Advances in Intelligent Systems and Computing 337

Suresh Chandra SatapathyA. GovardhanK. Srujan RajuJ. K. Mandal Editors

Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1

Advances in Intelligent Systems and Computing

Volume 337

Series editor

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Polande-mail: kacprzyk@ibspan.waw.pl

About this Series

The series “Advances in Intelligent Systems and Computing” contains publications on theory,applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually alldisciplines such as engineering, natural sciences, computer and information science, ICT, eco-nomics, business, e-commerce, environment, healthcare, life science are covered. The list of top-ics spans all the areas of modern intelligent systems and computing.

The publications within “Advances in Intelligent Systems and Computing” are primarilytextbooks and proceedings of important conferences, symposia and congresses. They cover sig-nificant recent developments in the field, both of a foundational and applicable character. Animportant characteristic feature of the series is the short publication time and world-wide distri-bution. This permits a rapid and broad dissemination of research results.

Advisory Board

Chairman

Nikhil R. Pal, Indian Statistical Institute, Kolkata, Indiae-mail: nikhil@isical.ac.in

Members

Rafael Bello, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cubae-mail: rbellop@uclv.edu.cu

Emilio S. Corchado, University of Salamanca, Salamanca, Spaine-mail: escorchado@usal.es

Hani Hagras, University of Essex, Colchester, UKe-mail: hani@essex.ac.uk

László T. Kóczy, Széchenyi István University, Gyor, Hungarye-mail: koczy@sze.hu

Vladik Kreinovich, University of Texas at El Paso, El Paso, USAe-mail: vladik@utep.edu

Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwane-mail: ctlin@mail.nctu.edu.tw

Jie Lu, University of Technology, Sydney, Australiae-mail: Jie.Lu@uts.edu.au

Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexicoe-mail: epmelin@hafsamx.org

Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazile-mail: nadia@eng.uerj.br

Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Polande-mail: Ngoc-Thanh.Nguyen@pwr.edu.pl

Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Konge-mail: jwang@mae.cuhk.edu.hk

More information about this series at http://www.springer.com/series/11156

Suresh Chandra Satapathy · A. GovardhanK. Srujan Raju · J.K. MandalEditors

Emerging ICT for Bridging theFuture - Proceedings of the49th Annual Convention of theComputer Society of India(CSI) Volume 1

ABC

EditorsSuresh Chandra SatapathyDepartment of Computer Science and

EngineeringAnil Neerukonda Institute of Technology

and SciencesVishakapatnamIndia

A. GovardhanSchool of Information TechnologyJawaharlal Nehru Technological University

HyderabadHyderabadIndia

K. Srujan RajuDepartment of CSECMR Technical CampusHyderabadIndia

J.K. MandalFaculty of Engg., Tech. & ManagementDepartment of Computer Science and

EngineeringUniversity of KalyaniKalyaniIndia

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-319-13727-8 ISBN 978-3-319-13728-5 (eBook)DOI 10.1007/978-3-319-13728-5

Library of Congress Control Number: 2014956100

Springer Cham Heidelberg New York Dordrecht Londonc© Springer International Publishing Switzerland 2015

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of thematerial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broad-casting, reproduction on microfilms or in any other physical way, and transmission or information storageand retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now knownor hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in this bookare believed to be true and accurate at the date of publication. Neither the publisher nor the authors or theeditors give a warranty, express or implied, with respect to the material contained herein or for any errors oromissions that may have been made.

Printed on acid-free paper

Springer International Publishing AG Switzerland is part of Springer Science+Business Media(www.springer.com)

Preface

This AISC volume-I contains 73 papers presented at the 49th Annual Convention ofComputer Society of India: Emerging ICT for Bridging the Future: held during 12–14December 2014 at Hyderabad hosted by CSI Hyderabad Chapter in association withJNTU, Hyderabad and DRDO. It proved to be a great platform for researchers fromacross the world to report, deliberate and review the latest progresses in the cutting-edgeresearch pertaining to intelligent computing and its applications to various engineeringfields. The response to CSI 2014 has been overwhelming. It received a good number ofsubmissions from the different areas relating to intelligent computing and its applica-tions in main tracks and four special sessions and after a rigorous peer-review processwith the help of our program committee members and external reviewers finally weaccepted 143 submissions with an acceptance ratio of 0.48. We received submissionsfrom seven overseas countries.

Dr Vipin Tyagi, Jaypee University of Engg and Tech, Guna, MP conducted aSpecial session on “Cyber Security and Digital Forensics ". Dr. B.N. Biswal, BEC,Bhubaneswar and Prof. Vikrant Bhateja, Sri. Ramswaroop Memorial Group of Profes-sional colleges, Lucknow conducted a special session on “Recent Advancements onComputational intelligence”. “Ad-hoc Wireless Sensor Networks” special session wasorganized by Prof. Pritee Parwekar, ANITS, Vishakapatnam and Dr. S.K. Udgata, Uni-veisity of Hyderabad. A special session on “Advances and Challenges in HumanitarianComputing” was conducted by Prof. Sireesha Rodda, Dept of CSE, GITAM University,Vishakapatnam.

We take this opportunity to thank all Keynote Speakers and Special Session Chairsfor their excellent support to make CSI2014 a grand success.

The quality of a referred volume depends mainly on the expertise and dedicationof the reviewers. We are indebted to the program committee members and externalreviewers who not only produced excellent reviews but also did in short time frames. Wewould also like to thank CSI Hyderabad Chapter, JNTUH and DRDO having comingforward to support us to organize this mega convention.

We express our heartfelt thanks to Mr G. Satheesh Reddy, Director RCI and ProfRameshwar Rao, Vice-Chancellor, JNTUH for their continuous support during thecourse of the convention.

