Advances in Intelligent Systems and Computing 736

Ajith Abraham · Pranab Kr. Muhuri Azah Kamilah Muda · Niketa Gandhi Editors

Intelligent Systems Design and Applications17th International Conference on Intelligent Systems Design and Applications (ISDA 2017) Held in Delhi, India, December 14–16, 2017

Advances in Intelligent Systems and Computing

Volume 736

Series editor

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

The series “Advances in Intelligent Systems and Computing” contains publications on theory,applications, and design methods of Intelligent Systems and Intelligent Computing. Virtuallyall disciplines such as engineering, natural sciences, computer and information science, ICT,economics, business, e-commerce, environment, healthcare, life science are covered. The listof topics 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 coversignificant recent developments in the field, both of a foundational and applicable character.An important characteristic feature of the series is the short publication time and world-widedistribution. 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 Perez, 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, Győr, 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 Kong

e-mail: jwang@mae.cuhk.edu.hk

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

Ajith Abraham • Pranab Kr. MuhuriAzah Kamilah Muda • Niketa GandhiEditors

Intelligent SystemsDesign and Applications17th International Conference on IntelligentSystems Design and Applications(ISDA 2017) Held in Delhi, India,December 14–16, 2017

123

EditorsAjith AbrahamMachine Intelligence Research LabsAuburn, WAUSA

Pranab Kr. MuhuriDepartment of Computer ScienceSouth Asian UniversityChanakyapuri, DelhiIndia

Azah Kamilah MudaFaculty of Information and CommunicationTechnology

Universiti Teknikal Malaysia MelakaDurian Tunggal, MelakaMalaysia

Niketa GandhiMachine Intelligence Research LabsAuburn, WAUSA

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-319-76347-7 ISBN 978-3-319-76348-4 (eBook)https://doi.org/10.1007/978-3-319-76348-4

Library of Congress Control Number: 2018935895

© Springer International Publishing AG, part of Springer Nature 2018This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective 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 thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

Printed on acid-free paper

This Springer imprint is published by the registered company Springer International Publishing AGpart of Springer NatureThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Welcome to the Proceedings of the 17th International Conference on IntelligentSystems Design and Applications (ISDA17), which was held in South AsianUniversity, Delhi, India, during December 14–16, 2017. ISDA 2017 is jointlyorganized by the Machine Intelligence Research Labs (MIR Labs), USA, and SouthAsian University, Delhi, India.

ISDA 2017 brings together researchers, engineers, developers, and practitionersfrom academia and industry working in all interdisciplinary areas of intelligentsystems and system engineering to share their experiences, and to exchange andcross-fertilize their ideas. The aim of ISDA 2017 is to serve as a forum for thedissemination of state-of-the-art research and development of intelligent systems,intelligent technologies, and applications. ISDA 2017 was organized in conjunctionwith the 7th World Congress on Information and Communication Technologies(WICT 2017).

The themes of the contributions and scientific sessions range from theories toapplications, reflecting a wide spectrum of the coverage of intelligent systems andcomputational intelligence areas. ISDA 2017 received submissions from over 30countries, and each paper was reviewed by at least 5 reviewers in a standardpeer-review process. Based on the recommendation by 5 independent referees,finally about 100 papers were accepted for publication in the proceedings publishedby Springer, Verlag.

Many people have collaborated and worked hard to produce the successful ISDA2017 conference. First, we would like to thank all the authors for submitting theirpapers to the conference, for their presentations and discussions during the con-ference. Our thanks go to Program Committee members and reviewers, who carriedout the most difficult work by carefully evaluating the submitted papers. Our specialthanks to Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexico, andAlexander Gelbukh, Instituto Politécnico Nacional, Mexico City, Mexico, for theexciting plenary talks.

v

We express our sincere thanks to special session chairs and organizing com-mittee chairs for helping us to formulate a rich technical program.

Ajith AbrahamPranab Kr. Muhuri

ISDA 2017 - General Chairs

vi Preface

ISDA 2017 Organization

General Chairs

Ajith Abraham Machine Intelligence Research Labs, USAPranab Kr. Muhuri South Asian University, Delhi, India

Program Committee Co-chairs

Simone Ludwig North Dakota State University, USAAswani Kumar VIT University, Vellore, IndiaPunam Bedi University of Delhi, IndiaMillie Pant Indian Institute of Technology Roorkee, IndiaAntonio J. Tallón-Ballesteros University of Seville, Spain

Advisory Board

Albert Zomaya The University of Sydney, AustraliaAndre Ponce de Leon

F. de CarvalhoUniversity of Sao Paulo at Sao Carlos, Brazil

Bruno Apolloni University of Milano, ItalyHideyuki Takagi Kyushu University, JapanImre J. Rudas Óbuda University, HungaryJanusz Kacprzyk Polish Academy of Sciences, PolandJavier Montero Complutense University of Madrid, SpainKrzysztof Cios Virginia Commonwealth University, USAMarina Gavrilova University of Calgary, CanadaMario Koeppen Kyushu Institute of Technology, JapanMohammad Ishak Desa Universiti Teknikal Malaysia Melaka, MalaysiaPatrick Siarry Université Paris-Est Créteil, FranceRonald Yager Iona College, USA

vii

Salah Al-Sharhan Gulf University of Science and Technology,Kuwait

Sebastian Ventura University of Cordoba, SpainVincenzo Piuri Università degli Studi di Milano, Italy

Publication Chairs

Azah Kamilah Muda UTeM, MalaysiaNiketa Gandhi Machine Intelligence Research Labs, USA

