order to make sure of the fact in which class the file belongs. The proposed
classifier produced a log-loss of 0.031% on the Microsoft dataset which was divided
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randomly into three parts train, cross- validation, and test.
Privacy Preserving Dynamic Task Scheduling For
Autonomous Vehicles
Muthurajkumar S, Ajay Karthikeyan C, Pradeep K and
Hariharan A
Anna University, India
Abstract. To improve the transportation system and to make it effective, the ultimate
resolution was to distribute the self-driving vehicles between multiple users. During
unwanted times, without any human efforts, autonomous vehicle owners distribute
their vehicles to other users. But it requires the user’s places, locations and route
information being published, which raises severe privacy issues. In this project we
have developed a privacy preserving dynamic scheduling system for continuous
sharing of self-driving vehicles. Initially, we find the attainable user for each of the
Autonomous Vehicles (AV) by designing a matching scheme. Then, we developed a
scheme using different ways of assigning the requesters was implemented to the AV
on variable system attributes. More conscientiously, using a set of IDs or Intermediate
Destination locations, our scheme enables a semi-trusted matching server to map the
requesters and the owners. Also, the provider and the requester can distribute their
specification of the trip and route, if the service can be given to the requester
effectively. All of the calculations for the verification of availability of the given
service is purely done in the untrusted server. And finally, the different scheduling
schemes are evaluated based on their effectiveness for this system.
Artificial Intelligence enabled IoT Based Smart Blood
Banking System
Muthu Kumaran E1, Velmurugan K2, Venkumar P2,
Amutha Guka D2 and Divya V3
1Dr.B.R. Amedkar Institute of Technology, Port Blair, Andaman &
Nicobar Islands-744103, India
2Kalasalingam Academy of Research and Education, Virudhunagr,
Tamilnadu, 626126, India. 3Pondicherry University, Port Blair Campus, Andaman & Nicobar
Islands-744103, India
Abstract. The purpose of this research is to help people who are in need of life-saving
blood at the right time by using current technologies. A complete database of real-time
blood transfusions has been developed in this research. The life-saving tool for a
normal human being has been considered and developed with immediate access to the
required blood using Artificial Intelligence (AI) and the Internet of Things (IoT). The
main objective of this research is the customization of the blood storage refrigerator
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and the ultra-freezer for plasma component storage compatible with IoT application
to improve the availability of various blood products in a timely manner and to reduce
the wastage of blood and its components. Real-time status of availability of each blood
component in each blood bank, including packing date using IoT enabled technology.
Further, the contact details of donors and their willingness to donate blood are
available in the database. Real-time GPS monitoring of potential donors can help track
the availability of donors around the needy area, and an Artificial Intelligence-enabled
algorithm for automatically contacting blood donors in a hospital/place is needed.
Reliability enhancement in harmony with prudent coding
for flight critical embedded automatic control software
Shobha S. Prabhu1 and H.L. Shashirekha2
1Gas Turbine Research Establishment, DRDO, Bangalore
2Department of Computer Science, Mangalore University, Mangalore
Abstract. Critical embedded control system along with its real-time software requires
high reliability in its design, development and maintenance. Failure in any critical
software contributes to risks in system safety and creates hazards. Reliability is a major
component of performance evaluator and it is inversely proportional to the defects at
every stage of development. Hence, identification of defects or faults proactively
which create these hazards is an important aspect while designing and developing any
critical system/software. Coding phase of Software Development Life Cycle (SDLC)
requires attention in every aspect to produce reliable software. This process of
augmenting quality through improved reliability into software code starts from the
design phase and continues up to maintenance phase. In this paper, significant coding
attributes which play vital role in the evaluation of reliability are studied, analysed and
improved to build enhanced reliability, safety and efficiency in airborne critical
embedded automatic control software. Even though there are direct measures of
reliability, indirect measures which govern the reliability are considered for the study
to manifest the influence of prudent coding.
Multi-Location Faults in Transmission Lines: Detection
and Classification
Gaurav Kapoor
Modi Institute of Technology, Kota, India
Abstract. Achieving the fault detection (FD) phase type classification (PTC) of multi-
location faults quickly in transmission line is a very complex job. For this issue, the
probable FD and PTC are verified with the presented single ended scheme in this work.
In the illustrated work, Fourier transform (FT) is applied to the current signals as a
feature extraction method, and thereafter, wavelet transform (WT) is used for detecting
faults. MATLAB is used for generating fault current data. A rapid FD and PTC task
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can be realized using the illustrated scheme according to the acquired consequences.
Detecting depressive online user behavior during global
pandemic by fusing LSTM and CNN Models
Bhuvaneswari Anbalagan and Jayanthi R R
Vellore Institute of Technology, Chennai Campus, India
Abstract. Online social media provide benign choice for online users to discuss about
psychological issues like depression which they prefer to share in Twitter, Facebook
platforms. In specific, during lockdown situations due to Covid-19, most of the people
isolated from societal interaction left untreated might lead to uncertain mental
conditions. Due of the stigma attached to mental illness many people undergo
depressive state and vent out in social media. In this paper, a fusion of Long Short
Term Memory (LSTM) and Convolutional Neural Networks (CNN) models are
applied on non-probability samplings of twitter data collected during lockdown
situations to detect the depressiveness condition. The dynamic chatbot is developed
using Natural Language Processing (NLP) to recover the similar depressive online
users. Moreover, the experiments demonstrate the fusing model selector choose the
deep learning techniques to predict the user behavior with high accuracy.
A Quick and Single-Ended Scheme for Fault Detection
and Classification on Transmission Line
Gaurav Kapoor
Modi Institute of Technology, Kota, India
Abstract. A fast approach for fault detection and phase type classification on a
transmission line is presented in this article. The proposed approach takes advantage
of DFT (discrete Fourier transform) and DWT (discrete wavelet transform) and makes
use of currents only at particular relaying end. Haar wavelet is used in the DWT. The
effects attained show that the approach is rapid and flawless as well.
Simplifying And Optimizing The Convolution Encoding
Algorithm In Error Control Codes
Constance Amannah
Ignatius Ajuru University, Nigeria
Abstract. Specifically, the study investigated most closely the convolution code
encoding algorithm (CCEA), simplified the CCEA, optimized the CCEA, designed
the simplified and optimized CCEA (SOCCEA), and evaluated the performance of the
SOCCEA. The SOCCEA has five critical steps which could be repeated until the least
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significant bit (LSB) in the input bits is moved into the shift registers (SR). The input
sequence is moved through the SR one at a time leading to the flushing of the last shift
in the register in an instance of bit movement within the register. The movement of the
input bits through the registers is from the most significant bit (MSB) to the LSB. N
modulo-2 adder (on the all the registers) is applied to achieve the leftmost bit (LMB)
of the output while the Boolean XOR logic operation is required to obtain the
rightmost bit (RMB).
Deep Model for Robust Tomato Disease Detection on
Low-Resolution Leaf Images
Siddhant Baldota, Rubal Sharma, Nimisha Khaitan and
Poovammal E
SRM Institute of Science and Technology, India
Abstract. Traditionally, diseases in plants have been identified through the naked eye.
However, this process is tedious and time consuming. The application of deep
convolutional neural networks in disease detection has helped immensely in the
process. The work is performed on the benchmark PlantVillage dataset consisting of
16,065 tomato images distributed among 10 classes. After preprocessing our dataset,
we subjected it to augmentation and balanced the data using class weights. We
implemented transfer learning on deep convolutional neural networks like Visual
Geometry Group-19 (VGG-19), Xception and residual networks (ResNets) ,
pretrained on ImageNet weights. We chose ResNet101 as our final baseline
architecture because of its high accuracy, lesser training time and higher stability in
comparison to other models. Using this model, we achieved near human-level
performance with an accuracy of 99.34.
A Novel Entropy-Based FCM Algorithm Using Inverse
Fuzzy Membership Framework and Uncertainty Measure
for Segmentation of Brain MR Images
Madhumita Ray1, Nabanita Mahata2 and Jamuna Kanta
Sing2
1Greater Kolkata College of Engineering and Management, India
2Jadavpur University, India
Abstract. Segmentation of human brain images is extremely significant and obvious
tread of brain image scanning and diagnosis. In addition, magnetic resonance imaging
(MRI) is affected by noise and inhomogeneity due to improper image acquisition
devices causes blurry tissue boundaries. So, MR imaging segmentation is very
complicated and remarkable task. Here, we propose a new entropy related fuzzy c-
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means (ebFCM) algorithm using an inverse fuzzy membership framework in
association with Gaussian distribution function. It also integrates the fuzzy
membership function with a local uncertainty factor. These two terms are combined
with two complementing influencing factors. Finally, we use Shannon entropy
function by means of uncertainty value describing the underline total uncertainty. We
compare the effectiveness of the algorithm with some FCM related algorithms on brain
MR image data and find that it yields superior results.
Radar Target Recognition And Classification Using
Supervised Machine Learning Appraoches
Jagan Mohana Rao Pathina and Rajesh Kumar P
Andhra University College of Engineering, India
Abstract. Target classification from the returned echo signals is one of the challenging
prob-lems in the modern RADAR systems. The key feature that is used for the target
classification is the Radar Cross Section (RCS). The recent advancements in the field
of machine learning techniques gave interesting results for the RADAR tar-get
recognition. A dedicated machine learning models are realized to recognize simple
and complex targets. The models corresponding to both simple and complex target
recognition are developed with a capability to identify four common geometrical
structures namely circular cylinder, frustum (truncated cone), circular disc and sphere.
The proposed method extracts features of simple targets by using Maximal Overlap
Discrete Wavelet Packet Transform (MODWPT). The un-known targets are classified
with the feature extraction set obtained using different supervised classifiers namely
k-Nearest Neighbor (k-NN), Support Vector Ma-chine (SVM), Artificial Neural
Network (ANN) and their performance is com-pared. The k-NN classifier gives better
performance of classification accuracy when compared to existing methods.