VI Preface

We would also like to thank the authors and participants of this convention, who haveconsidered the convention above all hardships. Finally, we would like to thank all thevolunteers who spent tireless efforts in meeting the deadlines and arranging every detailto make sure that the convention runs smoothly. All the efforts are worth and wouldplease us all, if the readers of this proceedings and participants of this convention foundthe papers and event inspiring and enjoyable.

We place our sincere thanks to the press, print & electronic media for their excellentcoverage of this convention.

December 2014 Volume EditorsDr. Suresh Chandra Satapathy

Dr. A. GovardhanDr. K. Srujan Raju

Dr J.K. Mandal

Team CSI 2014

Chief Patrons

Dr. G. Satheesh Reddy, Director RCI Prof. Rameswara Rao, VC JNTU-H

Advisory Committee

Sri. S. Chandra Sekhar, NASSCOMSri. J. Satyanarayana, A.P. GovtSri. Haripreeth Singh TS GovtSri. Sardar. G.S. Kohli GNITProf. D.V.R. Vithal, Fellow CSIMaj. Gen. R.K. Bagga, Fellow CSIDr. Ashok Agrwal, Fellow CSI

Dr. D.D. Sharma, GNIT Fellow CSISri. H.R Mohan, President CSISri. Sanjay Mahapatra, Secretary CSISri. Ranga Raj Gopal, Treasurer CSIProf. S.V. RaghavanProf. P. Trimurty

Conference Committee

Sri. Bipin MehtaSri. Raju L. KanchibotlaSri. Gautam Mahapatra

Dr. A. GovardhanDr. Srujan Raju K.

Conveners

Sri. K. Mohan Raidu MainProf. C. Sudhakar

Sri. Chandra SekharDr. Chandra Sekhar Reddy

VIII Team CSI 2014

Organizing Committee

Sri. J.A. ChowdaryTelent, SprintSri. R. Srinivasa Rao, Wipro

Dr. H.S. Saini, GNIT

Programme Committee

Dr. A. GovardhanSri. Bipin Chandra IkavyDr. T. Kishen Kumar ReddyProf. N.V. Ramana RaoDr. Srujan Raju K.

Prof. I.L. Narasimha RaoDr. J. SasiKiranProf P.S. AvadhaniSri. Venkatesh ParasuramProf. P. Krishna Reddy IIIT

Finance Committee

Sri. GautamMahapatraProf. C. Sudhakar

Sir. Raj Pakala

Publication Committee

Dr. A. GovardhanDr. S.C. SatapathyDr. Subash C. MishraDr. Anirban Paul

Dr. Srujan Raju K.Dr. VishnuDr. J.K. Mandal

Exhibition Committee

Sri. P.V. RaoSri. RambabukuragantySri. Balaram Varansi

Sri. VenkataRamana Chary G.Mr. Hiteshawar VadlamudiDr. D.V. Ramana

Transport Committee

Sri. RambabuKurigantiSri. Krishna KumarSri K.V. Pantulu

Smt. P. RamadeviSri. Amit Gupta

Hospitality Committee

Sri. Krishna Kumar Tyagarajan Sri. P.V. Rao

Team CSI 2014 IX

Sponsorship Committee

Sri. Raj PakalaSri. Srinivas konda

Sri. PramodJha

Marketing & PR Committee

Sri. KiranCherukuri Ms. Sheila P.

Registrations Committee

Sri Ramesh LoganathanSmt. Rama BhagiSri. Rajeev Rajan KumarSri. Krishna Kumar B.Sri. Sandeep RawatSri. Ram Pendyala

Sri T.N Sanyasi RaoSri D.L. Seshagiri RaoSri G. Vishnu MurthyProf. Ramakrishna PrasadSri. Vijay Sekhar K.S.Dr. SaumyadiptaPyne

Cultural Committee

Smt. Rama BhagiSmt. Rama Devi

International Advisory Committee/Technical Committee

P.K. Patra, IndiaSateesh Pradhan, IndiaJ.V.R. Murthy, IndiaT.R. Dash, KambodiaSangram Samal, IndiaK.K. Mohapatra, IndiaL. Perkin, USASumanth Yenduri, USACarlos A. Coello Coello, MexicoS.S. Pattanaik, IndiaS.G. Ponnambalam, MalaysiaChilukuri K. Mohan, USAM.K. Tiwari, IndiaA. Damodaram, IndiaSachidananda Dehuri, IndiaP.S. Avadhani, IndiaG. Pradhan, IndiaAnupam Shukla, India

Dilip Pratihari, IndiaAmit Kumar, IndiaSrinivas Sethi, IndiaLalitha Bhaskari, IndiaV. Suma, IndiaPritee Parwekar, IndiaPradipta Kumar Das, IndiaDeviprasad Das, IndiaJ.R. Nayak, IndiaA.K. Daniel, IndiaWalid Barhoumi, TunisiaBrojo Kishore Mishra, IndiaMeftah Boudjelal, AlgeriaSudipta Roy, IndiaRavi Subban, IndiaIndrajit Pan, IndiaPrabhakar C.J, IndiaPrateek Agrawal, India

X Team CSI 2014

Igor Belykh, RussiaNilanjan Dey, IndiaSrinivas Kota, NebraskaJitendra Virmani, IndiaShabana Urooj, IndiaChirag Arora, IndiaMukul Misra, IndiaKamlesh Mishra, IndiaMuneswaran, IndiaJ. Suresh, IndiaAm,lan Chakraborthy, IndiaArindam Sarkar, IndiaArp Sarkar, IndiaDevadatta Sinha, IndiaDipendra Nath, IndiaIndranil Sengupta, IndiaMadhumita Sengupta,IndiaMihir N. Mohantry, IndiaB.B. Mishra, IndiaB.B. Pal, IndiaTandra Pal, IndiaUtpal Nandi, IndiaS. Rup, IndiaB.N. Pattnaik, IndiaA Kar, IndiaV.K. Gupta, IndiaShyam lal, IndiaKoushik Majumder, IndiaAbhishek Basu, IndiaP.K. Dutta, India

