Artificial Intelligence

Research & Advances

Journal of

May - August 2014

(JoAIRA)

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Journal of Artificial Intelligence Research & Advances

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Industrial Tribology Machine Dynamics & Maintenance

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Indian Institute of Technology Delhi, India.

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Alternative Energy Technology Laboratory,

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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