Local Organizing Committee

Q. M. Danish Lohani South Asian University, Indiadanishlohani@cs.sau.ac.in

Local Organizing Committee Members

Amit K. Shukla South Asian University, Delhi, IndiaAshraf Zubair South Asian University, Delhi, IndiaManvendra Janmaijaya South Asian University, Delhi, IndiaAmit Rauniyar South Asian University, Delhi, IndiaRahul Nath South Asian University, Delhi, IndiaSandeep Kumar South Asian University, Delhi, IndiaTaniya Seth South Asian University, Delhi, IndiaDeepika Malhotra South Asian University, Delhi, India

Web Service

Kun Ma University of Jinan, China

International Program Committee

Ajith Abraham Machine Intelligence Research Labs, USAAkila Muthuramalingam KPR Institute of Engineering and Technology,

IndiaAlberto Cano University of Córdoba, SpainAmiya Tripathy Don Bosco Institute of Technology, Mumbai,

IndiaAndrzej Skowron Warsaw University of Technology, PolandAnna Jordanous University of Kent, UKAntonio J. Tallón Ballesteros Universidad de Sevilla, Spain

viii ISDA 2017 Organization

Aswani Cherukuri Vellore Institute of Technology, IndiaBharanidharan Shanmugam Universiti Teknologi Malaysia, MalaysiaBin Li University of Science and Technology of China,

ChinaCarlos Pereira Instituto Superior de Engenharia de Coimbra,

PortugalCerasela Crisan “Vasile Alecsandri” University of Bacau,

RomaniaCésar Hervás Martínez University of Córdoba, SpainChao Chun Chen Southern Taiwan University of Science

and Technology, TaiwanChin-Shiuh Shieh National Kaohsiung University of Applied

Sciences, TaiwanDaniela Zaharie West University of Timisoara, RomaniaDiaf Moussa Université Mouloud Mammeri, AlgeriaDilip Pratihar Indian Institute of Technology Roorkee, IndiaEduardo Solteiro Pires University of Trás-os-Montes and Alto Douro,

PortugalEfrén Mezura Montes Universidad Veracruzana, MexicoEiji Uchino Yamaguchi University, JapanElizabeth Goldbarg Universidade Federal do Rio Grande do Norte,

BrazilEnrique Dominguez Universidad de Málaga, SpainFabrício Olivetti de França Universidade Federal do ABC, BrazilFedja Netjasov University of Belgrade, SerbiaJosé Francisco Martínez

TrinidadNational Institute of Astrophysics, Optics

and Electronics, Puebla, MexicoGagandeep Kaur JIIT, Noida, IndiaGeorg Peters Munich University of Applied Sciences,

GermanyHector Benitez-Perez Universidad Nacional Autónoma de México,

MexicoHeder Bernardino Universidade Federal de Juiz de For a, BrazilHema Banati University of Delhi, IndiaHiroshi Dozono Saga University, JapanIlhem Kallel École Nationale d’Ingénieurs de Sfax, TunisiaIsabel Barbancho Universidad de Málaga, SpainIsabel S. Jesus Instituto Superior de Engenharia do Porto,

PortugalJanos Botzheim Tokyo Metropolitan University, JapanJerzy Grzymala Busse University of Kansas, USAJolanta Mizera-Pietraszko Opole University, PolandKelemen Arpad University of Maryland, USAKeun Ho Ryu Chungbuk National University, South KoreaKonstantinos Parsopoulos University of Ioannina, Greece

ISDA 2017 Organization ix

Korhan Karabulut Yaşar Üniversitesi, TurkeyKyriakos Kritikos Foundation for Research and Technology

(FORTH) Hellas, GreeceLaurence Amaral Universidade Federal de Uberlândia, BrazilLee Chang Yong Kongju National University, South KoreaLeocadio G. Casado University of Almería, SpainLeticia Hernando The University of the Basque Country, SpainLin Wang Jinan University, ChinaLubna Gabralla Sudan University of Science and Technology,

SudanLudwig Simone North Dakota State University, USALuigi Troiano University of Sannio, ItalyMatthias Becker Leibniz Universität Hannover, GermanyMauricio Ayala Rincon Universidade de Brasilia, BrazilMdrafiul Hassan King Fahd University of Petroleum & Minerals,

Dhahran, KSAMillie Pant Indian Institute of Technology Roorkee, IndiaMohammad Shojafar Sapienza University of Rome, ItalyMrutyunjaya Panda Gandhi Institute for Technological Advancement,

IndiaNebojsa Bacanin Megatrend Univerzitet, SerbiaNeetu Sardana JIIT, Noida, IndiaNiketa Gandhi Machine Intelligence Research Labs, USAOlfa Jemai Université de Sfax, TunisiaOscar Castillo Tijuana Institute of Technology, TijuanaOscar Gabriel Reyes Pupo The University of Central Oklahoma, USAPatrick Siarry Université de Paris, FrancePaulo Carrasco Universidade do Algarve, PortugalPaulo Moura Oliveira University of Trás-os-Montes and Alto Douro,

PortugalPranab Muhuri South Asian University, Delhi, IndiaRamzan Muhammad Maulana Mukhtar Ahmad Nadvi Technical

Campus, IndiaShikha Mehta JIIT, Noida, IndiaShing Chiang Tan Multimedia University, MalaysiaShu Fen Tu Chinese Culture University, ChinaSiddhivinayak Kulkarni University of Ballarat, AustraliaTarun Sharma Amity University, RajasthanTerry Gafron Bio Inspired Technologies, USAThomas Hanne University of Applied Sciences Northwestern

Switzerland, SwitzerlandUsue Mori University of the Basque Country, SpainVarun Kumar Ojha Swiss Federal Institute of Technology,