An Attention-based Medical NER in the Bengali
Language
Tanvir Islam, Sakila Mahbin Zinat, Shamima Sukhi,
Zakir Hossain Zamil, Aynur Nahar and M. F. Mridha
Bangladesh University of Business and Technology (BUBT), Bangladesh
Abstract. Medical Named Entity Recognition is a process where medical entities are
identified for extracting keywords in particular tasks in the medical sector such as
summarizing prescriptions, identifying diseases, etc. NER can make a context more
comfortable to understand by identifying entities in the context. In the Bengali
language, there is no artificial work that can identify automatically which kind of
medical specialist a patient needs to consult based on patients’ problems and
symptoms. In this paper, NER has been selected and proposed an attention-based
BiLSTM-CRF model for the task of telemedicine consultancy where the patient tells
their problems, symptoms, and diseases at the first attempt, both the consultant and
17
patient need to understand which specialist the patient needs according to the problems
or symptoms. This task has been implemented based on a self-made medical dataset
in the Bengali language which gives an F1 score of 95.6% accuracy level and performs
more efficiently in this task.
Estimation of Reflection Coefficient of Quarter Circle
Breakwater Using Artificial Neural Network
Shankara Krishna A, Vishwanath Mane and Subba Rao
National Institute of Technology Karnataka, Surathkal, India
Abstract. In this present study Reflection co-efficient of a Quarter circle breakwater
(QCB) with various S/D ratios (spacing to diameter ratio) and perforation are predicted
by Artificial Neural Network (ANN) using MATLAB. Data collected from the
laboratory investigation conducted in the Marine Structures Lab of the Department of
Water Resources and Ocean Engineering, NITK Surathkal, by Binumol (2017) is used
in the present study. The collected data is divided into 2 sets for testing and training of
the ANN model. Incident wave steepness (H/gT2), relative water depth (d/hs) are
considered as input parameters and the Reflection coefficient (Kr) is the output
parameter to create the ANN model. The performance of created ANN model is
assessed by using various statistical parameters such as Root mean square (RMSE),
Nash-Sutcliffe Efficiency (NSE), Correlation coefficient (CC), and Scatter Index (SI).
Semantic Similarity Extraction on Corpora Using Natural
Language Processing Techniques and Text Analytics
Algorithms
Nisha Varghese and Punithavalli M
BHARATHIAR UNIVERSITY, India
Abstract. Extraction of Semantic Similarity and relevant information from the corpus
is one of the elusive tasks in Text Mining due to the unstructured data, uneven pattern,
multiple resolutions, concealed meaning and other ambiguities. The main focus of
semantic similarity analysis lies in meaning with respect to the word sense that lies in
the arrangements of position, subject, context and occurrence of other words in the
sentence. One of the hurdles to extract the exact semantic similarity from paraphrase
statements is the corpus length. The longer corpus has the better chance to match any
query statement and it may contain more words, which arises the over penalization
problem. To alleviate this problem avoid over penalization by length normalization.
The objective of the study is to improve the efficiency in capturing semantic similarity
and pertinent information by increased term frequency saturation and increased impact
of document normalization with the less penalization method. This study introduced a
novel method, Perfect Matching Algorithm (PMA), developed to reduce the over
penalization on context corpus with taken into account, on the length of both Query
18
and Context Documents by the length normalization. The first phase is the Text
Normalization, which includes Tokeni-zation, special characters and stop words
removal Lemmatization, and Named Entity Recognition. The second phase is the
information retrieval with lexical analysis and finally the semantic similarity
extraction. The experimental results exhibit PMA achieves the accuracy and improved
efficiency in semantic extraction by pivot length normalization.
Modeling and Simulation of Supply Chain System in
Stochastic Environment: A Simple Case Study for
Periodic Review Policy using Python
Arun Kumar Mishra
University College of Engineering and Technology (Ucet), VBU,
Hazaribag, India
Abstract. In today’s business environment, global competition has heightened
companies’ competitive struggle to survive and prosper. Consequently, companies are
trying vigorously to produce the desired product in the required quantity at the right
time with minimum cost by managing their supply chains in an integrated manner.
Modeling and Simulation technology has emerged as a new tool in supply chain
management and its basic strength is in evaluating system variation and inter-
dependencies. This key feature allows a decision maker to evaluate changes in the
segments of supply chain and visualize the impact of these changes have on the
performance of the entire supply chain. Further, the features available in Python
programming language make it very simple to apply various machine learning
algorithms to analyze and visualize the data for the ‘what-if’ analysis. This paper aims
at visualizing the impact of changing the review period for the order placement at
retailer level through simulation of Supply Chain (SC) systems in a stochastic
environment in a very general framework. The SC systems have been compared on
the basis of average inventory, average backlog and no. of stock-out situations at the
retailer level. An effort has also been made to examine the impact of variability in
customer demands by changing the review period.
Graph based data analysis in Big Data Computing
Environment: An investigation of Flight Network
Datasets
Naishadh Mehta1, Anand Ruparelia1, Jaiprakash Verma1
and Manoj Kumar Khinchi2
1Institute of Technology, Nirma University, Ahmedabad, India
2Mody University, Lachhmangarh, India
19
Abstract. The airline industry has always been a cornerstone in growing economies of
the world and also stands to be the most efficient mode of transport for many decades.
Being such a prime industry, it requires vital operational analytics for productive
decision making, which is directly related to the revenue generation and being a data-
driven industry, Big Data analytics and especially graph-based analytics turn out to be
the appropriate match for generating actionable insights from airline network data. The
research work here, applies algorithms such as the PageRank algorithm and Label
Propagation algorithm to the flight network data. The results generated, are helpful in
achieving business objectives such as discovering the most influential airports and
finding airport communities amongst the airline network that ultimately leads to
effective flight route planning.
Introduction of PMI-SO Integrated with Predictive and
Lexicon Based Features to Detect Cyberbullying in
Bangla Text Using Machine Learning
Md. Tofael Ahmed1, Maqsudur Rahman2, Shafayet Nur2,
Dr. Azm Islam3 and Dipankar Das4
1Department of Information and Communication Technology, Comilla
University, Bangladesh
2Department of Computer Science and Engineering, Port City
International University, Bangladesh.
3Department of Electrical & Electronics Engineering, University of
Rajshahi, Bangladesh.
4Department of Information and Communication Engineering, University
of Rajshahi, Bangladesh
Abstract. The increasing use of social media is causing a huge escalation in
cyberbullying. Cyberbullying causes significant emotional and psychological distress.
Previous research has shown good accuracy in detecting cyberbullying from textual
data. In this research, we introduced PMI-SO to develop a feature-based model which
detects cyberbullying in Bangla text with remarkable accuracy. The developed model
utilizes PMI-SO as a new input feature along with other predictive and lexicon-based
features to detect cyberbullying using Machine Learning classifiers. We created two
datasets in order to conduct this research. The Social Media Dataset contained 5000
Bangla texts and the PMI Dataset contained 10277 Bangla texts. We used the PMI
dataset to generate PMI-SO for the Social Media Dataset. The Social Media Dataset
was used to perform classification. Performance analysis revealed that XGBoost
classifier was able to classify texts with an accuracy score of 93%. The lowest accuracy
was 85% and it was obtained by SVM. We also developed a web page, which takes a
Bangla text, its likes and reply count as input and predicts cyberbullying in that text.
20
Predicting Survivability in Oral Cancer (OC) Patients
Diksha Sharma1, Neelam Goel1 and Vivek Kumar Garg2
1Panjab University, Chandigarh
2Punjab Biotechnology Incubator, Mohali, India
Abstract. Background: The present article is an attempt to review the important
advances and recent developments made for the survivability of patients with oral
cancer (OC). Cancer of the oral cavity is more prevalent in countries, where the
population is addicted to chewing betel nut, tobacco, and maintains poor oral hygiene.
Method: A systematic search of the literature was performed using the databases of
different sources. All studies which had investigated the survivability of oral cancer
patients during the period from 2005-2020 were retrieved. For detecting the overall
survival of patients, the most often performed technique is machine learning.
Conclusion: After reviewing the papers, it has been found that the prognosis of Oral
Cancer (OC) remains poor. It is important to identify and address the structural and
social determinants of oral cancer. Without detailed knowledge of these factors, the
outcomes of prevention and detection of diseases are ineffective. Early detection and
diagnosis increase survival rates and reduces morbidity. Raising public awareness of
oral cancer may also help in early diagnosis. Machine learning is mostly in use for
predicting the survival of patients by using different techniques to improve prediction
accuracy.
Particle Swarm Optimization with Weighted Extreme
Learning Machine for Software Change Prediction
Ruchika Malhotra, Deepti Aggarwal and Priya Garg
Delhi Technological University, India
Abstract. Software Change Prediction (SCP) is a branch of research that reduces
maintenance efforts by predicting change-prone classes prior to the software re-lease.
The past SCP studies have highly motivated the use of techniques to handle the two
major issues in SCP datasets, i.e., feature representation and imbalance handling. This
study proposes a novel combination of particle swarm optimization for feature
selection and Weighted extreme learning machine for imbalance handling and
classification to tackle these issues. The experiment is conducted on 6 Java datasets
that have been collected using tools and open-source repository. This study uses AUC
and F-measure as the performance measure with Friedman and Wilcoxon statistical
tests to evaluate and compare the results of the proposed model PSOWE with ten state-
of-the-art techniques. PSOWE achieved an AUC-ROC median value of 0.9337 and an
F-Measure median value of 0.6883 which prove the superiority of the proposed model
with the majority of the techniques.
21
Application of Machine Learning for Heart Disease
Prediction
Mohsin Qureshi and Nilima Warke
VESIT, India
Abstract. Cardiovascular Diseases (CVD) have become one of the leading causes of
the increase in mortality rate globally. A vast majority of this suffers from heart
diseases which can be diagnosed and treated effectively if predicted before time.
Modern Healthcare Systems make use of medical instruments that can give high-
resolution reports in a very short time frame. The problem associated with these
instruments is the data size. Machine learning (ML) was introduced to extract vital
information from this enormous data produced by Healthcare systems. This paper aims
in providing novel methods in implementing different machine learning algorithms
that can analyze the data and give reliable decisions. Using these reports/predictions
as supportive information can reduce the time needed for treatment and enhance the
efficiency of the overall healthcare system.