Md. Abdur Rahaman Sardar, IndiaSarika Sharma, IndiaV.K. Agarwal, IndiaMadhavi Pradhan, IndiaRajani K. Mudi, IndiaSabitha Ramakrishnan, IndiaSireesha Rodda, IndiaSrinivas Sethi, IndiaJitendra Agrawal, IndiaSuresh Limkar, IndiaBapi Raju Surampudi, IndiaS. Mini, IndiaVinod Agarwal, IndiaPrateek Agrwal, IndiaFaiyaz Ahmad, IndiaMusheer Ahmad, IndiaRashid Ali, IndiaA.N. Nagamani, IndiaChirag Arora, IndiaAditya Bagchi, IndiaBalen Basu, IndiaIgor Belykh, RussiaDebasish Jana, IndiaV. Valli Kumari, IndiaDac-Nuuong LeSuneetha Manne, IndiaS. Rattan Kumar, IndiaCh. Seshadri, IndiaSwathi Sharma, IndiaRavi Tomar, India and Many More

Contents

Advances and Challenges in Humanitarian Computing

A Hybrid Approach for Image Edge Detection Using Neural Networkand Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1D. Lakshumu Naidu, Ch. Seshadri Rao, Sureshchandra Satapathy

Studying Gene Ontological Significance of Differentially Expressed Genesin Human Pancreatic Stellate Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Bandana Barman, Anirban Mukhopadhyay

Software Effort Estimation through a Generalized Regression NeuralNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Parasana Sankara Rao, Reddi Kiran Kumar

Alleviating the Effect of Security Vulnerabilities in VANETs throughProximity Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31R.V.S. Lalitha, G. JayaSuma

A GIS Anchored System for Clustering Discrete Data Points –A Connected Graph Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Anirban Chakraborty, J.K. Mandal

Comparative Analysis of Tree Parity Machine and Double HiddenLayer Perceptron Based Session Key Exchange in WirelessCommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Arindam Sarkar, J.K. Mandal

Comparative Analysis of Compression Techniques and FeatureExtraction for Implementing Medical Image Privacy Using SearchableEncryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63J. Hyma, P.V.G.D. Prasad Reddy, A. Damodaram

Offline Detection of P300 in BCI Speller Systems . . . . . . . . . . . . . . . . . . . . . . . 71Mandeep Kaur, A.K. Soni, M. Qasim Rafiq

XII Contents

Nanorobotics Control Systems Design – A New Paradigm for HealthcareSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Sankar Karan, Bhadrani Banerjee, Anvita Tripathi, Dwijesh Dutta Majumder

A Comparative Study on Subspace Methods for Face Recognition underVarying Facial Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93G.P. Hegde, M. Seetha

Recognizing Faces When Images Are Corrupted by Varying Degree ofNoises and Blurring Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Steven Lawrence Fernandes, Josemin G. Bala

Functional Analysis of Mental Stress Based on Physiological Data of GSRSensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Ramesh Sahoo, Srinivas Sethi

Low Power Affordable and Efficient Face Detection in the Presence ofVarious Noises and Blurring Effects on a Single-Board Computer . . . . . . . . . 119Steven Lawrence Fernandes, Josemin G. Bala

The Computational Analysis of Protein – Ligand Docking with DiverseGenetic Algorithm Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129S.V.G. Reddy, K. Thammi Reddy, V. Valli Kumari

An Auto Exposure Algorithm Using Mean Value Basedon Secant Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137V. Krishna Sameera, B. Ravi Kiran, K.V.S.V.N. Raju, B. Chakradhar Rao

Image Authentication in Frequency Domain through G-Let DihedralGroup (IAFD-D3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Madhumita Sengupta, J.K. Mandal, Sakil Ahamed Khan

Information Tracking from Research Papers Using ClassificationTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153J.S.V. Sai Hari Priyanka, J. Sharmila Rani, K.S. Deepthi, T. Kranthi

Sentiment Analysis on Twitter Streaming Data . . . . . . . . . . . . . . . . . . . . . . . . . 161Santhi Chinthala, Ramesh Mande, Suneetha Manne, Sindhura Vemuri

A Secure and Optimal Data Clustering Technique over DistributedNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169M. Yogita Bala, S. Jayaprada

Enabling the Network with Disabled-User-Friendliness . . . . . . . . . . . . . . . . . . 175T.K.S. Lakshmi Priya, S. Ananthalakshmi

Hierarchical Clustering for Sentence Extraction Using Cosine SimilarityMeasure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185D. Kavyasrujana, B. Chakradhar Rao

Contents XIII

Fuzzy System, Image Processing and Software Engg

A Novel Encryption Using One Dimensional Chaotic Maps . . . . . . . . . . . . . . 193Saranya Gokavarapu, S. Vani Kumari

An Empirical Analysis of Agent Oriented Methodologies by Exploitingthe Lifecycle Phases of Each Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205E. Ajith Jubilson, P.M. Joe Prathap, V. Vimal Khanna, P. Dhanavanthini,W. Vinil Dani, A. Gunasekaran

An Expeditious Algorithm for Random Valued Impulse Noise Removal inFingerprint Images Using Basis Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215Mohit Saxena

TERA: A Test Effort Reduction Approach by Using Fault PredictionModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223Inayathulla Mohammed, Silpa Chalichalamala

A Multibiometric Fingerprint Recognition System Based on the Fusion ofMinutiae and Ridges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Madhavi Gudavalli, D. Srinivasa Kumar, S. Viswanadha Raju

Segment Based Image Retrieval Using HSV Color Space and Moment . . . . . 239R. Tamilkodi, R.A. Karthika, G. RoslineNesaKumari, S. Maruthuperumal

Pedestrian with Direction Detection Using the Combination of DecisionTree Learning and SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249G. Santoshi, S.R. Mishra

Application of Radon Transform for Image Segmentation on Level SetMethod Using HKFCM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257R. Nirmala Devi, T. Saikumar