Switzerland

x ISDA 2017 Organization

Additional Reviewers

Kaushik Das Sharma University of Calcutta, IndiaSafia Djemame Ferhat Abbas University, AlgeriaYi-Fei Pu Sichuan University, ChinaMd Sarwar Haque King Fahd University of Petroleum & Minerals

Dammam, Saudi ArabiaDenis Felipe Federal University of Rio Grande do Norte,

BrazilSílvia M. D. M. Maia Federal University of Rio Grande do Norte,

BrazilLucas Daniel M. S. Pinheiro Universidade Federal do Rio Grande do Norte,

BrazilHector-Gabriel Acosta-Mesa Universidad Veracruzana, MexicoEdgar-Alfredo Portilla-Flores Instituto Politécnico Nacional, MexicoMd Sarwar Haque King Fahd University of Petroleum & Minerals,

Saudi ArabiaAdelaide Cerveira INESC TEC and UTAD, PortugalJoslaine Cristina Jeske

de FreitasUniversidade Federal de Goiás, Brazil

Eliana Pantaleão Universidade Federal de Uberlândia, BrasilAriane Alves Almeida University of Brasília, BrazilLucas Angelo Silveira University of Brasília, BrazilDaniele Nantes-Sobrinho University of Brasília, BrazilDaniel Saad Nogueira Nunes University of Brasília, BrazilSumit Kumar Banshal South Asian University, Delhi, IndiaRajesh Piryani South Asian University, Delhi, IndiaSandeep Kumar South Asian University, Delhi, IndiaAmit Kumar Shukla South Asian University, Delhi, IndiaThatiana C. N. Souza Federal University Rural Semi-Arid, BrazilShadrack Maina Mambo Kenyatta University, Nairobi, KenyaNawel Drira Ecole Nationale d’Electronique et des

Télécommunications de Sfax, TunisiaEsteban José Palomo University of Malaga, Spain

ISDA 2017 Organization xi

Contents

Enhancing Job Opportunities in Rural India Through ConstrainedCognitive Learning Process: Reforming Basic Education . . . . . . . . . . . . 1Shivangi Nigam, Abhishek Bajpai, and Bineet Gupta

UML2ADA for Early Verification of Concurrency Insidethe UML2.0 Atomic Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Taoufik Sakka Rouis, Mohamed Tahar Bhiri, Mourad Kmimech,and Layth Sliman

A New Approach for the Diagnosis of Parkinson’s DiseaseUsing a Similarity Feature Extractor . . . . . . . . . . . . . . . . . . . . . . . . . . . 21João W. M. de Souza, Jefferson S. Almeida,and Pedro Pedrosa Rebouças Filho

A Novel Restart Strategy for Solving Complex Multi-modalOptimization Problems Using Real-Coded Genetic Algorithm . . . . . . . . 32Amit Kumar Das and Dilip Kumar Pratihar

Evaluating SPL Quality with Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Jihen Maazoun, Nadia Bouassida, and Hanêne Ben-Abdallah

Using Sentence Similarity Measure for Plagiarism Detectionof Arabic Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Wafa Wali, Bilel Gargouri, and Abdelmajid Ben Hamadou

Computer Aided Recognition and Classification of Coats of Arms . . . . . 63Frantisek Vidensky and Frantisek Zboril Jr.

Mining Gene Expression Data: Patterns Extraction for GeneRegulatory Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Manel Gouider, Ines Hamdi, and Henda Ben Ghezala

Exploring Location and Ranking for AcademicVenue Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Nour Mhirsi and Imen Boukhris

xiii

Designing Compound MAPE Patterns for Self-adaptive Systems . . . . . . 92Marwa Hachicha, Riadh Ben Halima, and Ahmed Hadj Kacem

CRF+LG: A Hybrid Approach for the Portuguese NamedEntity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Juliana P. C. Pirovani and Elias de Oliveira

A Secure and Efficient Temporal Features Based Frameworkfor Cloud Using MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114P. Srinivasa Rao and P. E. S. N. Krishna Prasad

A Comparison of Machine Learning Methods to Identify BrokenBar Failures in Induction Motors Using Statistical Moments . . . . . . . . . 124Navar de Medeiros Mendonça e Nascimento,Cláudio Marques de Sá Medeiros, and Pedro Pedrosa Rebouças Filho

Canonical Correlation-Based Feature Fusion Approachfor Scene Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134J. Arunnehru, A. Yashwanth, and Shaik Shammer

A Mixed-Integer Linear Programming Model and a SimulatedAnnealing Algorithm for the Long-Term PreventiveMaintenance Scheduling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144Roberto D. Aquino, Jonatas B. C. Chagas, and Marcone J. F. Souza

Interval Valued Feature Selection for Classification of Logo Images . . . 154D. S. Guru and N. Vinay Kumar

An Hierarchical Framework for Classroom Events Classification . . . . . 166D. S. Guru, N. Vinay Kumar, K. N. Mahalakshmi Gupta, S. D. Nandini,H. N. Rajini, and G. Namratha Urs

Hand Gesture Recognition System Based on Local Binary PatternApproach for Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180Houssem Lahiani, Monji Kherallah, and Mahmoud Neji

An Efficient Real-Time Approach for Detectionof Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191Joyjit Chatterjee, Ayush Saxena, Garima Vyas, and Anu Mehra

Dual Image Encryption Technique: Using Logistic Map and Noise . . . . 201Muskaan Kalra, Hemant Kumar Dua, and Reena Singh

A Memetic Algorithm for the Network Construction Problemwith Due Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Jonatas B. C. Chagas, André G. Santos, and Marcone J. F. Souza