A Divisive Hierarchical Clustering Algorithm to Find
Clusters with Smaller Diameter to Cardinality Ratio
Sadman Sadeed Omee and Md. Saidur Rahman
Bangladesh University of Engineering and Technology, Bangladesh
Abstract. Given a point set $S$ of $n$ points on a $d$-dimensional space and a
positive integer $k$, we are asked to split $S$ into $k$ clusters such that the maximum
diameter to cardinality ratio among all clusters is minimized. In this paper we give a
divisive hierarchical clustering algorithm for finding such clusters which uses two
different greedy heuristics at each iteration. We compare the performance of our
algorithm with that of some well-known clustering algorithms including the widely
used $k$-means clustering algorithm using three similarity metrics and find some
cases where our algorithm performs better than $k$-means clustering. We also test our
algorithm on different benchmark datasets where the ``ground truth" labels are known
and show that our algorithm outperforms other clustering algorithms in almost every
case. We also perform experiments with increasing value of $k$ on another benchmark
dataset and show that our algorithm performs better than other clustering algorithms.
Flood Hazard Mapping of Kuttanaad Region, Kerala
Jayati Vijaywargiya and Rama Rao Nidamanuri
Department of Earth and Space Sciences, Indian Institute of Space
Science and Technology, Kerala, India
Abstract. This work presents the index-based approach considering multiple criteria
22
to assess the flood hazard areas of Kuttanaad region, Kerala, India. In this work, seven
parameters were considered to derive the Flood Hazard Index over the spatial region.
The physical parameters that were taken into account are elevation, distance from
drainage network, rainfall intensity, land use, geology and flow accumulation. For
calculating Flood Hazard Index relative weight is given to each of these physical
parameters. These weight values are calculated using Analytical Hierarchical Process.
The presented methodology was applied on Kuttanaad region of Kerala state, India. It
is extended over the latitudinal range of 9.248869 Degree North to 9.791042 Degree
North and longitudinal range of 76.328700 Degree East to 76.604953 Degree East. It
accounts for an approximate area of 121866 hectares. Kuttanaad is a low-lying area in
the west coast of India which consists of 79 villages spread across the districts of
Alleppey and Kottayam. Major part of this region lies below the main sea level and
thus this region is very susceptible to flood. Flood Hazard mapping is used to
determine the areas in order of their vulnerability to flooding.
A conceptual framework based on conversational agents
for the early detection of cognitive impairment
Moises Ruben Pacheco Lorenzo, Sonia Maria Valladares
Rodriguez, Luis Eulogio Anido Rifon and Manuel Jose
Fernandez Iglesias
AtlanTTic, Universidade de Vigo, Spain
Abstract. Within the aging society in which we currently live, it is important to provide
solutions to the emerging social and health problems. In this work we propose a
conceptual framework for an AI-assisted conversational agent that will be able to
provide elderly people a validated early detection of cognitive impairment,
implemented with widespread commercial smart speakers. Thereby, we aim to take
another step towards achieving the concept of healthy lifestyle.
Multi Objectives for TCSC Placement using Self-Adaptive
Firefly Algorithm
Selvarau Ranganathan, Palanivel Panjamoorthy and
Ellappan Venugopal
Adama Science and technology University, Ethiopia
Abstract. This paper examines multi objectives for power system performance
improvement through placement of Thyristor Controlled Series Compensator (TCSC)
with the application of Self-Adaptive Firefly Algorithm (SAFA). The SAFA selects
the best positions and parameters for TCSC placement. Three single objectives of Real
Power Loss (Ploss) minimization, improvement of Voltage Profile (VP), enhancement
of Voltage Stability (VS) and one multi objective of Ploss, minimization,
simultaneously improve the VP besides enhancing the VS are considered. The
23
proposed SAFA approach is performed on IEEE 30 bus system and the simulation
solutions are conferred to validate the effectiveness of proposed SAFA.
Hybrid CNN – LSTM for Traffic Flow Forecasting
Rajalakshmi V and Ganesh Vaidyanathan S
Sri Venkateswara College of Engineering, India
Abstract. The accurate forecast of traffic flow is a crucial need for intelligent
transporation systems (ITS). This supports dynamic and proactive traffic control
management. The challenging part lies in reducing the forecast error rate. There was
limited success in the previous attempts put forth to develop traffic flow forecasting
systems. This paper proposed hybrid CNN-LSTM model to predict the traffic flow for
MIDAS Site – UK Highways data. The data is pre-processed using Z-Score
Normalization. The CNN model is used to efficiently to extract the features from the
pre-processed data. These extracted features are fed as input to the LSTM network to
forecast the traffic flow. CNN, LSTM and hybrid CNN – LSTM models are trained
and tested for estimating traffic flow 15 min into the future. The results convey that
the pro-posed hybrid CNN-LSTM model forecasts the traffic flow with reduced error
rate.
Navigation App for People with Disabilities Through
Store Accessibility Assessment
Christine Guo1, Lawrence Han2, Vicky Tang3 and Hao
Tang4
1The Pingry School, United States
2Ridge School, United States
3Westfield School, United States
4City University of New York, United States
Abstract. Sensory problems that affect muscles, movement, and balance lead to motor
disability, and the deterioration of our eyes' capacity to interpret visual details results
in a state of visual disability. Thus, the goal of this work is to help those with dis-
abilities to travel independently through an app. The app provides the accessibility
details of stores in a friendly manner so that people can securely navigate around the
environment. Overall, our principal purpose is to access the store accessibility level
using deep learning, combined with our proposed app to promote a high quality of life
for those who have any form of incapacity. With the proposed app in place, we hope
to motivate those with visual and motor disabilities to express autonomy and
individualism.
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Optimization of Fractional Order PID
Controller(FOPID)Using Cuckoo Search
Tarun Varshney1, Vikassingh Bhadoria1, Pravin
Sonwane2 and Nitin Singh3
1ABES Engineering College, Ghaziabad, India
2Poornima College of Engineering, Jaipur, India
3MNNIT, Allahabad, UP, India
Abstract. This paper shows the detailed study on optimization of fractional-order PID
Controller (FOPID) for fractional order estimated non linearized dynamical thermal
system. Initially, parameters of integer-order PID (IOPID) controller have been
optimized and then keeping those optimized values of gains the same, exponents of
FOPID controller have been optimized and finally gains and exponents of FOPID
controller have been optimized. The performance of both IOPDS and FOPIDs
controllers are compared for most popular conventional Nelder–Mead’s, Integer point
algorithms and nature inspired Cuckoo Search (CS) optimization algorithms.
Simulation results proclaim the effectiveness and efficiency of the FOPID Controllers
with CS optimization algorithm in terms of Mean Square Error (MSE).
Impact of Overall Service Quality and Technology
Factors on Intention to Use the Internet of Things (IoT) at
Bescom
Kavitha Desai1, Mahalakshmi S2, Sivaretinamohan R1
and Macherla Bhagyalakshmi1
1CHRIST (Deemed to be University), Bengaluru, India
2Jain Deemed to be University, India
Abstract. The reason for opting the Internet of Things in Power Distribution
management was to minimize the existing distribution losses and to distribute the
available power optimally. This will be achieved by the inception of Smart Grids and
au-tomating the existing Distribution network. The IoT enabled Smart Grid enables
the utilities for real-time monitoring, control of the distribution network, reduce fault
detection, isolation time by automating the distribution network, outage management,
reduce pilferage by relying on demand response, real-time pricing, overcome the
metering inefficiencies through Advanced Metering Infrastructure, reliable
information transmission and smart information processing for facilitating better
decision capabilities. The bi-directional communication helps in better power
management as there is both consumer and utility participation. For the consumers, it
25
gives real-time energy usage statistics, reliable and improved power quality, lesser
outages and encourages consumers to use power wisely by keep-ing them informed
about peak hour pricing. Overall, it improves the operational efficiency and provides
better quality of service. Thus, BESCOM thought of IoT enabled Smart Grid for
efficient Power Distribution management
Design of AMC based Metasurface Loaded Slot Antenna
for Wideband RCS Reduction and Gain Improvement
Ankit Sharma1, Animesh Chandra1, Deepak Kumar1,
Himanshu Prajapat1, Madan Kumar Sharma2, Hridesh
Kumar Verma2 and Aniket Chauhan1
1Galgotias College of Engineering and Technology, India
2Rajasthan Technical University, Kota, India
Abstract. In this work, an improvement in radiation and scattering characteristics of
the slot antenna is achieved by using metasurface. To obtain wideband RCS reduction,
an artificial magnetic conductor (AMC) based metasurface is proposed. The proposed
metasurface consists of the design of two different AMC unit cells such that the
designed unit cells must have 180o ± 30o reflection phase difference in wideband. The
array of both the AMC unit cells are arranged in two configurations: checker-board,
and pyramidal for RCS reduction in wideband. Further, the proposed AMC
metasurface is loaded on the split ring resonator (SRR) inspired slot antenna. The
measured results of the metasurface loaded antenna show that impedance bandwidth
of the antenna is 9.9 to 10.5 GHz and peak gain of the proposed antenna is increased
by 1.61 dB as compared to slot antenna. The designed antenna achieves an average
RCS reduction of 5.2 dB within the band of 6.5 to 14.3 GHz while 10-dB RCS
reduction bandwidth of 30 % is attained related to reference slot antenna. The peak in-
band RCS reduction of the proposed antenna is 27.9 dB at 10.3 GHz. The overall
performance of the slot antenna is improved by the implementation of AMC
metasurface.
A Novel Hybrid ASO-NM Algorithm and Its Application
to Automobile Cruise Control System
Davut Izci and Serdar Ekinci
Batman University, Turkey
Abstract. A novel hybrid algorithm developed by merging atom search optimization
(ASO) and Nelder-Mead (NM) simplex search algorithms is presented. The proposed
improved algorithm (ASO-NM) is the first reported work on combining ASO and NM
method for optimization problems. Combination of ASO and NM leads to construction
of a desired metaheuristic approach that has a balanced exploration and exploitation.