Performance Analysis of Filters to Wavelet for Noisy Remote SensingImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Narayan P. Bhosale, Ramesh R. Manza, K.V. Kale, S.C. Mehrotra

Singer Identification Using MFCC and LPC Coefficients from IndianVideo Songs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275Tushar Ratanpara, Narendra Patel

Role of Clinical Attributes in Automatic Classification of Mammograms . . . 283Aparna Bhale, Manish Joshi, Yogita Patil

Quantifying Poka-Yoke in HQLS: A New Approach for High Quality inLarge Scale Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293K.K. Baseer, A. Rama Mohan Reddy, C. Shoba Bindu

XIV Contents

SIRIUS-WUEP: A Heuristic-Based Framework for Measuring andEvaluating Web Usability in Model-Driven Web Development . . . . . . . . . . . . 303S. Sai Aparna, K.K. Baseer

Implementation of Secure Biometric Fuzzy Vault Using Personal ImageIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Sarika Khandelwal, P.C. Gupta

Robust Pattern Recognition Algorithm for Artifacts Elimination inDigital Radiography Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321Igor Belykh

Hull Detection from Handwritten Digit Image . . . . . . . . . . . . . . . . . . . . . . . . . 329Sriraman Kothuri, Mattupalli Komal Teja

Learning Approach for Offline Signature Verification Using VectorQuantization Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Aarti Chugh, Charu Jain, Priti Singh, Preeti Rana

Fuzzified Inverse S-Transform for Identification of Industrial NonlinearLoads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Srikanth Pullabhatla, Chiranjib Koley

Bandwidth Allocation Scheme in Wimax Using Fuzzy Logic . . . . . . . . . . . . . . 355Akashdeep

GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363R. Thiruvengatanadhan, P. Dhanalakshmi, S. Palanivel

Heart Disease Prediction System Using Data Mining Technique by FuzzyK-NN Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371V. Krishnaiah, G. Narsimha, N. Subhash Chandra

Applying NLP Techniques to Semantic Document Retrieval Applicationfor Personal Desktop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385D.S.R. Naveenkumar, M. Kranthi Kiran, K. Thammi Reddy, V. Sreenivas Raju

Cyber Security, Digital Forensic and Ubiquitous Computing

An Adaptive Edge-Preserving Image Denoising Using Epsilon-MedianFiltering in Tetrolet Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393Paras Jain, Vipin Tyagi

A Comparison of Buyer-Seller Watermarking Protocol (BSWP) Basedon Discrete Cosine Transform (DCT) and Discrete Wavelet Transform(DWT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401Ashwani Kumar, S.P. Ghrera, Vipin Tyagi

Contents XV

Log Analysis Based Intrusion Prediction System . . . . . . . . . . . . . . . . . . . . . . . 409Rakesh P. Menon

Multi-objective k-Center Sum Clustering Problem . . . . . . . . . . . . . . . . . . . . . . 417Soumen Atta, Priya Ranjan Sinha Mahapatra

Reliability Aware Load Balancing Algorithm for Content DeliveryNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427Punit Gupta, Mayank Kumar Goyal, Nikhil Gupta

SQL Injection Detection and Correction Using Machine LearningTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435Garima Singh, Dev Kant, Unique Gangwar, Akhilesh Pratap Singh

An Enhanced Ontology Based Measure of Similarity between Words andSemantic Similarity Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443M. Uma Devi, G. Meera Gandhi

Intelligent Traffic Monitoring Using Internet of Things (IoT) withSemantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455Manuj Darbari, Diwakar Yagyasen, Anurag Tiwari

Development of a Real-Time Lane Departure Warning System for DriverAssistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463N. Balaji, K. Babulu, M. Hema

Visual Simulation Application for Hardware In-Loop Simulation (HILS)of Aerospace Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473Pramod Kumar Jha, Chander Shekhar, L. Sobhan Kumar

Multiprotocol Label Switching Feedback Protocol for Per Hop BasedFeedback Mechanism in MPLS Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481Ankur Dumka, Hardwari Lal Mandoria

Towards Improving Automated Evaluation of Java Program . . . . . . . . . . . . . 489Aditya Patel, Dhaval Panchal, Manan Shah

Fault Tolerant Scheduling - Dual Redundancy in an Automotive CruiseControl System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497Manne Lakshmisowjanya, Annam Swetha, V. Radhamani Pillay

A Simple Mathematical Model for Performance Evaluation of FiniteBuffer Size Nodes in Non- Saturated IEEE 802.11 DCF in Ad HocNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505Neeraj Gupta, C.S. Rai

An Efficient Algorithm and a Generic Approach to Reduce Page FaultRate and Access Time Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513Riaz Shaik, M. Momin Pasha

XVI Contents

A Cryptography Scheme through Cascaded Session Based SymmetricKeys for Ubiquitous Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525Manas Paul, J.K. Mandal

E-Commerce, ICT Applications, Big Data and CloudComputing

A Comparative Study of Performance Evaluation of Services in CloudComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533L. Aruna, M. Aramudhan

A Case Study: Embedding ICT for Effective Classroom Teaching &Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541Sandeep Vasant, Bipin Mehta

Cloud Based Virtual Agriculture Marketing and Information System(C-VAMIS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549A. Satheesh, D. Christy Sujatha, T.K.S. Lakshmipriya, D. Kumar

An Adaptive Approach of Tamil Character Recognition Using DeepLearning with Big Data-A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557R. Jagadeesh Kannan, S. Subramanian

Tackling Supply Chain Management through Business Analytics:Opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Prashant R. Nair

A Multiple Search and Similarity Result Merging Approach for WebSearch Improvisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577Vijayalakshmi Kakulapati, Sudarson Jena, Rajeswara Rao

System for the Detection and Reporting of Cardiac Event UsingEmbedded Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587Maheswari Arumugam, Arun Kumar Sangaiah