Incremental Real Time Support Vector Machines . . . . . . . . . . . . . . . . . 221Fahmi Ben Rejab and Kaouther Nouira

xiv Contents

Content-Based Classification Approachfor Video-Spam Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Palak Agarwal, Mahak Sharma, and Gagandeep Kaur

Kinematic Analysis and Simulation of a 6 DOF Robotin a Web-Based Platform Using CAD File Import . . . . . . . . . . . . . . . . . 243Ujjal Dey and Kumar Cheruvu Siva

Large Scale Deep Network Architecture of CNNfor Unconstraint Visual Activity Analytics . . . . . . . . . . . . . . . . . . . . . . . 251Naresh Kumar

An Automated Support Tool to Compute State RedundancySemantic Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262Dalila Amara, Ezzeddine Fatnassi, and Latifa Rabai

Computing Theory Prime Implicates in Modal Logic . . . . . . . . . . . . . . 273Manoj K. Raut, Tushar V. Kokane, and Rishabh Agarwal

Fault Tolerance in Real-Time Systems: A Review . . . . . . . . . . . . . . . . . 283Egemen Ertugrul and Ozgur Koray Sahingoz

Gauss-Newton Representation Based Algorithm for MagneticResonance Brain Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 294Lingraj Dora, Sanjay Agrawal, and Rutuparna Panda

Evaluating Different Similarity Measures for AutomaticBiomedical Text Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Mozhgan Nasr Azadani and Nasser Ghadiri

Fingerprint Image Enhancement Using Steerable Filterin Wavelet Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315K. S. Jeyalakshmi and T. Kathirvalavakumar

Privacy Preserving Hu’s Moments in Encrypted Domain . . . . . . . . . . . 326G. Preethi and Aswani Kumar Cherukuri

Ensemble of Feature Selection Methods for Text Classification:An Analytical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337D. S. Guru, Mahamad Suhil, S. K. Pavithra, and G. R. Priya

Correlation Scaled Principal Component Regression . . . . . . . . . . . . . . . 350Krishna Kumar Singh, Amit Patel, and Chiranjeevi Sadu

Automated Detection of Diabetic Retinopathy Using WeightedSupport Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357Soumyadeep Bhattacharjee and Avik Banerjee

Predictive Analysis of Alertness Related Features for DriverDrowsiness Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368Sachin Kumar, Anushtha Kalia, and Arjun Sharma

Contents xv

Association Rules Transformation for Knowledge Integrationand Warehousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378Rim Ayadi, Yasser Hachaichi, and Jamel Feki

Abnormal High-Level Event Recognition in Parking lot . . . . . . . . . . . . 389Najla Bouarada Ghrab, Rania Rebai Boukhriss, Emna Fendri,and Mohamed Hammami

Optimum Feature Selection Using Firefly Algorithmfor Keystroke Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Akila Muthuramalingam, Jenifa Gnanamanickam,and Ramzan Muhammad

Multi-UAV Path Planning with Multi Colony Ant Optimization . . . . . . 407Ugur Cekmez, Mustafa Ozsiginan, and Ozgur Koray Sahingoz

An Efficient Method for Detecting Fraudulent Transactions UsingClassification Algorithms on an Anonymized Credit Card Data Set . . . . 418Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam,Krishnan Kannoorpatti, Mirjam Jonkman, and Arasu Balasubramaniam

A Deep Convolution Neural Network Based Model for EnhancingText Video Frames for Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430C. Sunil, H. K. Chethan, K. S. Raghunandan, and G. Hemantha Kumar

A Novel Approach for Steganography App in Android OS . . . . . . . . . . 442Kushal Gurung, Sami Azam, Bharanidharan Shanmugam,Krishnan Kannoorpatti, Mirjam Jonkman, and Arasu Balasubramaniam

Exploring Human Movement Behaviour Based on MobilityAssociation Rule Mining of Trajectory Traces . . . . . . . . . . . . . . . . . . . . 451Shreya Ghosh and Soumya K. Ghosh

Image Sentiment Analysis Using Convolutional Neural Network . . . . . . 464Akshi Kumar and Arunima Jaiswal

Cluster Based Approaches for Keyframe Selection in NaturalFlower Videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474D. S. Guru, V. K. Jyothi, and Y. H. Sharath Kumar

From Crisp to Soft Possibilistic and Rough Meta-clusteringof Retail Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Asma Ammar and Zied Elouedi

Improved Symbol Segmentation for TELUGU OpticalCharacter Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496Sukumar Burra, Amit Patel, Chakravarthy Bhagvati, and Atul Negi

Semantic Attribute Classification Related to Gait . . . . . . . . . . . . . . . . . . 508Imen Chtourou, Emna Fendri, and Mohamed Hammami

xvi Contents

Classification of Dengue Gene Expression Using Entropy-BasedFeature Selection and Pruning on Neural Network . . . . . . . . . . . . . . . . 519Pandiselvam Pandiyarajan and Kathirvalavakumar Thangairulappan

Hardware Trojan: Malware Detection Using Reverse Engineeringand SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530Girishma Jain, Sandeep Raghuwanshi, and Gagan Vishwakarma

Obtaining Word Embedding from Existing Classification Model . . . . . . 540Martin Sustek and Frantisek V. Zboril

A Robust Static Sign Language Recognition System Basedon Hand Key Points Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548Pengfei Sun, Feng Chen, Guijin Wang, Jinsheng Ren, and Jianwu Dong

Multiobjective Genetic Algorithm for Minimum Weight MinimumConnected Dominating Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558Dinesh Rengaswamy, Subham Datta, and Subramanian Ramalingam