The proposed hybrid ASO-NM was used for optimizing a proportional-integral-
26
derivative controller design for automobile cruise control system as well as testing
four well-known classical benchmark functions for the first time. The obtained
statistical and transient response analyses and comparisons have shown better
capability of the proposed hybrid ASO-NM algorithm which can be used for further
optimization problems as an effective approach.
On the use of Machine Learning for Soil Condition
Monitoring
Vikash Rameshar, Wesley Doorsamy and Babu Paul
University of Johannesburg, South Africa
Abstract. The sustainability of farming has come under tremendous pressure with
growing demand, constrained resources, and climate change. Some of the key factors
affecting large-scale farming and small-scale farming include soil fertility and
maintaining the condition of soil for optimum growth. Two-thirds of the developing
world’s rural people live in small farm households. Many of these small farm
households are poor and have limited access to services, but their farmland produces
food for a substantial proportion of the world’s population. Due to the lack of services,
these small-scale farmers cannot replenish their soil to produce optimum crop yield.
Services such as soil condition monitoring are crucial for a small-scale farmer as it
would assist the farmer in curbing crop disease and parasites that add to the
degradation of the soil and evidently affecting the crop harvest. This research is
essentially aimed towards helping small-scale farmers make informed decisions about
nutrients, correct soil pH to crop planting and soil type. This paper analyses data
science techniques within agriculture that could potentially assist in the development
of assistive technology that will assist in small-scale farming practices. A review of
data analytics in agriculture is presented together with a case study that utilizes
unsupervised learning to automatically distinguish soil conditions.
Forest Fire Damage and Recovery Assessment
Jayati Vijaywargiya and Rama Rao Nidamanuri
Indian Institute of Space Science and Technology, India
Abstract. Forest fire has been a major cause of forest loss. Numerous incidences of
forest fires local to large-scale infernos have been taking place in protected forest
areas. To assess damage and recovery of forests change detection based remote
sensing approach has been widely used. This has limitations in identifying the forest
patches affected by frequent and sporadic fire incidences across space and time.
Recent evolution in technology led to emergence of big geospatial data cubing, an ICT
cloud-based approach-encapsulating tens of thousands of multi-sensor satellite
imagery to provide the imaginary in analysis ready data (ARD) form. It is an emerging
paradigm for large-scale geospatial data analysis. A seamless spatial- temporal
modeling assessment of forest fires over a large area can effectively done using virtual
programming interfaces and non-parametric algorithms. The objective of this work is
27
the identification and spatial-temporal assessment of forest fire severity and regrowth
potential in the Bandipur National Park using geospatial datacube and spectral indices-
based algorithms. Results exhibit the area burnt by mi-nor and major forest fires in the
region and the area of re-growth at local level on very finer spatial-temporal scale over
an interval of time.
A Method of Micro Pixel Similarity for Lung Cancer
Diagnosis using Adaboost
Vaishnaw Kale
Dr.Vithalrao Vikhe Patil College of Engineering,Ahmednagar, India
Abstract. Today Lung Cancer is one of the difficult diseases to diagnose and is
responsible for higher number of deaths in the country, which is estimated to be 1.869
million by 2026 as compared to 1.192 million in 2011 for both sexes as per the
statistical data of Indian Council for Medical Research. The responsible factors for this
rise are increase in population, pollution and drastic changes in living lifestyle.
Researchers have been taking efforts on radiological images such as X-ray, CT and
HRCT, but still there is a scope of improvement in case of microscopic lung images
due to less work. In this paper, we have proposed a new method of Micro pixel
Similarity Technique for lung cancer analysis and diagnosis. The proposed method
utilizes the identified statistical and mathematical parameters such as Structural
Similarity Indices Matrix (SSIM), Mean Absolute Error (MAE), Absolute Difference
(AD), Mean Square Error (MSE) and Skewness. Adaboost function of ANN is used
as an image classifier. Each of the statistical and mathematical parameter utilized plays
a decisive role in lung cancer analysis and diagnosis. The proposed method is validated
through standard diagnostic test in terms of Specificity (66.34%), Sensitivity (94.95%)
and Accuracy (85.71%). This paper will be useful for researchers, academicians and
Physicians. The researchers and Academicians will understand research scope in
microscopic lung images and it will be helpful for the physicians to take a final
decision on lung cancer.
Application of hybrid of ACO-BP in Convolution Neural
Network for effective Classification
Suruchi Chawla
Shaheed Rajguru College Delhi University, India
Abstract. Convolution Neural Network (CNN) has been widely used in pattern
recognition for various applications. Convolution neural network performs non- linear
transformation on input to generate the global abstract feature vector. The resulting
global feature vector are input to Fully connected Neural Network (FNN) and the
activation value at the neuron in the output layer classify the input data vector. During
training of CNN on a given dataset, error at the output layer is minimized using
backpropagation with stochastic gradient descent. The weights optimization using
28
backpropagation has a drawback of local minima. Thus, in this research paper hybrid
of ACO-BP has been used for initialization of CNN weights using Ant Colony
Optimization (ACO) and its further optimization using Backpropagation (BP) to
overcome local minima. The performance of CNN shows the improvement since the
ability of deep learning architecture to generalize depend on the weight configuration
during training phase, Experiment was conducted on MINST data set using k-fold
cross-validation method to confirm the effectiveness of CNN with hybrid of ACO-BP
in pattern recognition. The results show the improvement in the classification
accuracy-using hybrid of ACO-BP with CNN in comparison to CNN with BP only.
Face Recognition And Mobile Location Data For Class
Attendance Monitoring
Francis Somba, Simon Mwendia and Ezekiel Kuria
KCA University, Kenya
Abstract. Consistent attendance of classes by students is vital in higher learning
institutions. Currently, most lecturers and tutors monitor class attendance by issuing
paper sign sheets where students sign for their presence. This approach comes with
challenges like students signing attendance via proxies, the possibility of lecturers
losing the paper sheets and difficulty analysing the manual record at the end of a
semester. There is need to digitize class attendance monitoring in schools. This study
proposes the use of a mobile application to track class attendance. The application will
have GPS capability to determine a student’s presence in a lecture room and face
recognition is used to authenticate the student. That is, the application prompts the
student to capture a facial photo which is compared against a known photo in the
system. Experimental results show that this approach is technically feasible and
provides a cheaper solution for managing class attendance.
Early Epilepsy Seizure Prediction using CNN
Aditya Karmokar, Chris David, Shaun Jacob and Rupali
Deshmukh
Fr. Conceicao Rodrigues Institute of Technology, India
Abstract. Epilepsy is a disorder that makes a person awkward or nervous in social life.
Epilepsy seizures are caused by sudden problems in the brain which can affect the
patient’s health. The seizure can be treated and prevented by predicting it. Electro
Encephalo-Gram (EEG) is used to detect epilepsy as it is capable of capturing the
signals of the brain. The early prediction of arriving seizure has a great impact on a
patient’s health. At the time, many machine learning methods were used to read EEG
recordings and predict seizures. However, techniques with good performance and
clinical applicability are still not made. Using the power of modern machine learning,
deep learning technique, and improving the results of seizure prediction would help
by warning the patients of upcoming seizures. So by applying Convolutional Neural
Networks (CNNs) to the device and using speech and signal processing tasks and
29
Fourier Transform algorithm will become fast and can be performed on resource-
limited hardware. So, combining the two algorithms, the device can run interference
on hardware to predict seizures by identifying preictal states.
Transformer Deep Learning Model for Bangla-English
Machine Translation
Argha Chandra Dhar1, Arna Roy1, Md. Ahsan Habib1,
M. A. H. Akhand1 and N. Siddique2
1Khulna University of Engineering & Technology (KUET), Bangladesh
2University of Ulster, United Kingdom
Abstract. Bangla is a widely spoken language but unfortunately very few researches
in Machine Translation (MT) for Bangla have been reported in the literature. This
research aims at developing an MT system for Bangla-English transla-tion. MT is
language dependent as data preparation is different from lan-guage to language.
Moreover, the vital part of MT is a model which requires training to adjust the
particular language pair along with their grammar and phrase rules. Modern deep
learning-based transformer model has been used for this language pair as it worked
well for other language pairs. A trans-former model comprising encoders and decoders
is adapted by tuning the different parameter sets to identify the best performing model
for Bangla-English translation. The proposed model is tested on a benchmark of
Bangla-English corpus, which outperformed some prominent existing MT methods.
Time Series analysis and Forecasting on crime data
Vimala Devi J1 and Dr Kavitha K S2
1Global Academy of Technology, India
2Cambridge Institute of Technology, India
Abstract. The main objective of this work is to employ and style prophetic additive
model to predict and forecast by quantifying the crime activities. This paper presents
knowledge findings by the detailed and systematic Exploratory Data analysis on crime
data of city Sacremento. A novel approach has been employed to capture the past to
understand what happened on time series data over the period January 2018-April
2020 and later FB Prophet algorithm is used to forecast on the same crime data in near
future. This Paper displays the topmost offense categories that predominantly puts the
common people in eerie situation. The experimental results obtained using Time series
analysis algorithm forecasts the crime situation for the next one month with certainty
and also indicates that there is a gradual dip in the criminal activities as the year goes.
The results capture weekly, yearly and holidays seasonality trends.
30
Distributed Association Mining for discovering interesting
rules for Tours and Travel Company
Manoj Sethi and Rajni Jindal
Delhi Technological University, India
Abstract. Mining is a favourite area of research of many researchers, developing
algorithms for association rule mining on distributed data. Distributed mining is used
in many commercial areas and there is a need to explore new commercial applications
of the mining. The application area chosen for the study is a tour and travel company
organizing package tours, as the tourism industry is growing very fast and companies
with small, medium and large sized operations are operating in these areas. Tourism
is a potential application where mining can be applied and new association rules can
be generated which can help the companies to develop new strategies and target
potential customer based on the mining outcome. This paper applies the distributed
data mining technique on a medium sized tour and travel company for finding the
association between age and destination visited parameters. The results show that
association rules generated by mining are useful and effective for the growth of the
business and making new strategies.
