Artificial Intelligence
Research & Advances
Journal of
May - August 2014
(JoAIRA)
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Industrial Tribology Machine Dynamics & Maintenance
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I take the privilege to present the print version for the Volume 1 Issue (2) of Journal of Artificial
Intelligence Research & Advances. The intension of JoAIRA is to create an atmosphere that
stimulates creativeness, research and growth in the area of Artificial Intelligence.
The development and growth of the mankind is the consequence of brilliant Research done by
eminent Scientists and Engineers in every field. JoAIRA provides an outlet for Research findings
and reviews in areas of Artificial Intelligence found to be relevant for National and International
recent developments & research initiative.
The aim and scope of the Journal is to provide an academic medium and an important reference for
the advancement and dissemination of Research results that support high level learning, teaching and
research in the domain of Artificial Intelligence.
Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for
their continued support and invaluable contributions and suggestions in the form of authoring write
ups/ reviewing and providing constructive comments for the advancement of the journals. With
regards to their due continuous support and co-operation, we have been able to publish quality
Research/Reviews findings for our customers base.
I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.
Dr. Archana Mehrotra
Director
STM Journals
Director's Desk
STM JOURNALS
1. A Production Inventory Model in a Random Planning Horizon Incorporating Learning Effects on Inventory Costs Under Inflation and Time Value of Money via Genetic Algorithm
Arindam Roy, Manas Kumar Maiti 1
2. Text Extraction from Images Using Connected Component Method Arvind, Mohamed Rafi 13
3. Face Detection and Recognition System Using Back-Propagation Neural Network ClassifierRizoan Toufiq, Md. Shamiul Haque Chowdhury, Md. Rabiul Islam 19
ContentsJournal of Artificial Intelligence Research & Advances
JoAIRA (2014)© STM Journals 2014. All Rights Reserved
Journal of Artificial Intelligence Research & Advances
Volume 1, Issue 2
www.stmjournals.com
A Production Inventory Model in a Random Planning
Horizon Incorporating Learning Effects on Inventory
Costs Under Inflation and Time Value of Money
via Genetic Algorithm
Arindam Roy1*, Manas Kumar Maiti
2
1Department of Computer Science, Prabhat Kumar College, Contai, Purba Medinipur, W.B, India
2Department of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, W.B, India
Abstract A production inventory model for a new product is developed incorporating inflation and time value of money. It is assumed that lifetime of the product is random in nature and
follows exponential distribution with a known mean. So planning horizon of the model is
random in nature. Here learning effect on production and set-up cost is incorporated. Model is formulated to maximize the expected profit from the whole plan-ning horizon
and is solved using Genetic Algorithm (GA). The model is illustrated with some numerical
data. Sensitivity analysis on expected profit function is also presented.
Keywords: Learning Effect, inflation, time value of money, random planning horizon,
genetic algorithm
JoAIRA (2014)© STM Journals 2014. All Rights Reserved
Journal of Artificial Intelligence Research & Advances
Volume 1, Issue 2
www.stmjournals.com
Text Extraction from Images Using Connected
Component Method
Arvind*, Mohamed Rafi Computer Science and Engineering Department, U.B.D.T College of Engineering,
Visvesvaraya Technological University, Davangere, Karnataka, India
Abstract Text information from image serves as an important clue for many image based applications. it has been used in many different applications such as; document analysis,
vehicle licence plate extraction, content based information indexing, To name a few, the
detection and extraction of text regions in images is a well-known problem in computer vision and image processing. However, variation of these texts due to differences in size,
orientation style, and alignment, as well as low image contrast and complex background
make problem of automatic text detection and extraction tremendously difficult and challenging one. Here the connected component method applied on the dataset which
includes the images which categorized according to the language of the scene text captured.
Keywords: Text Information Extraction (TIE), Text Detection, Connected Component
Method.
JoAIRA (2014)© STM Journals 2014. All Rights Reserved
Journal of Artificial Intelligence Research & Advances
Volume 1, Issue 2
www.stmjournals.com
Face Detection and Recognition System Using Back-
Propagation Neural Network Classifier
Rizoan Toufiq, Md. Shamiul Haque Chowdhury, Md. Rabiul Islam* Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology
Rajshahi, Bangladesh
Abstract Today Security is an important part of our everyday lives. Biometric technologies are used for providing and increasing the security of the organizations. Human face
recognition is a potential method of biometric authentication. This paper represents a
process of face detection and recognition system using principal component analysis with back-propagation neural network. For recognizing the faces there are several steps
required. Principal component analysis is used for feature extraction whereas back-
propagation neural network is used as face recognition classifier. This paper focuses on identifying and verifying a person by matching the test image comparing with the facial
database images .The aim of this paper is to provide a better security system by identifying and recognizing human face. The proposed face recognition system gives an
accuracy of more than 85%.
Keywords: Face recognition, facial feature extraction, principal component analysis,
back-propagation neural network, face detection, acceptance ratio