Modeling of a System for fECG Extraction from abdECG . . . . . . . . . . 568Rolant Gini John, Ponmozhy Deepan Chakravarthy,K. I. Ramachandran, and Pooja Anand

Supervised Learning Model for Combating Cyberbullying:Indonesian Capital City 2017 Governor Election Case . . . . . . . . . . . . . . 580Putri Sanggabuana Setiawan, Muhammad Ikhwan Jambak,and Muhammad Ihsan Jambak

Improving upon Package and Food Delivery by Semi-autonomousTag-along Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589Vaclav Uhlir, Frantisek Zboril, and Jaroslav Rozman

A Novel Multi-party Key Exchange Protocol . . . . . . . . . . . . . . . . . . . . . 597Swapnil Paliwal and Ch. Aswani Kumar

NLP Based Phishing Attack Detection from URLs . . . . . . . . . . . . . . . . . 608Ebubekir Buber, Banu Diri, and Ozgur Koray Sahingoz

Hand Pose Estimation System Based on a Cascade Approachfor Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619Houssem Lahiani, Monji Kherallah, and Mahmoud Neji

HMI Fuzzy Assessment of Complex Systems Usability . . . . . . . . . . . . . . 630Ilhem Kallel, Mohamed Jouili, and Houcine Ezzedine

A Novel Hybrid GA for the Assignment of Jobs to Machinesin a Complex Hybrid Flow Shop Problem . . . . . . . . . . . . . . . . . . . . . . . 640Houda Harbaoui, Soulef Khalfallah, and Odile Bellenguez-Morineau

Contents xvii

Selecting Relevant Educational Attributes for Predicting Students’Academic Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650Abir Abid, Ilhem Kallel, Ignacio J. Blanco, and Mounir Benayed

Detection and Localization of Duplicated Frames in Doctored Video . . . 661Vivek Kumar Singh, Pavan Chakraborty, and Ramesh Chandra Tripathi

A Novel Approach for Approximate Spatio-Textual Skyline Queries . . . 670Seyyed Hamid Aboutorabi, Nasser Ghadiri,and Mohammad Khodizadeh Nahari

SMI-Based Opinion Analysis of Cloud Services from Online Reviews . . . 683Emna Ben-Abdallah, Khouloud Boukadi, and Mohamed Hammami

Heuristics for the Hybrid Flow Shop Scheduling Problemwith Parallel Machines at the First Stage and Two DedicatedMachines at the Second Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693Zouhour Nabli, Soulef Khalfallah, and Ouajdi Korbaa

Breast Density Classification for Cancer Detection Using DCT-PCAFeature Extraction and Classifier Ensemble . . . . . . . . . . . . . . . . . . . . . . 702Md Sarwar Morshedul Haque, Md Rafiul Hassan, G. M. BinMakhashen,A. H. Owaidh, and Joarder Kamruzzaman

Scheduling Analysis and Correction of Periodic Real Time Systemswith Tasks Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712Faten Mrabet, Walid Karamti, and Adel Mahfoudhi

Generating Semantic and Logic Meaning RepresentationsWhen Analyzing the Arabic Natural Questions . . . . . . . . . . . . . . . . . . . 724Wided Bakari, Patrice Bellot, and Mahmoud Neji

An Arabic Question-Answering System Combining a Semanticand Logical Representation of Texts . . . . . . . . . . . . . . . . . . . . . . . . . . . 735Mabrouka Ben-Sghaier, Wided Bakari, and Mahmoud Neji

Algorithms for Finding Maximal and Maximum Cliques: A Survey . . . 745Faten Fakhfakh, Mohamed Tounsi, Mohamed Mosbah,and Ahmed Hadj Kacem

K4BPMN Modeler: An Extension of BPMN2 Modelerwith the Knowledge Dimension Based on Core Ontologies . . . . . . . . . . . 755Molka Keskes, Mariam Ben Hassen, and Mohamed Turki

Exploring the Integration of Business Process with Nosql Databasesin the Context of BPM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771Asma Hassani and Sonia Ayachi Ghannouchi

xviii Contents

An Effective Heuristic Algorithm for the Double Vehicle RoutingProblem with Multiple Stack and Heterogeneous Demand . . . . . . . . . . . 785Jonatas B. C. Chagas and André G. Santos

Named Entity Recognition from Gujarati TextUsing Rule-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797Dikshan N. Shah and Harshad B. Bhadka

A Meta-modeling Approach to Create a Multidimensional BusinessKnowledge Model Based on BPMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806Sonya Ouali, Mohamed Mhiri, and Faiez Gargouri

Toward a MapReduce-Based K-Means Methodfor Multi-dimensional Time Serial Data Clustering . . . . . . . . . . . . . . . . 816Yongzheng Lin, Kun Ma, Runyuan Sun, and Ajith Abraham

Mining Communities in Directed Networks: A GameTheoretic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826Annapurna Jonnalagadda and Lakshmanan Kuppusamy

A Support Vector Machine Based Approach to Real Time FaultSignal Classification for High Speed BLDC Motor . . . . . . . . . . . . . . . . . 836Tribeni Prasad Banerjee and Ajith Abraham

Automatic Identification of Malaria Using Image Processingand Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846Mahendra Kanojia, Niketa Gandhi, Leisa J. Armstrong,and Pranali Pednekar

Comparative Analysis of Adaptive Filters for PredictingWind-Power Generation (SLMS, NLMS, SGDLMS, WLMS, RLMS) . . . 858Ashima Arora and Rajesh Wadhvani

Blind Write Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 868Khairul Anshar, Nanna Suryana, and Noraswaliza Binti Abdullah