Advanced identification of Alzheimer’s disease from brain
MRI images using Convolution Neural Network
Soniya Lalwani1, Rajesh Kumar2, Neha Rajawat3, Bharat
Singh Hada4 and Mayank Meghawat4
1BKIT, Kota (affiliated to RTU, Kota), India
2MNIT, Jaipur, India
3Career Point University, Kota, India
4Samsung R & D Institute, Noida, India
Abstract. The robust and effective diagnosis techniques have made medical science
more advance and efficient which has resulted in a longer and healthier human life. In
parallel, the risk of non-communicable diseases is increasing in elderly people. One of
the examples of such disease is Alzheimer’s disease (AD) which is 60-70% patients
of Dementia. A slow progression of AD makes the early diagnosis very difficult using
the primitive methods thus more advance and technical solution are needed. This paper
also proposes an efficient convolution neural network (CNN) based model to detect
the presence of AD using the image dataset of Magnetic Resonance Imaging (MRI) of
brain tissues. The proposed model is trained on publicly available dataset namely
Alzheimer's Dataset (4 class of Images) which contains brain MRI scans arranged into
four classes: NonDemented, VeryMildDemented, MildDemented, and
31
ModerateDemented. Input images are first enhanced using an image processing
pipeline and then fed into CNN network. Model is quite lightweight with only 653000
trainable parameters and efficient with accuracy and F1-score of 99.3% and 99.5%
respectively. Performance is compared with other state-of-the-art works in the same
domain and current approach outperforms with a good margin.
An application of OB-MFO for Optimal Bidding Strategy
in Pay-as-bid auction environment
Pooja Jain and Akash Saxena
SKIT M & G,Jagatpura,jaipur, India
Abstract. In the restructured power system, all competitor generating companies wish
to maximize their profit as much as possible without knowing the behaviour of their
rivals. In this paper, to maximize the profit of generating company, an optimization
technique namely opposition theory enabled moth flame optimizer (OB-MFO) is used
in the pay-as-bid auction (PABA)environment. The major objective of this paper is to
maximize the profit obtained by generating company in the PABA environment. The
proposed algorithm is applied on two different test systems i.e., on IEEE-14 bus test
system and also on 7-Generator Test System and block bid prices and profit is obtained
through optimization algorithm in PABA discriminatory auctioning. The statistical
results prove the efficacy of the proposed technique to maximize the profit of
Generating Company-GC.
Wearable fall-detection using deep embedded clustering
algorithm
Jothi Ramasamy
Vellore Institute of Technology, Chennai, Tamilnadu, India
Abstract. Falls in elderly people are common. Detecting near-fall situations can
prevent fall related injuries. Wearable technology has made a significant impact in this
direction and fall-detection from wearable sensors has become an important research
problem in ambient assisted living. Although a number of machine learning algorithms
exist for wearable fall-detection, most of them are based on supervised learning. These
algorithms require a huge amount of training data and generating such data is very
time-consuming process. This paper employs deep embedded clustering, an
unsupervised learning approach, for wearable fall-detection. For experimental
purpose, Kaggle fall-detection dataset is considered. Results indicate that deep
embedded clustering achieves higher accuracy in attaining fall-detection.
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Nacelle: Knowledge Graph-based Conversational AI for
Skills Gap Analysis to Achieve Sustainable Learning at
Workplace
Chao Hong Loh, Sophia Wei, Chee Teck Phua, Azizah
Mohd, Albert Tan and Boon Khoon Seow
Nanyang Polytechnic, Singapore
Abstract. Skills Gap Analysis (SGA) is critical to identify the training needs and devel-
op the corresponding program to upskill and improve the productivity of the
workforce. The current methodology to conduct SGA for companies is large-ly done
through interview, survey and focus group discussion. This is time consuming,
manpower resources consuming, and outcomes could be de-pendent on the
interviewer/facilitator/scribe. To achieve effective SGA, it is highly desired to
leverage on conversational AI technologies to address chal-lenges. Thus, we first built
knowledge graphs which combines the SGA logic and the skills dynamically catering
requirements from respective industry. Conversational AI – Nacelle, is then developed
leveraging on the knowledge graphs to mimic SGA specialist to conduct interviews
through online chat. With Nacelle, more consistent SGA can be achieved with
minimized man-power resources requirements. More importantly, SGA conducted
through Nacelle can be applied to further train the AI models to enable self-learn and
improvement across industries. Our validation with Human Resource Indus-try shows
promising results with 99% accuracy.
Classification of driving behaviour using machine
learning methods at signalized intersections in urban and
suburban roads
Soni Karri, Liyanage C De Silva, Daphne Teck Ching Lai
and Shiaw Yin Yong
Universiti Brunei Darussalam, Brunei Darussalam
Abstract. As the drivers approaches a signalized intersection, at the onset of the yellow
signal, the driver will be in a dilemma whether to stop or go ahead. Since the yellow
signal lasts only 3 seconds, an improper decision will result in major accidents before
it turns to red. A sudden stop may lead to a back-end crash and crossing the
intersection, resulting in a red-light violation or sometimes lead to a right-angle crash.
This study's main motive is to understand the driver behavior in dilemma zone
approaching the signalized intersection and then classify the outcome of driving
behaviour (safe stopping and unsafe stopping). The real-time data is difficult to obtain
with all the driving factors and privacy concerns to keep this data in the public domain.
Since there is no reasonably live or recorded data publicly available, which captured
33
the car's parameters and the driver's driving behaviour, these parameters are captured
using a driving simulator. The simulator vehicle is a fixed interactive car with all the
controls. The test-track simulated environment is designed spanning across rural,
suburban, and urban roads to mimic the real-time scenarios and understand driver’s
driving behavior in different environments. The driver’s behaviour is classified as safe
stopping / unsafe stopping at signalized junctions using training data at the yellow
signal's onset with different machine learning approaches. The outcome of this
analysis has been used to reduce the rear-end crash risk at signalized intersections to
seek effective countermeasures and lower crash rates for the high-risk locations.
IMAGEBOT: Imagination to Quotation
Aditi Sharma, Divya Gupta and Mukesh Kumar
University Institute of Engineering and Technology, Panjab University,
Chandigarh, India
Abstract. Photographs are compact stores of special moments of our lives. Due to
convenience of photography tools such as mobiles and cameras we can capture each
and every moment of celebration. We often share those moments with our friends and
colleagues and sometimes put them on our social media profiles. To make our social
media posts attractive we usually try to associate it with some inspirational quotes.
Those quotes are either self-created or searched from the internet. Searching and
selecting the right quote from the internet for our photograph is tedious as well as
uninspiring at the same time. We have to first think about the theme which our image
is depicting and then we have to search quotes related to that theme. After this we have
to filter out the most probable quotes for our image from about thousands of quotes.
Our application IMAGEBOT: Imagination to Quotation aims to ease the process of
searching and associating the right quote for the image. It accepts a picture as an input
and after processing it, suggests relevant quotes for the image to user. To achieve this
functionality, we have used the computation power of Convolution neural networks,
concept of Long Short-Term Memory and Similarity measures for suggesting the
suitable caption for the image which are then further utilized to render quotes to user.
In this paper we not only represent the method of conversion of image to quote but
also comparative study on performance of various pre-trained CNN models.
Cataract detection using textural features and Machine
learning algorithms
Kaushal Chande, Piyush Jha, Kamaljit Kaur and
Swapnil Shinde
Ramrao Adik Institute of Technology, India
Abstract. According to the World Health Organization report, one of the world's
leading causes of blindness is reported to be due to cataracts. Even though cataract
majorly affects the elderly population however now they can be seen among minors
too. Among the various types, the prominently three types of cataract affect masses in
34
high numbers which are nuclear, cortical, and post subcapsular cataract. Conventional
methods of cataract diagnoses include slit lamp image tests by doctors which do not
prove to be effective in classifying cataracts in the early stages and can also have
inaccuracies in identifying the correct type of cataract. Existing work to automate the
process have worked on classification based upon binary detection only or have
considered only one type of cataract among the mentioned types for further expanding
the system. Our system works on the detection of cataracts in an attempt to reduce
errors of manual detection of cataracts in the early ages. Our proposed system has
successfully classified images as cataract affected or as a normal eye with an accuracy
of 96% using combined feature vectors from SIFT-GLCM algorithm applied to
classifier models of SVM, Random Forest, and Logistic Regression. The effect of
using SIFT and GLCM separately have also been studied which leads to comparatively
lesser accuracies in the model trained.
A Granular Intuitionistic Fuzzy Meta Clustering
Algorithm
B.K. Tripathy and Urmi Bhambhani
Vellore Institute of Technology, Vellore, India
Abstract. Granular computing is an approach used in information sciences to look at
data from different frames of reference. Meta clustering refers to clustering done
iteratively with some part of data also keeps updating. When these two novel ideas are
combined, interesting experiments can be performed. In this paper, we shall look at a
new algorithm called Granular Intuitionistic Fuzzy Meta Clustering, which uses ideas
of both granular computing and meta-clustering. We apply this new algorithm to a
real-world data set in order to improve clustering performance.
Performance Evaluation and Comparison of Optical
Amplifiers in Non-Linear Effects for WDM Long-Haul
Transmission System
Tsegye Menber Belay and Pushparaghavan Annamalai
Bahir Dar Unveristy, Ethiopia
Abstract. The high-speed optical network supports for higher bandwidth and it needs
choosing better optical amplifiers in long-haul fiber optic communication networks.
In Wavelength Division Multiplexing (WDM) long-haul transmission, it is vital to
minimize the dispersion and non-linear effects using Optical amplifiers along with
Dispersion Compensation Fiber (DCF) and Fiber Bragg Gratings (FBGs) to recover
the original sig-nal at the receiver end. In this research paper, the performance analysis
of Erbium Doped Fiber Amplifier (EDFA), RAMAN and EDFA + RAMAN at
different parameters has been done on the basis of two major approaches: The first
approach is Fixed Channel Spacing (FCS) where the channel spacing is fixed at
35
100GHz. The second approach is Dynamic Channel Spacing (DCS) approach, varying
channel spacing (12.5GHz - 200GHz) by considering SPM, XPM and FWM effects.