An Early Warning System to Prevent Human Elephant Conflict andTracking of Elephant Using Seismic Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . 595D. Jerline Sheebha Anni, Arun Kumar Sangaiah

Impact of ICT Infrastructure Capability on E-Governance Performance:Proposing an Analytical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603Deepak Dahiya, Saji K. Mathew

Towards Identifying the Knowledge Codification Effects on the FactorsAffecting Knowledge Transfer Effectiveness in the Context of GSDProject Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611Jagadeesh Gopal, Arun Kumar Sangaiah, Anirban Basu, Ch. Pradeep Reddy

Contents XVII

Formulation and Computation of Animal Feed Mix:Optimization by Combination of Mathematical Programming . . . . . . . . . . . . 621Pratiksha Saxena, Neha Khanna

Resource Grid Architecture for Multi Cloud Resource Management inCloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631Chittineni Aruna, R. Siva Ram Prasad

An Efficient Framework for Building Fuzzy Associative Classifier UsingHigh-Dimensional Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641S. Naresh, M. Vijaya Bharathi, Sireesha Rodda

A Survey on Access Control Models in Cloud Computing . . . . . . . . . . . . . . . . 653RajaniKanth Aluvalu, Lakshmi Muddana

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665

© Springer International Publishing Switzerland 2015 S.C. Satapathy et al. (eds.), Emerging ICT for Bridging the Future – Volume 1,

1

Advances in Intelligent Systems and Computing 337, DOI: 10.1007/978-3-319-13728-5_1

A Hybrid Approach for Image Edge Detection Using Neural Network and Particle Swarm Optimization

D. Lakshumu Naidu, Ch. Seshadri Rao, and Sureshchandra Satapathy

Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakhapatnam, Andhra Pradesh, India {lakshumu,sureshsatapathy}@gmail.com,

seshadri.rao.cse@anits.edu.in

Abstract. An Edge of an image is a sudden change in the intensity of an image. Edge detection is process of finding the edges of an image. Edge detection is one of the image preprocessing techniques which significantly reduces the amount of data and eliminates the useless information by processing the important structural properties in an image. There are many traditional algorithms used to detect the edges of an image. Some of the important algorithms are Sobel, Prewitt, Canny, Roberts etc. A Hybrid approach for Image edge detection using Neural Networks and Particle swarm optimization is a novel algorithm to find the edges of image. The training of neural networks follows back propagation approach with particle swarm optimization as a weight updating function. 16 visual patterns of four bit length are used to train the neural network. The optimized weights generated from neural network training are used in the testing process in order to get the edges of an image.

Keywords: Image edge detcion, Image processing, Artificial Neural Networks, Particle swarm optimization.

1 Introduction

Edges in images are the curves that characterize the boundaries (or borders) of objects. Edges contain important information of objects such as shapes and locations, and are often used to distinguish different objects and/or separate them from the background in a scene. In image processing, edge detection can be employed to filter out less relevant information while preserving the basic structural properties of an image. This image data can be used for further preprocessing steps like extraction of features, image segmentation, interpretation and registration of an image.

A sudden change in intensity of an image is an edge. Edge detection is a process of finding the sudden intensity discontinues in an image. Slow changes refer to small value of derivatives and fast changes refer to large values of derivatives. Dimensional spatial filters or the gradient operator uses this principle to find the edges. This type of filters detects the gradient of image intensity without considering the uniform regions (i.e., the area with contrast intensity) in the image. There are different types of filters [1] have been developed to detect different types of edges in the image. The classical operators like Sobel, prewitt, kirsch detect edges in all directions such as horizontal,

2 D. Lakshumu Naidu, Ch. Seshadri Rao, and S. Satapathy

vertical and diagonal etc. Laplacian of Gaussian (LOG) operator finds the correct places of edges but cannot find edges in corners and curves. Though canny and Shen-Castan detects edges even in noise conditions which involves complex computations and more time to execute. Identifying the correct threshold value is the crucial part for all these algorithms. There is no appropriate method to find threshold value.

2 Related Work

Artificial neural networks (ANN) used in many area and also used for edge detection. Neural networks are used a Non linear filter to detect the edges of an image. In [2], Terry and Vu introduced a multi-layer feed forward neural network which is used to detect the edges of a laser radar image of a bridge. Synthetic edge patterns are used to train the networks. The network can detect different types of edges like horizontal, vertical and diagonal and so on. Li and Wang developed a new neural network detector which is applied on 8 bit sub image of an image. After completion of the entire image the results combined and produce the final edges of image. This approach does not provide good training set and increases the overhead.

Hamed Mehrara[4], Mohammad Zahedinejad [5] & [6] developed and produced new training data set with 16 different visual patterns which can be used to detect any type of edges. Initially the gray-scale images are converted into binary form and these binary images are sent to neural networks. Since binary image have only two values 1 and 0, specifies high and low intensities so that we can easily detect the edges. This method suffers from two problems called initialization of weights to the network and selection of threshold for converting image into binary image.

In this research we are training the neural networks using Particle Swarm Optimization instead of Using Back propagation algorithm. As Neural network is good localization and PSO is good for exploration of search space. Neural network produces good results based on selection of initial parameters like connected weights etc. So we can use Particle Swarm Optimization to get the best optimized weights. No need to concentrate on initial weights of ANN.

2.1 Neural Networks

An Artificial Neural Network (ANN) is an interconnected network of neurons which process the data in parallel.ANN is motivated from human brain which is highly complex parallel computer. According to [7] & [8] network has set of inputs and outputs which are used to propagate the calculations at each neuron i.e. output of the one neuron can be input to some other neuron. Each neuron is associated with an input and weight. The weights of the network are represents knowledge and the learning of the network involves updating of these weights. Neural networks are used to detect the patterns of the data.

The performance of any neural network training depends on many parameters like activation function used, type of network architecture, learning algorithm used etc.