Ontology Visualization: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 880Nassira Achich, Bassem Bouaziz, Alsayed Algergawy,and Faiez Gargouri

Towards a Contextual and Semantic Information Retrieval SystemBased on Non-negative Matrix Factorization Technique . . . . . . . . . . . . 892Nesrine Ksentini, Mohamed Tmar, and Faïez Gargouri

Design and Simulation of Multi-band M-shaped Vivaldi Antenna . . . . . 903Jalal J. Hamad Ameen

Performance Evaluation of Openflow SDN Controllers . . . . . . . . . . . . . 913Sangeeta Mittal

Contents xix

Monitoring Chili Crop and Gray Mould Disease AnalysisThrough Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924Sana Shaikh, Amiya Kumar Tripathy, Gurleen Gill, Anjali Gupta,and Riya Hegde

Intelligent AgriTrade to Abet Indian Farming . . . . . . . . . . . . . . . . . . . . 932Kalpita Wagaskar, Nilakshi Joshi, Amiya Kumar Tripathy, Gauri Datar,Suraj Singhvi, and Rohan Paul

Evaluating the Efficiency of Higher Secondary Education StateBoards in India: A DEA-ANN Approach . . . . . . . . . . . . . . . . . . . . . . . . 942Natthan Singh and Millie Pant

Design of Millimeter-Wave Microstrip Antenna Arrayfor 5G Communications – A Comparative Study . . . . . . . . . . . . . . . . . . 952Saswati Ghosh and Debarati Sen

Simulation Design of Aircraft CFD Based on High PerformanceParallel Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961Yinfen Xie

Determining the Optimum Release Policy Through DifferentialEvolution: A Case Study of Mula Irrigation Project . . . . . . . . . . . . . . . 969Bilal, Millie Pant, and Deepti Rani

Characterising the Impact of Drought on Jowar (Sorghum spp)Crop Yield Using Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 979Shubhangi S. Wankhede and Leisa J. Armstrong

Linear Programming Based Optimum Crop Mix for CropCultivation in Assam State of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . 988Rajni Jain, Kingsly Immaneulraj, Lungkudailiu Malangmeih,Nivedita Deka, S. S. Raju, S. K. Srivastava, J. P. Hazarika,Amrit Pal Kaur, and Jaspal Singh

eDWaaS: A Scalable Educational Data Warehouse as a Service . . . . . . 998Anupam Khan, Sourav Ghosh, and Soumya K. Ghosh

Online Academic Social Networking Sites (ASNSs) SelectionThrough AHP for Placement of Advertisementof E-Learning Website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1008Meenu Singh, Millie Pant, Arshia Kaul, and P. C. Jha

Fingerprint Based Gender Identification Using Digital ImageProcessing and Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . 1018Mahendra Kanojia, Niketa Gandhi, Leisa J. Armstrong,and Chetna Suthar

xx Contents

Indian Mobile Agricultural Services Using Big Data and Internetof Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1028Pallavi Chatuphale and Leisa Armstrong

A Study of the Privacy Attitudes of the Users of the SocialNetwork(ing) Sites and Their Expectations from the Law in India . . . . 1038Sandeep Mittal and Priyanka Sharma

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1053

Contents xxi

Enhancing Job Opportunities in Rural IndiaThrough Constrained Cognitive Learning

Process: Reforming Basic Education

Shivangi Nigam, Abhishek Bajpai(✉), and Bineet Gupta

Shri Ramswaroop Memorial University, Lucknow, UP, Indiaabhishek.srmu@gmail.com

Abstract. Technological advancements in cognitive learning suggest significantchanges in methods of teaching and learning process. A Constrained CognitiveLearning (CCL) model links various forms of cognitive learning methods with arestrictive domain. The main objective of this research study is to propose a CCLscheme that integrates cognitive learning theories and instructional prescriptionsto achieve an effective learning environment for the basic education system inrural India. It improves both knowledge acquisition and employment in optimizedway. Furthermore, our objective is that, the proposed research contributes inpromoting the dialogue between professional learners, academic researchers andpractitioners that increasingly brings empirical educational and research orienta‐tion into the contemporary educational environment across the rural India. Ourfocus is to plan such a cognitive learning environment so that the learner not onlyacquire knowledge but also improve their cognitive abilities to apply their knowl‐edge for the employment and extend their knowledge depth to move towardsresearch oriented innovative skills.

Keywords: Constrained Cognitive Learning (CCL) · Learning paradigmBehaviorism · Cognitivism · Constructivism · Self regulated learning

1 Introduction

“Our profession has always stood at the interface of person and environment-a tenuous placeto be theoretically, since no theory has to date effectively spanned this bridge”.

Cooper (Educationist), 1979

Learning is a continuous pattern of perceiving various goings-on and attainingknowledge from them. The learning process leads to change in behaviors’, increase inthe intellect levels and the ways to analyze and process the perceived information. Theanalysis of the learning process is significant for student-centered learning environmentto enhance the learning abilities of the students. The students vary greatly in their poten‐tial to grasp any new information. Their attitude of apprehension is very low as they donot try to engage themselves towards the understanding of the new subject. The Learningtheories paramount the learning propensities by analyzing the learning patterns andunraveling the ways of perception, perseverance and exploitation of the knowledge.

© Springer International Publishing AG, part of Springer Nature 2018A. Abraham et al. (Eds.): ISDA 2017, AISC 736, pp. 1–9, 2018.https://doi.org/10.1007/978-3-319-76348-4_1

As per the quote of Jean Piaget:

“The goal of education is not to increase the amount of knowledge but to create the possibilitiesfor a child to invent and discover, to create men who are capable of doing new things.”