In this research analysis, EDFA has good performance than hybrid and RAMAN
amplifiers for minimizing the SPM and XPM impairments and XPM affects more
seriously than SPM in all Optical amplifiers. But, EDFA + RAMAN can enhance the
transmission span better than EDFA and RAMAN alone. The transmission
performance measured by Output optical signal to noise ratio (OOSNR) indicates that
EDFA, 94.94dB, RAMAN, 75.59dB and EDFA + RAMAN, 102.94dB. This implies
that EDFA + RAMAN has OOSNR improvement by 20dB than EDFA and 27dB
improvement than RAMAN. It is evident that hybrid Optical amplifier works best for
gain compensation and noise reduction. The complete study has supported with
Optisystem for verifying various measurements, plots and all other graphical analysis.
Estimation Of Wave Overtopping Discharge At Quarter
Circle Breakwater Using Lssvm
Haritha Sasikumar, Vishwanath Mane and Subba Rao
National Institute of Technology Karnataka, Surathkal, India
Abstract. In this paper, Least Square Support Vector Machine (LSSVM) is used for
estimating mean wave overtopping discharge at a quarter circle breakwater for varying
radii and perforations. The LSSVM model is trained and tested using the LS-SVMlab
toolbox in MATLAB. LSSVM model is developed with kernel functions Linear,
Polynomial and Radial Basis Function (RBF). The regularization parameter γ and
kernel parameters σ2 and t are tuned using grid search in LS-SVMlab. The parameters
used for training the model are input parameters Hi/gT2, d/gT2, Rc/Hi, p and output
parameter q/gTHi. The performance of the model is evaluated using statistical
parameters Root Mean Square (RMSE), Correlation Coefficient (CC), Nash Sutcliffe
Model Efficiency (NSE) and Scatter Index (SI). The RBF kernel performed better
compared to the other kernels. The predicted values had a correlation of 0.9143 and
0.8551 for train and test models respectively.
Forward and Backward Modelling of Wire and Arc
Additive Manufacturing Process using Multiple Adaptive
Neuro-Fuzzy Inference System
Dhrubajyoti Gupta1, Ananda Rabi Dhar1, Shibendu
Shekhar Roy1 and Nilrudra Mandal2
1National Institute of Technology Durgapur, India
2Central Mechanical Engineering Research Institute, India
Abstract. Wire and arc additive manufacturing (WAAM), especially its new variant
cold metal transfer (CMT) process is regarded as one of the most potential and
36
advanced additive manufacturing processes. The process parameters play an
extremely influential role to produce the desired dimensional accuracy, surface finish
and overall process stability. Hence, subtle determination of a suitable combination of
the process parameters stands extremely crucial. In this paper, adaptive neuro-fuzzy
inference system-based models have been developed in order to achieve a bi-
directional predictive capability for a set of 3 inputs and 12 responses. The models
have been trained and tested in accordance with additional data generated from
statistical regression applied on experimental data. R-squared values of the training
samples and mean absolute percentage errors of the test samples for each response
have been found quite satisfactory suggesting fairly adequate predictive models. With
this approach both forward and backward mappings have been successfully achieved.
Wireless Sensor Networks Localization by Improved
Whale Optimization Algorithm
Nebojsa Bacanin1, Milos Antonijevic1, Timea Bezdan1,
Miodrag Zivkovic1 and Tarik A. Rashid2
1Singidunum university, Serbia
2Computer Science and Engineering Department, University of Kurdistan
Hewler, Erbil, KRG, Iraq
Abstract. Wireless sensor networks, that are composed of a finite number of spatially
distributed autonomous sensors, are widely used in different areas with many potential
applications. However, in order to be implemented efficiently, especially in poorly
accessible terrains, localization challenge should be addressed. Localization refers to
determining the unknown target nodes positions by using information about location
of anchor nodes, based on different measurements, such as the time and the angle of
arrival, time difference of arrival, and so on. This task is considered to be a NP-hard
by its nature and cannot be addressed with traditional deterministic approaches. In this
research we have proposed the improved implementation of swarm intelligence
approach, whale optimization algorithm, to address localization challenge in wireless
sensor networks. Observed drawbacks of original whale optimization algorithm are
overcome in enhanced implementation by incorporating quasi-reflected based learning
algorithm. Proposed metaheuristics is tested using the same network topology and
experimental conditions as other advanced metaheuristics which results are published
in the most recent computer science literature. Based on simulation results, devised
algorithm manages to establish lower localization error than the basic whale
optimization algorithm, as well as other outstanding metaheuristics.
Early Flood monitoring Using Intelligent System
Sidhart Joshi, Sushma Dave and Parth Sarthi Medatwal
JIET Jodhpur, India
Abstract. The interactions between land-use changes and water regime has severe
37
impacts on the surface water balance. The most common land-use change is changing
from natural cover into built-up concrete cover. At a catchment scale, the calculation
of land-use changes the overall impact on flooding requires an understanding of the
relevant storm water flow generation mechanisms for both catchment characteristics
and precipitation conditions that follow. Generally, these impacts could mainly
influence evapotranspiration, groundwater recharge and surface water stay and flow,
taking land-use change as the process and water regime as the product, changing
natural surface into built-up concrete land will reduce the interception of precipitation
and the water storage which has a direct effect on the amount of water that is
immediately available for evapotranspiration This conversion also decreases
groundwater recharge rate which may has a strong negative impact on surface water
stay or flow and can make a major impact. Therefore, an intelligent system could be
implemented in Disaster Management System (DMS) to produce the Intelligent
Disaster Management System (IDMS) intelligent system. The Intelligent concept
would accommodate the service appropriately. As the result, IDMS framework model
hoped to reduce the victims of disaster.
An Enhanced DBA for Supporting Maximum User with
Minimum Delay
Md Hayder Ali and Mohammad Hanif Ali
Jahangirnagar University, Bangladesh
Abstract. Telecom operators are becoming eagerness about GPON service, for its
multiservice, video on demand (VoD), any time any service and easy O&M (operation
and maintenance). Service delay or any network related delay while service running
could affect on Quality of Service (QoS) and Service Level Agreement (SLA). That’s
why, supporting maximum users with minimum delay is becoming main concern for
service providers. An enhanced Dynamic Bandwidth Allocation Algorithm (DBA)
could minimize delay and support maximum user. In this paper, a comparison between
existing P-DBA C-DBA, and a proposed DBA are studied with delay calculation. The
simulation is designed in OptSIM simulator and captured data are analyzed by
MATLAB coding. Proposed DBA could support maximum user with minimum delay.
Prediction of the Geographical Origin of Soils Using
Ultra-Performance Liquid Chromatography (UPLC)
Fingerprinting and K-Nearest Neighbor (K-NN)
Loong Chuen Lee1, Hukil Sino1, Nor Azman Mohd Noor1,
Saiful Mohd Ali2 and Azhar Abdul Halim1
1Universiti Kebangsaan Malaysia, Malaysia
2Pusat Analisis Sains Forensik, Jabatan Kimia Malaysia, Malaysia
38
Abstract. Machine learning methods had scarcely been applied to forensic
discrimination of soil samples. In this work, the non-volatile organic profile of five
different red and brown soils, respectively, have been acquired via ultra-performance
liquid chromatography (UPLC) technique and the K-nearest neighbour (KNN)
algorithm has been successfully evaluated for discriminating and classifying the ten
soil samples. The data matrix of 30 rows and 18 000 columns was first explored using
principal component analysis (PCA) and then modelled via KNN algorithm. Several
KNN models were constructed by considering: (a) two different input regions, i.e., full
and truncated chromatograms; and (b) four values of the number of nearest neighbors
(K): 1, 2, 3 and 4, respectively. Scores plots of PCA indicated soils showing different
colors but originated from the same location were not always clustered together.
Despite that, most of the KNN models achieved internal and external accuracy rates
of approaching 100%.
Flower Classification in Videos: A HOG-PCA-NN Method
Chaitra K N1, Jyothi V K2, Chandrajith M1 and Guru D2
S
1Department of Computer Science, MIT First Grade College,Mysore,
India
2Department of Studies in Computer Science, University of Mysore,
Manasagangotri, India
Abstract. In this paper, a model for the classification of videos of the flower is
proposed using the Nearest Neighbor (NN) classifier and Histogram of Orient
Gradient (HOG) texture feature. Flowers in videos are segmented using the Otsu
threshold segmentation technique. Further, Principal Component Analysis (PCA) has
been used to select the discriminating features and for dimensionality reduction. The
efficiency of the proposed system is ascertained using the dataset which consists of
ten different classes of flower videos. The dataset exhibits large intraclass variation
with less inter-class similarity. Comparative analysis with well-known models
demonstrates the efficacy of the proposed method.
Building damage detection using Discrete Wavelet
Transforms and Convolutional Neural Networks
Piyush Ranjan Biswal, Banhi Sanyal and Ramesh Kumar
Mohapatra
National Institute of Technology, Rourkela, India
Abstract. Assessing the damage to life and property in the wake of a natural disaster
can be a herculean task. Rapid identification of the damaged areas can aid in
appropriate maintenance to mitigate the damage caused. The paper discusses the
detection of damaged buildings from post-event aerial imagery of disaster-affected
39
areas using Deep Learning. Data is taken from Kaggle dataset of Satellite Images of
Hurricane Damage. The images are first subjected to Discrete Wavelet Transforms to
extract features and reduce dimensionality. Further, a Convolutional Neural Network
has been employed to classify the affected area as damaged or undamaged. This
unconventional approach for classification is found to yield promising results and
outperforms many previously published architectures. It is observed through
experiments that the Wavelet-based feature extraction combined with Convolutional
Neural Networks produces a significant accuracy of 94.8% on a balanced test set and
gives 92.44% of accuracy on an unbalanced test set.
On-device ML: An efficient approach to classify large
number of images using multi-threading in Android
Devices.