A Hybrid Approach for Image Edge Detection Using Neural Network 3

2.2 Particle Swarm Optimization

Particle swarm optimization is a population based algorithm which improves the overall performance of a system by interactions of all the particles.PSO was first proposed and developed by Kennedy J. and Eberhart R. C [9]. They explained that each particle in the population maintains its best solution that has been achieved so far by that particle called as pbest. The overall best value of the neighboring particles so far is called gbest.

According to James Kennedy [9] the particle position can be modified by using the following equations.

Vi(t+1)=w*Vi(t)+c1*Rand1()*(pbest(i)-Xi(t))+c2*Rand1()*(gbest-Xi(t) ) (1)

Xi (t+1) =Xi (t) +Vi (t+1) (2)

Here c1 and c2 are constants and Rand1 () and Rand2 () are automatically generated random numbers within the range from 0 to 1.Eq(1) is used to find the velocity of particle which is used to speed up the process by adding it to current position as in eq(2). Eq(1) contains two parts first part is for personnel enhancement of particle and second part for overall or global enhancement of the population.

3 Proposed System

The proposed System contains two phases. First Phase contains the training of the neural networks using particle swarm optimization. In this phase a 4 bit length 16 visual patterns are used as a training data. The weight updating is done using particle swarm optimization. Second phase contains the testing phase. In this phase we will submit the binary image of window size 2X2 to the trained neural network. It produces the edges of an image.

The novel algorithm contains the following modules to train and produce the edges of an image.

3.1 Network Architecture

There are different types of neural network architectures exist. We are using a multi layer feed forward network of four input neurons, one hidden layer with ‘n’ no of user defined neurons and four output neurons.

3.2 Learning Algorithm

A Supervised learning algorithm is used to train the neural networks. It follows a back propagation approach and which uses bipolar function as activation function. Here we are using Particle swarm optimization for weight updating. For the better performance the size of the swarm is 10. Mean squared error is used as fitness function. The basic working of how we update the weights of neural networks is explained by the following diagram.

4 D. Lakshumu Naidu, Ch. Seshadri Rao, and S. Satapathy

Fig. 1. PSO for weight updating

3.3 Training Data

Training data set is the basic key for any neural network training process. The following 16 visual patterns are used to train the neural networks to get the edges of an image.

Fig. 2. Visual patterns for training neural networks

A Hybrid Approach for Image Edge Detection Using Neural Network 5

The training pattern and its corresponding edges are explained in the following table.

Table 1. visual patterns for training

Training Inputs

Detected As Training Output

0 0 0 0

None edge 1 1 1 1

0 0 0 1

Corner edge 1 0 0 1

0 0 1 0

Corner edge 0 1 1 0

0 0 1 1

Horizontal edge 0 0 1 1

0 1 0 0

Corner edge 0 1 1 0

0 1 0 1 Parallel edge 0 1 0 1

0 1 1 0

Diagonal edge 0 1 1 0

0 1 1 1

Pseudo noise 1 1 1 1

1 0 0 0

Corner edge 1 0 0 1

1 0 0 1

Diagonal edge 1 0 0 1

1 0 1 0

Parallel edge 1 0 1 0

1 0 1 1

Pseudo noise 1 1 1 1

1 1 0 0

Horizontal edge 1 1 0 0

1 1 0 1 Pseudo noise 1 1 1 1

1 1 1 0 Pseudo noise 1 1 1 1

1 1 1 1 None edge 1 1 1 1

3.4 Testing Phase

The updated weights are used for testing and applied on binary image to get the edges. Testing Phase involves two major components. Converting the given image into binary image and eliminating the noisy edges.

6 D. Lakshumu Naidu, Ch. Seshadri Rao, and S. Satapathy

3.4.1 Conversion of Original Image into Binary Image The original image is first converted into gray scale image and then global threshold is applied on it and a binary image is produced as a result. This binary image is send to neural network with 2X2 window size.

3.4.2 Elimination of Noise The proposed algorithm eliminates the noisy edges of an image by using White wasshing method. White washing is nothing but replacing the white(1) bits for all noisy edges and none edges. All the nosiy edges and none edges are shown in the above table.

3.5 Architecture of Proposed System

The following diagram explains the working process of proposed system.

Fig. 3. Proposed System Architecture

3.6 Algorithm for the Proposed System

Input: 16 visual patterns and binary image. Output: Optimized weights, Edges of an image. Step 1: Initialize the positions (weight and bias) and velocities of a group of particles randomly. Step 2: The PSO-NN is trained using the initial particles position. Step 3: Calculate the mean Square error (MSE) produced from BP neural network can be treated as particles fitness value according to initial weight and bias. Step 4: The learning error is reduced by changing the particles position, which will update the weight and bias of the network.

(i) The “pbest” value (each particle’s MSE so far) and (ii) The “gbest” value (lowest learning MSE found in entire learning process so far) are applied to the velocity update equation (Eq. 1) to produce a value for positions adjustment to the best solution or targeted learning error.

A Hybrid Approach for Image Edge Detection Using Neural Network 7

Step 5: The new sets of positions (NN weight and bias) are produced by adding the calculated velocity value to the current position value using movement equations (Eq. 1 & Eq. 2). Then, the new sets of positions are used to produce new Learning error in feed-forward NN. Step 6: This process terminates if the stopping conditions either minimum learning error or maximum number of iteration are met. Step 7: Save the optimized weights, this can be used for further processing. Step 8: Find the edges of binary image by using the optimized weights in NN testing process.

4 Experimental Results

Initially we find the edges of an image using Neural network using general back propagation algorithm and after that we use NN-PSO detector to find the edges of an image. Here we take the value for the constants c1=c2=2.0,w=0.729844.After some iterations the the fitness becomes constant and the detector produces optimized set of weights.The output for the basic lena image as shown in the following figure.

Fig. 4. The edges of lena image using BP-NN and PSO-NN edge detectors

The following table shows how the entropy and no edges for lena image for both edge detectors.