With this objective, Learning Theories have proposed various models for anenhanced learning process. The Learning theories have been broadly categorized in 3models as Behaviorism, Cognitivism and Constructivism. The behaviorism, fabricatedby John Watson, is the study of learner’s behaviour. The conditioning of the learner isdone via reinforcement (rewards) or punishment which is perceptible from the behaviourof the learner Greeno (1996). The Cognitivism stemmed from Gestalt psychology,explains the learning as processing of information gained as past experiences. Thistheory focuses on thinking caliber of an individual with a previously owned knowledgeregarding the subject matter. The Behaviorist expostulate learning process as the incre‐mental pattern of change in behaviour according to the conditioning of individual i.e.the conditioning is exhibited by the behaviour of the learner. It prepares students forperforming in a knlearning is the mental escalation instead of a behavioral reform. Thepersistent knowledge acquisition upshots the mental structure which aids the individualin resolving novel issues. The psychoown state of affairs. The individuals fail to enactin any inconclusive environment. They lack the problem solving and creative thinkingcapabilities. The Cognitivist theory juxtaposes the Behaviorist theory by propoundingthat logist, Jean Piaget gave a constructive approach to learning as Constructivism. Itaccentuates the students to intensively partake in acquiring knowledge for themselves.Where Cognitivism is based on previous knowledge, Constructivism focuses on strivingto achieve a novel information with the help of previous knowledge thus providing betterperceivance and exploitation. The learner is free to choose his goal which is not the casein Behaviorism/Cognitivism where it is confined to a predetermined set of goalsHannafin (2010).

The Curricular and instructional design which strongly affect educational practiceand advancements extend the scope of learning paradigm of instructional design andmanagement Hill (1997). Cognitive system of learning can be analyzed from severalpoints of view in a dialogue between different parties cross and host. Their informalconversation provides the background for displaying examples and different styles oflearning, teaching, testing, and group dynamics situations. The applicability of cognitivesystem of teaching and learning is emphasized in two ways. First, not all learners havesame level of understanding that match the traditional lecture delivery and laboratoryformat of teaching. Second, certain teachers lecture delivery style is more effective withcertain types of students because they have matching cognitive styles and levels. Effortsshould be made, therefore, to ascertain cognitive styles, to match students and teacherswith compatible styles, and to develop individualized level of materials appropriatenessfor specific cognitive styles of learning.

2 Background

The cognitive learning has been the concern of various research studies in the recentpast. The study by Van Merriënboer and Ayres (2005) proposes a student centered

2 S. Nigam et al.

cognitive learning in web based environments. The research analyses the current patternof student learning from web based multimedia Iiyoshi et al. (2005). The research focusis on exposing students for a better learning experience as proved by various researches.Although the internet technologies may be fruitful for deep learning of the learners, therehas to be some curtailment for the exploration otherwise it can mislead the learners fromthe subject. Thus the web based learning needs to be refined for better learning results.The research in Pintrich (1999) draws the distinction between the self regulated learningand motivational learning. The research study briefs about various categories of selfregulated learning as cognitive learning strategies, self-regulatory strategies to controlcognition, and resource management strategies. The study aims to scrutinize the role ofself regulated learning techniques in student centered classroom environment. The studyby Ohlsson (2016) restrains the cognitive learning process to provide the learners asupervised learning environment. The research commingles the cognitive mechanismsby the user specific domains thus providing aid for Intelligent tutoring systems (ITS).The work in Roberson and Merriam (2005) explores the correlation among epistemo‐logical beliefs and conceptual change learning (CCL). The study reveals that priorknowledge and learning abilities of an individual are significant in conceptual learningprocess. Turning over to the rural population which has least exposure to the techno‐logical learning, various studies have been done to study the factors affecting the learningpatterns of students as well as the elderly/adults. The research by Qian and Alvermann(1995) takes into consideration the adults a rural town of America. The study revolvesaround the self-directed learning (SDL) also termed as personal learning of the elderlyin a rural domain. The results depict a positive response of adults towards making themost of learning resources such as computers, mobile phones etc. thus rendering selfdirected learning activities. An application of cognitive learning is presented by Rahmanet al. (2008) as providing assistance to the health workers in rural areas of Pakistan. Thestudy examined pregnant women over a period of 12 months. The cognitive learning ofhealth works in a resource deficient environment proved a successful intervention.

3 Constrained Cognitive Learning Scheme

3.1 Issues

The Basic Education in India, predominantly, in rural India needs to be brushed up asit will eventually enhance India’s economic development Roediger (2013). Accordingto the Annual Status of Education Report (ASER) in 2012, a larger fragment (96.5%)of children of age group 6–14 years are enrolled in some school. Although the quantityof this percentage has perked up, but the make-up of the knowledge measure is still notcompetent. This deterioration is the repercussion of various factors as teacher quality,classroom quality, curriculum concerns and other dismissive factors as motivation tostudy etc. The teacher based learning have shown appalling pattern of learning stature.There is a need to develop a supervised learning methodology so as to develop betterlearning scheme specifically designed for rural environments.

Enhancing Job Opportunities in Rural India 3

3.2 Research Questions

The research probes the repercussions of bringing in the restrictive cognitive learninginto the current learning paradigm. The study aims to give the way outs for followingquestions:

Q1. What are the factors in dominance by introducing Constrained Cognitive Learning(CCL) in curriculum of rural area students?Q2. How can the CCL paradigm up-skill the teachers in rural areas?Q3. What determinants can motivate the students for CCL?Q4. What is the stumbling block the pavement for quality learning?