Saurabh Kothari1, Rayan Crasta1, Alen Biju1, Trupti
Lotlikar1 and Harshit Rai2
1Fr. Conceicao Rodrigues Institute of Technology, Navi Mumbai, India
2Shell India Markets Private Limited
Abstract. Machine Learning has unwaveringly found its way into many modern-day
applications and it only seems to be spreading widely in the near future. Most of the
applications make use of machine learning models and algorithms for classification,
object detection purposes and are dependent on GPU’s to perform complex
computational tasks. This posed as a serious limitation for the development of such
applications for mobile platforms due to lower processing capabilities of mobile chips
and storage issues. For a long time in the past, developers were reliant on cloud-based
approach for machine learning capabilities to their applications with the help of REST
API services. This is where “On-device Machine Learning” comes into the picture,
using a mobile computing platform which utilizes the internal CPU and GPU, present
in every mobile device. However, due to certain limitations in the computing power
of mobile processing chips, it is very easy to throttle this solution. In this paper, we
present a multi-threaded approach to classify bulk images on the mobile CPU. This
approach aids the On-Device Machine Learning approach by providing multiple
threads for the application to process and handle data without throttling the hardware.
A Systematic Study of Intelligent Face Scanning in Rare
Disease Detection
Suksham Sharma and Deepti Malhotra
Central University of Jammu, India
Abstract. A rare disease is a health condition of low prevalence that affects a small
number of people compared with other prevalent diseases in the general population.
40
The cause of many rare diseases is still unknown. However, most of them can be traced
to genetic mutations and are known as rare genetic diseases. These diseases have very
limited diagnostic information available; making clinical diagnosis difficult.
Distinguishable facial features can serve as an important criterion in the detection of
rare genetic diseases. With the use of facial recognition technology, the detection of
rare genetic diseases can be made easier for clinicians. Recent trends show that AI
Face-Scanning apps, based on machine learning and deep learning algorithms, can
detect genetic diseases with a high accuracy rate. This research work presents the
systematic study done in the detection of some of the rare genetic diseases, which can
be distinguished based on distinct facial phenotypes (the set of observable physical
characteristics of an individual).
Key Exchange Using Tree Parity Machines: A Survey
Ishak Meraouche and Kouichi Sakurai
Kyushu University, Japan
Abstract. Secure key exchange is an important step to secure a communication. When
multiple parties are using a symmetric key encryption protocol, they need a secret key
to exchange encrypted messages. If the key is compromised, their whole
communication gets exposed. While many techniques are based on mathematics for
their design, another trend is using Artificial Intelligence (AI) to build a model that
can learn to exchange keys securely. One of the most famous AI-based techniques is
the Tree Parity Machine Key Exchange Model. Although it has been broken shortly
after its introduction, it has seen many improvements during the last decade. In this
paper, we will conduct a survey on this model to how it works and how it was broken.
Then, we will survey the improvements it has seen and tell if there is still a possibility
to use it in real world applications.
Adaptive Exon Prediction using Maximum Error
Normalized Algorithms
Zia Ur Rahman Mohammad, Vishnu Vardhan
Baligodugula, Jenith Lakkakula, Rakesh Reddy
Veeramreddy, Surekha Sala and Srinivasareddy Putluri
Koneru Lakshmaiah Education Foundation, K L University, India
Abstract. Cloud Computing affords healthcare companies with vital studies and
financial Profits. Cloud computing ensure that large quantities of such sensitive data
will be stored and managed securely. The gene collection labs ship uncooked or gather
records through the Net to numerous collection center below traditional move
alongside with the drift of gene records. Cloud service use will reduce DNA
sequencing storage costs to a minimum. These services, has got suggested a brand
latest genomic bioinformatics primarily form total system, using Amazon Cloud
Services, that stores and processes genomic sequence information. The clue job in bio-
informatics, it gives idea about recognition and blueprint of disease drug, is the true
41
recogniting of exon regions in (DNA) series. All exon identification techniques are
based on three basic periodicity (TBP) properties of exons. In differentiation to various
techniques, adaptive signal processing techniques have been promising. This paper
uses the maximum error normalized logarithmic mean least square (MENLMLS)
algorithm also its signed variants to develop multiple adaptive exon predictor (AEPs)
with less computational complexity. Eventually, a performance evaluation is
performed for different AEPs using various standard gene data sequences derived from
National Biotechnology Information Centre (NBI) genomic sequence database, such
as Sensitivity (Sn), Specificity (Sp) and Precision (Pr) measurements.
A Novel Approach for wavelength Optimization in GPON
Quad play
Md Hayder Ali and Mohammad Hanif Ali
Jahangirnagar University, Bangladesh
Abstract. Receiving sensitivity for GPON service is an important part, for better
service, maintaining QoS and SLA. GPON has a wide range of wavelength for service
activation and also has different receiving sensitivity. Operators’ requirement is
increasing for STM, E1 traffic along with GPON triple play service. GPON can use
1310 nm to 1610 nm wavelength both for triple play (voice, video and Data) and quad
play (SDH traffic, voice, video and data). In this paper a graphical comparison is
presented in context of receiving sensitivity both for triple and quad play. The
simulation is designed in two steps (triple play and quad play) using OPTsim
simulator. Graphical comparison is made separately for voice and data, video and eye
diagram for SDH traffic.
LoRa Based Sensing Network Setup and IoT Integration
for Smart Agricultural Management
Aruna Singh
RGPV, India
Abstract. Multi Intelligent control system (MICS) development and its application
handling become crucial day by day in 21st Century. Technological inputs have been
grown abruptly in all sectors of society. With the emerging technology of Internet of
Things (IoT), smart agriculture system has become a new trend in the agriculture field.
Our Proposed work uses multi sensing applications, use of Internet of Things (IoT),
Smart communication flow of data between sensors and controlling devices, power
optimization, water management and smart security compatible with expected
solutions of real time challenges in irrigation sector. Water storage and circulation is
enriched with sensory modules for regular supervising and fault identification. The
most popular communication technologies are the Bluetooth, Wi-Fi, Zigbee etc. But
have few limitations like limited range, limited power and limited bandwidth. The
battery power consumption in Wi-Fi and Bluetooth technology is high and drain out
42
the battery quickly. Cellular networks and LAN technology also have same problems
of high power consumption. The LAN and Cellular network both are more expensive.
LoRa Technology in field of IoT can perform very-long range transmission with low
power battery consumption. Applications of LoRa includes Smart Agriculture & Soil
Health Monitoring system, Smart Parking, Smart shopping, Smart water monitoring,
Remote Control of Appliances and Autonomous irrigation. Furthermore, we are
proposing a Generic MICS architecture to integrate LoRa capability in IoT- based
applications for enhanced performance.
Evaluation of Machine Learning Models for Sign
Language Digit Recognition
Divya Lakshmi and Balasundaram S R
National Institute of Technology, Trichy, India
Abstract. Basically, sign language helps to define communication in many ways.
Useful for hearing challenged people as well as for children of earlier ages to
understand the concepts in a better way. In the context of human and ma-chines
interaction, sign language can help in the process of training for larger groups and
training at any pace or time. When the computer applications have to understand the
signs of people, they must be trained with suitable machine learning models. This
paper discusses evaluation of various learning models for sign language digits
recognition. Classifiers based on decision tree, regression, and deep learning are
considered for the recognition of digits from various sign images. Performances were
observed by training the models over raw and skin segmented images from publicly
available digit datasets. Among machine learning models over unsegmented images,
Support Vector Machines returned higher test accuracy whereas Random Forest
classifier returned higher accuracy over segmented images. Deep learning based
convolutional neural network with higher number of parameters and elaborate training
process achieved the highest accuracy. The performances were found to improve when
trained and tested over segmented images. Also, the accuracy was found to improve
with an improvement in segmentation accuracy.
Parking Lot Occupancy Detection Using Hybrid Deep
Learning CNN-LSTM Approach
Bui Thanh Hung, Prasun Chakrabarti and Anand
Nayyar
Data Analytics & Artificial Intelligence Laboratory, Engineering-
Technology School, Thu Dau Mot University, Viet Nam
Techno India NJR Institute of Technology, India
Graduate School, Duy Tan University, Viet Nam
43
Abstract. One of common challenges facing smart city is to predict the crowd
movement patterns, and their application in public transportations. Recently, in
computer vision, especially for recognition and cognitive tasks, deep learning has
made great breakthroughs. Inspired by human brain structures, deep learning takes
advantage of the hierarchical models. Inspired by the advantages of deep learning, we
propose a system to identify the occupied and vacant parking lots using a hybrid deep
learning approach. The hybrid model combines the superior features of Convolutional
Neural Networks (CNNs) and Long Short Term Memory (LSTM) deep learning
methods. We did four experiments in two datasets: CRNPark and CRNPark-EXT and
compared the results with other models. Our proposed model enhances the accuracy
of the system in comparison with the results of the others.
Chili leaf disease detection using texture features of image
and classification by SVM and KNN
Asha Patil and Kalpesh Lad
S.T.Co.Op.Edu.Society Science Sr.College Shahada, India
Uka Tarsadia University, Bordoli, Gujrat, India
Abstract. Recognition and diagnosis of chili leaf diseases in agriculture is a major
challenge. Monitoring crop fields and identifying disease signs are essential for
farmers. Image processing is an aid in the identification and diagnosis of leaf diseases.
For leaf dis- ease identification, there are three features of the image i.e., texture, color,
and shape. Out of three textures is a more important feature. In this work, sixteen
texture features are measured using the GLCM algorithm. These texture features are
contrast, energy, homogeneity, Correlation, entropy, mean, cluster_shade, cluster_
provience, variance, kurtosis, skewness, Std_deviation, IDM,RMS, smoothness, and
Max.Probability respectively. After computing sixteen texture features values entered
in multi-class classifiers one by one in SVM and KNN. The SVM has given better
accuracy results than KNN in each feature.