Table 2. comparision of BP-NN and PSO-NN edge detectors.

Edge detector Entropy No of edges

BP-NN 1.2335 50625

PSO-NN 0.2892 50625

In the above table the entropy is the basic operator to differentiate the efficiency of two algorithms. An entropy is a statistical measure of randomness that can be used to

8 D. Lakshumu Naidu, Ch. Seshadri Rao, and S. Satapathy

characterize the texture of the input image. Entropy can be calculated by using the following formula

Entropy= - ∑ ( )

Where pi is the probability of difference between two adjacent pixels is equal to i. The entropy value using PSO-NN is very less when compared to entropy value of

BP-NN (clearly seen from Table 2), indicating that the noise is removed and edges are nicely detected with more information of the pixel as discussed in the proposed edge-detection process.

5 Conclusion

The particle swarm optimization enhances in exploration of the search space and eliminates the weight intialization problem of neural networks. The PSO-NN algorithm produces the optimized set of weights which are used to detect the edges of an image. This algorithm produces same no of edges as BP-NN with less entropy. In this paper global threshold is used for converting the given image into binary image.we can also extend our future work for converting the image into binary using local threshold and can find the edges of color images too. As the neural networks with backpropagation involves more complex operations the optimiztion techniques clearly reduce mathematically computational overhead so we can also extend the work to recent optimization techinique like TLBO.

References

1. Maini, R., Aggarwal, H.: Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing (IJIP) 3(1)

2. Terry, P., Vu, D.: Edge Detection Using Neural Networks. In: Conference Record of the Twenty-seventh Asilomar Conference on Signals, System99999s and Computers, pp. 391–395 (November 1993)

3. Li, W., Wang, C., Wang, Q., Chen, G.: An Edge Detection Method Based on Optimized BP Neural Network. In: Proceedings of the International Symposium on Information Science and Engineering, pp. 40–44 (December 2008)

4. He, Z., Siyal, M.: Edge Detection with BP Neural Networks. In: Proceedings of the International Conference on Signal Processing, pp. 1382–1384 (1998)

5. Mehrara, H., Zahedinejad, M., Pourmohammad, A.: Novel Edge Detection Using BP Neural Network Based on Threshold Binarization. In: Proceedings of the Second International Conference on Computer and Electrical Engineering, pp. 408–412 (December 2009)

6. Mehrara, H., Zahedinejad, M.: Quad-pixel edge detection using neural network. Journal of Advances in Computer Research 2(2), 47–54 (2011)

7. Graupe, D.: Principle of Artificial Neural Networks. World Scientific Publishing Co. Pte. Lte. (2007)

8. Du, K.L., Swamy, M.N.: Neural Network in Soft Computing. Springer (2006) 9. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE

International Conference on NN, Piscataway, pp. 1942–1948 (1995)

A Hybrid Approach for Image Edge Detection Using Neural Network 9

10. Lu, D., Yu, X.-H., Jin, X., Li, B., Chen, Q., Zhu, J.: Neural Network Based Edge Detection for Automated Medical Diagnosis

11. Settles, M., Rylander, B.: Neural Network Learning using Particle Swarm Optimizers. In: Advances in Information Science and Soft Computing, pp. 224–226 (2002)

12. Shi, Y.: Particle Swarm Optimization. Electronic Data Systems, Inc. Kokomo, IN 46902, USA Feature Article, IEEE Neural Networks Society (February 2004)

13. Zhao, L., Hu, H., Wei, D., Wang, S.: Multilayer forward artificial neural network. Yellow River Conservancy Press, Zhengzhou (1999)

14. Marcio, C., Teresa, B.L.: An Analysis Of PSO Hybrid Algorithms For Feed-Forward Neural Networks Training. Center of Informatics, Federal University of Pernambuco, Brazil (2006)

15. Pärt-Enander, E.: The MATLAB handbook. Addison-Wesley, Harlow (1996) 16. Stamatios, C., Dmitry, N., Charles, T., Alexander, C., et al.: A Character Recognition

Study Using a Biologically Plausible Neural Network of the Mammalian Visual System, pp. D2.1-D2.10-D12D12.10. Pace University (2011)

17. Gonzalez, R., Woods, R.: Digital image processing, 2nd edn., pp. 567–612. Prentice-Hall Inc. (2002)

18. Argyle, E.: Techniques for edge detection. Proc. IEEE 59, 285–286 (1971) 19. Vincent, O.R., Folorunso, O.: A Descriptive Algorithm for Sobel Image Edge Detection.

Clausthal University of Technology, Germany

Studying Gene Ontological Significance

of Differentially Expressed Genes in HumanPancreatic Stellate Cell

Bandana Barman1,� and Anirban Mukhopadhyay2

1 Department of Electronics and Communication Engineering,Kalyani Government Engineering College,

Kalyani, Nadia, West Bengal, India2 Department of Computer Science and Engineering,

University of Kalyani, Kalyani,Nadia, West Bengal, Indiabandanabarman@gmail.com,

anirban@klyuniv.ac.in

Abstract. In this paper, we studied and analyzed the significant ontolo-gies by gene ontology in which the differentially expressed genes (DEG)of human pancreatic stellate cell participate. We identified up-regulatedand down-regulated differentially expressed genes between dose responseand time course gene expression data after retinoic acid treatment ofhuman pancreatic stellate cells. We first perform statistical t-test andcalculate false discovery rate (FDR) then compute quantile value of testand found minimum FDR. We set the pvalue cutoff at 0.02 as thresholdand get 213 up-regulated (increased in expression) genes and 99 down-regulated (decreased in expression) genes and analyzed the significantGO terms.

Keywords: Microarray data, p-value, false discovery data, q-value, nor-malization, permutation test, differential gene expression, Gene Ontology(GO).

1 Introduction

If a gene is statistically and biologically significant, then the gene is consideredas differentially expressed between two samples. The ratio of expression levels ofdifferent samples and variability is a measure of differential expression. The qual-ity control of microarray data improves differentially expressed gene [8] detectionand it is also a useful analysis of data. The stratification-based tight clusteringalgorithm, principal component analysis and information pooling method areused to identify differentially expressed genes in small microarray experiments.To detect DEG for RNA-seq data, the comparison of two Poisson means (rates)are determined [4].

� Corresponding author.

c© Springer International Publishing Switzerland 2015 11S.C. Satapathy et al. (eds.), Emerging ICT for Bridging the Future – Volume 1,Advances in Intelligent Systems and Computing 337, DOI: 10.1007/978-3-319-13728-5_2

12 B. Barman and A. Mukhopadhyay

The statistical hypothesis test is a method of statistical inference which usesdata from a scientific study. Statistically, a result is significant if it has been pre-dicted as unlikely have occurred by chance alone according to the significancelevel. The significance level is a pre-determined threshold probability. The hy-pothesis testings are two types: parametric and non-parametric. The parametricmethod is based on assuming a particular underlying population distribution.For large sample it is used without that assumption. The nonparametric methodcan be used without assuming a distribution and often not as “powerful” as para-metric methods. Z-test, t-test, One-way ANOVA test are the parametric testsand Wilcoxon rank sum test, Kruskal Wallis H-test[7] are the non-parametrictest. In Wilcoxon rank sum test, heuristic idealized discriminator methods areused to identify diffentialy expressed genes [11]. The pre-processing steps forimage analysis and normalization is introduced to identify DEG. The DEG areidentified based on adjusted p-values. It is a statistical method for identificationof diferentially expressed genes in replicated cDNA microarray experiments [5].To perform testing, data are used after its normalization. Each differentially ex-pressed gene may has univariate testing problem and it is corrected by adjustedpvalue. For finding DEG between two sample conditions, the principal compo-nent analysis (PCA) space and classification of genes are done based on theirposition relative to a direction on PC space representing each condition [10].

Identification of differentially expressed genes (DEG) with understandingwrong or fixed under conditions (cancer, stress etc.) is the main key and mes-sage in these genes. It is extensively precessed and modified prior to translation[1]. The DEG may considered as features for a classifier and it is also served asstarting point of a model. To identify differentially expressed genes a maskingprocedure is used. There cross-species data set and gene-set analysis are stud-ied. The ToTS i.e Test of Test Statistics and GESA i.e. Gene Set EnrichmentAnalysis were also investigated there [3].

The Gene Ontology is three structured, controlled vocabularies (ontologies)which describe gene products in terms of their associated biological processes,cellular components and molecular functions in a species-independent manner.The ontologies develop and maintain ontologies themselves [9]. The gene prod-ucts annotation entails making associations between ontologies and genes andcollaborating databases. We identified differentially expressed genes from mi-croarray gene expression data both dose response and time response gene ex-pression data after retinoic acid treatment of human pancreatic stellate cells.Then we study and analyze the identified both up-regulated and down-regulatedsignificant genes with gene ontologies (GO).

2 Material

The gene expression profile measures the activity of genes at a instance and itcreates a global picture of cellular function. The RNA-Seq is the next generationsequencing. We implement our approach in expression profiling by array typemicroarray data. We collect data set from website, http://www.ncbi.nlm.nih.

Studying Gene Ontological Significance 13

gov.in/geodata/GSE14427.The data represents the change in expression of genesafter retinoic acid treatment of human pancreatic stellate cells. It is a superseriesof the subseries, GSE14426 and GSE14425. In GSE14425 a pancreatic stellatecell line is treated on plastic or matrigel with 1 or 10 micromolar dose of all-transretinoic acid (ATRA). Then RNA was extracted and hybridized on IlluminaHuman microarrays. Then the target genes regulated by ATRA and evaluatedfor dose repsonse. Those RNA expression values are taken and changed due tobackground culture conditions. In GSE14426 data, same steps are maintainedfor timepoints of 30 mins, 4 hours, 12 hours, 24 hours and 168 hours.

3 Methods

After collecting data, we first perform normalization. It is done by dividing themean column intensity and it scales the values in each column of microarraydata. Its output is a matrix of normalized microarray data. Several statisticalmethods are used to identify the differentially expressed genes (DEG). Thosemethods are Student’s t-test, T-test, Linear regression model, Nonparametrictest (Wilcoxon, or rank-sums test), SAM etc. We perform statistical t-test tofind DEG from the sample microarrays. This is also a statistical hypothesis test.We are describing the mathematical concept of this test as follows:

The size of our used samples are not equal. The number of genes present ineach sample is 48701 but in data of dose response the number of expressionvalues for each gene is 4 and in time response data the expressed values for eachgene is 6. So the size of sample groups are 48701× 4 and 48701× 6 respectively.The standard formula for the t-test is as follows:

t =X1 −X2√var1n1

+ var2n2

(1)

Where, X1, X2 denote mean of two samples, standard error of the difference

between the means is√

var1n1

+ var2n2

, var1 and var2 are the variances of two

samples, n1, n2 are number of population in samples respectively. We then findfalse discovery rate (FDR) and estimate the result statistically. To control theexpected proportion of incorrectly rejected null hypotheses i.e. false discoveriesfrom a list of findings, FDR procedures are used. It is given by

FalseDiscoveryRate = Estimationno.ofFalseDiscoveries

no.ofRejectedDiscoveries(2)

The FDR is kept below a threshold value i.e. quantile value (q-value). If X1 islarger than , X2, then t-value will be positive and negative if it is smaller. In thet-test, the degrees of freedom (DOF) is the sum of population in both samplegroups minus 2. A table of significance in t-test is use to test whether the ratio islarge enough to say that the difference between the groups is not likely to havebeen a chance of finding. For testing, the significance risk level (called alpha