Chronological Sine Cosine Algorithm Based Deep CNN
for Acute Lymphocytic Leukemia Detection
Sneha D and Alagu S
Anna University, Chennai, India
Abstract. Blood cancer is one of the most crucial diseases. Especially, the most
common type of blood cancer is Leukemia. In the history of the acute lymphocytic
leukemia detection, many techniques are employed. The proposed work has developed
a Chronological Sine Cosine Algorithm (SCA) based deep CNN for leukemia
detection. For the leukemia detection, the blood smear images are taken from the
Acute Lymphocytic Leukemia image database. The images are get resized in the pre-
processing module. The segmentation is done by the proposed Mutual Information
44
(MI) based hybrid model which is a combination results of Active Contour Model and
Fuzzy C means Algorithm (FCM). From the segmented images, statistical and textual
features are extracted. The extracted features are provided to the chronological SCA
based deep CNN classifier for leukemia detection. The chronological SCA Algorithm
is used for selecting the optimal weights for the CNN model. The algorithm computes
the fitness value as an error function of CNN model. Simulation results of the proposed
methodology shows that the classifier has an accuracy of 81%. Precision, Recall and
F1 score are calculated to evaluate the performance of the deep CNN classifier.
Malarial Parasite Detection Based On Smartphone
Microscopic Imaging Using Deep Learning Approach
Breesha R and Alagu S
Anna University, Chennai, India
Abstract. Malaria is a dangerous and life risking disease and sometimes leads to death.
Microscopy examination was used for diagnosing malaria infected cells in early days.
Due to large number of samples for analysis and complexity of time, it may lead to
false detection. More time consumption and false detection resulted in a great need for
automated parasite detection systems. The proposed work aims to detect the malaria
infected images from microscopic blood smear images which are acquired by
smartphone. Detection of malaria infected images is done by using a convolutional
neural network model called ResNet. In the proposed work, Deep learning approach
is used to provide more reliable diagnosis, specifically in resource limited areas and it
also reduces the cost of diagnosis. As the microscopic blood smear images are acquired
by smartphones, it provides easy and cost effectiveness for collecting image datasets
with less time. It can also quickly transfer the blood smear images for early diagnosis.
In the proposed work, the images are passed through convolutional layer consists of
residual units which is defined by ReLu and Batch normalization. Finally, proceeded
by fully connected layer to give the predicted output either malarial infected or
uninfected images. The training and validation accuracy and loss graphs have been
plotted and the performance metrics of the model have been evaluated.
Linguistic Data Analysis using Nagel Point based Ranking
Fuzzy Numbers for Financial Risks Management
Lazim Abdullah, Ahmad Termimi Ab Ghani and
Nurnadiah Zamri
Universiti Malaysia Terengganu, Malaysia
Abstract. Studies of financial management show the importance of various types of
financial risks as these risks will affect the performance of financial institutions.
However, the authentic risks leading to financial crises are indecisive. This paper aims
to propose the rank of the selected financial risks contributed to financial crises in
banking sector using a method of ranking fuzzy number. The linguistic data given in
45
triangular fuzzy numbers are analyzed using the method of ranking fuzzy numbers
based on Nagel Point to deter-mine the highest-ranking risk of financial management.
Five experts were invited to provide qualitative linguistic evaluation over risks in
financial risk management. The proposed method of Nagel Point, which considers
Carte-sian coordinates of Nagel Point and function N of triangular fuzzy numbers are
implemented in financial risk management. The transformation function N suggests
that credit risk is the highest risk in financial management. An im-plication for banks
is that the importance of addressing risk and paying close attention to the risk
management to avoid financial losses.
A Dynamic Web Data Extraction From Srldc (Southern
Regional Load Dispatch Centre) And Feature
Engineering Using Etl Tool
Dhanalakshmi J and Ayyanathan N
B.S. Abdur Rahman Crescent Institute of Science and Technology, India
Abstract. Dynamic Web extraction is used to extract the data from web server based
on researcher needs. ETL software is a piece of software that collects data from
multiple sources and then cleans, customizes, reformats, incorporates and inserts data
into a data source. The organization of SRLDC website is hard because the conversion
of unstructured field variable may vary. The ETL data mechanism is responsible for
gathering and repairing data from operating systems into the data source. In order to
overcome, this proposed research work of web extraction with python beautiful soup
is used to extract data directly from website to form a cumulative dataset.
Unlocking the potential of Natural Language Processing
and Healthchatbots in Health care management
Sivarethinamohan R1, Sujatha S2, Pritha Biswas1 and
Parthiban Jovin1
1CHRIST (Deemed to be University), Bengaluru, India
2K Ramakrishnan College of Technology, India
Abstract. During the COVID-19 pandemic, Natural Language Processing (NLP)
Healthchatbots play a strategic role in disease detection, intensive care, drug dis-
covery and controlling the mushrooming of infections. It energizes chat programs to
assist in the reduction of outbreaks during the initial stages of coronavirus infection.
NLP technologies have reached new heights in terms of utility, and are at the heart of
the success of a multilingual conversation system, Chatbots, and Deep learning
language models. NLP powered AI such as Health map and Copweb platforms track
patient requests and perform incident detections. This study looks at the role of NLP
and its technologies, challenges, and future possibilities using AI and machine learning
46
for crisis mitigation and easier EHRs in the health care industry.
Discrete Wavelet based Multi-classifier Approach for
Recognition of Offline Handwritten Hindi Numerals
Danveer Rajpal and Akhil Ranjan Garg
MBM Engineering College, Jodhpur, India
Abstract. The challenges and broad application fields related to handwritten numeral
recognition, attracts the research communities for further development in pattern
recognition techniques. The main challenges one has to face for developing such
systems are individuals writing practices, degree of similarity in digit shapes and
typical structure of digits written in Hindi script. The proposed model is designed to
face these challenges by implementing effective feature extraction and classification
methods. The model exploited Bi-orthogonal Discrete Wavelet Transform for
important feature extraction from offline handwritten digits and classified them with
the help of multiple classifiers like Multi-Layer Perceptron (MLP), Support Vector
Machine (SVM) and K-Nearest Neighbor (KNN) to test their performance for solving
the given problem. The proposed model not only recognized the handwritten numerals
quite accurately but also successful in reducing the size of original features to release
computational loads of classifiers. The scheme managed to attain recognition accuracy
of 96.64%, 99.84% and 97.04% by the mentioned classifiers respectively.
Sentiment Analysis through Machine Learning: A Review
Meenu Bhagat and Brijesh Bakariya
IKGPTU,Kapurthala, India
Abstract. Sentimental analysis is gaining its popularity in the field of text mining. It is
the study about people’s opinions about any event, individual or topic. Users are
posting online reviews and opinions about specific product or service and it has
become popular way to share our reviews on social web, as it is difficult to obtain
users reviews in such a rapid manner through any other means. It also provides us
volume of information on social media like Facebook and Twitter and range of
possible user opinions in a time saving way. It is difficult as well as interesting due to
bulk amount of information generated by online social media and different kind of
possible opinions. Sentimental analysis on Facebook, Twitter has attracted much
attention recently due to its wide applications in various commercial and public
sectors. The main focus of this paper is to give a brief overview of sentimental analysis
and its techniques and it also provides a comparative analysis of the research done in
the field of sentiment analysis. These types of analysis are based on machine learning
approach.
RAFI: PARALLEL DYNAMIC TEST-SUITE
REDUCTION FOR SOFTWARE
47
Najneen Qureshi
RTU, Rajasthan, India
Abstract. A trend in software testing is reducing the size of a test suite while preserving
its overall quality. For software, requirements and a set of test cases are given. Each
test case is covering some requirements. In this paper, our goal is to find the method
for test-suite reduction (TSR) to calculate the minimal subset of test cases that cover
all the requirements across versions. While this problem has gained significant
attention, it is still difficult to find the smallest subset of test cases and widely used
methods to solve this problem with only approximate solutions. In this paper, our goal
is to find the greedy method for test-suite reduction (TSR) to calculate the minimal
subset of test cases that cover all the requirements across versions. There are already
existing exponential-time algorithms and greedy algorithms to find the TRS in a
version-specific and across versions. We proposed a new parallel greedy heuristic
method RAFI to find minimal test sets in across versions. Our approach shows that:
(i) RAFI is much faster than the exponential time algorithms and approximately 1000x
time faster than the traditional greedy method. (ii) RAFI method achieves roughly the
same reduction rate compared to the traditional greedy method.
Memetic spider monkey optimization for spam review
detection problem
Sayar Singh Shekhawat and Harish Sharma
Rajasthan Technical University, Kota, India
Abstract. Spider monkey optimization (SMO) algorithm mimics the fission-fusion
social behavior of the spider monkeys. It is clear through literature that the SMO is a
competitive swarm intelligence-based algorithm to solve the complex real-life
optimization problems. As the optima search process of SMO is little bit biased by the
random component that drives it with high explorative searching steps. So, this may
enhance the chance of skipping the optimum solution. Here this paper hybridized SMO
with memetic search to improve the local search ability of SMO. The newly developed
strategy is titled as Memetic SMO (MeSMO). Furthermore, the proposed MeSMO
based clustering approach is applied to get rid of the spam review detection problem.
A customer usually makes decisions to purchase something or make an image about
someone, based on the online reviews. Therefore, there is a good chance that the
individuals or companies may write spam reviews to upgrade or degrade the stature or
value of a trader/product/company. Therefore, given designing an efficient spam
detection algorithm, the proposed MeSMO is tested over four complex spam datasets.
The reported results of MeSMO are compared with the outcomes obtained from the
six state-of-art strategies. A comparative analysis of the results proved that the
MeSMO is a competitive swarm intelligence-based approach to solve the spam review
detection problem.
48
Best Practices of Machine Learning Methods in the Field
of Cybersecurity: A Review
Manish Choubisa
Arya Institute of Engineering and Technology, India
Abstract. In this review paper, many research studies on machine learning (ML)
procedures for analysis of computer network intrusion detection are described. With
Machine Learning techniques, cybersecurity systems frameworks can ana-lyze
designs and improvement from them to help prevent similar attacks and react to
changing behavior. In addition, it presents a short instructional exercise clarifi-cation
on each ML/DL strategy. Information holds a huge situation in ML/DL strategies; thus
this paper features the datasets utilized in ML procedures, which are the essential tools
for analyzing the overall the traffic in computer networks. Moreover, we expand on
the issues encountered in utilizing ML & DL for cyber security and offer counsels for
forthcoming examinations