3.3 Methodology

The Constrained Cognitive Learning (CCL) scheme proposes learning in 2 phases Up-skill phase and Learning phase in recurrence depicted by Fig. 1.

Fig. 1. Constrained cognitive learning scheme

The up-skill phase involve perk up the teachers in the existing environ. This phaserecapitulate for 3 steps: Priming, Probing and Proficiency. The first step, Priming ofteachers is done to enhance their current skills as per the student apriori knowledge. Itis important to consider the student stature before imparting knowledge to them of anew subject. It makes them have better understanding of the new subject. The teachersshould ruminate over their level of apprehension. The second step, Probing of theteachers is done to observe them if they are using any supplementary methods forimparting a better learning to the students. The reflection of positive probe results depict

4 S. Nigam et al.

that this phase is successful. The third step, Proficiency of the teachers is a significantconcern. The teacher’s proficiency is decided as per the result of probe. It should be veryclear that the teachers are provided the domains in which they are proficient in. It has asubstantial effect on the learning. The up-skill phase helps the supervisors to have finerpractical knowledge thus evolving out their exceptional aspects.

The Learning Event involves the student and his environment. Before irrupting intothe learning phase, the student is supposed to have a basic understanding of the subject.The learning event is a process involving 3 intrinsic units in chorus. In the Acquisitionpart, the student strives to acquire knowledge for a problem solving. The students aregiven elementary knowledge of the subject. With some understanding regarding asubject, the students are exposed to the practical problems of the subject area. Thelearner’s are expected to knock themselves out and explores the problem for a solution.The exploration of student is guided by a supervisor to fabricate the domain of hisanatomy. The supervisor is required to keep the domain of research restricted so as toguide the student for a correct path of knowledge.

4 Implementation: Data Collection and Analysis

4.1 Data Collection

The study involves participants of a school in a rural area near Lucknow, India. A changeof schedule of the teachers and children was done for a month. A prior text of student’sstature was done to be compared later on with the end results. The initial tests includequiz, interviews and brief problem solving sessions. The students were observed for 1week prior to the refashion of their curriculum. In the mean time, the up-skill session ofteachers was programmed as per the above mentioned scheme. The teachers weredispensed with various development stratagems such as video lectures, presentationsetc. They were enlightened with various teaching schemes and were appraised todisseminate teaching with a practical aspect. They were asked to put in for the studentsmore towards practical learning. This scheme also objected to perceive to best of theskills of the teachers and thus making them involve in subjects of their proficiencies.

The program commenced with a reprogrammed curriculum and a schedule withsome sessions of problem solving in a practical environments. For instance, the studentswere taken to fields to learn about the crops and they free to explore the environ. TheCCL scheme decreased the theory sessions and replaced it with more of practicalsessions. They were also exposed to various technologies in the area of their subjects.This was done to make students more jobs oriented as they can have an idea of what aretheir special aspects and they can pursue their higher education in the same to achievetheir goals. The students were probed once every week for their performance. Theinquest involved comprehensive problem solving and quizzes sessions. Table 1 showsthe standard deviation of students as well as supervisors computed on the basis of ques‐tionnaire conducted at various intervals of the program.

Enhancing Job Opportunities in Rural India 5

Table 1. Standard deviation of supervisors and students based on questionnaire

Participant Pre-CCL 1st iteration 2nd iteration 3rd iteration 4th iterationSupervisors 0.60 0.74 0.99 0.76 0.71Students 0.71 0.85 0.78 0.71 0.60

Table 2 presents a comparison of the parameters to distinguish the performance ofstudents before and after. The parameters value of the conceptual parameters is thestandard deviation of the aspects before and after the program commencement. Theparameters of practical learning have been to measure of responsiveness (Resp.) andsatisfaction (Satisf.) of the students on a ranking scale of 1 to 5.

Table 2. Comparison of various practical and conceptual aspects of CCL scheme.

Concept Before AfterConceptual knowledgeObjectivity of subject 0.92 0.88Curriculum design 0.61 0.72Teaching practices(Visual/Theoretical)

0.76 0.79

Practical learningResp. Satisf. Resp. Satisf.

Problem elaboration 2.2 1.2 1.3 2.1Logical attitude 1.5 1.6 1.6 3.4Solution exploration 2.8 2.3 2.3 3.8Performance 1.2 3.1 3.1 4.6Grasping speed 1.5 3.2 3.2 3.5

4.2 Analysis

The CCL scheme focuses on introducing supervision in previous cognitive learningscheme. The up-skilling of the supervisors has improved the overall results of theprogram. The average probing result of the teachers has improved significantly.

The development programs have been rated as positive by the faculties as theythemselves have shown better performance also due to the restriction introduced as theproficiency part of up-skill phase. The higher the standard deviation, the parametersdepict a wider distribution of values. Figure 2 depicts growing graph from the 6–8 yearstudents to the 14–16 year students. This demonstrates that the students from the agegroup 10–16 year have a good effect of CCL scheme. The exploration skills of thestudents exhibit tremendous amelioration in their problem solving skills with significantincrease in standard deviation of the overall performance.

The above research findings were verified by the Chi square tests to determine thefitness of various variables among each others. The results of the tests were found fruitfulas the chi values achieved are lower. The small values of chi square signify that theresults have proved to be as expected. The comparison of chi values achieved forResponsiveness and Satisfaction of the students can be depicted in the Fig. 2.

6 S. Nigam et al.

4.3 Research Findings

The study postulated four questions to find out the correct formulation of the CCL schemefor the basic education of rural India. The factors of Dominance in the investigation werefound as the teaching practices, technology exposure and curriculum design (Fig. 3).

Perf

orm

ance

4.5

4

3.5

3

2.5

2

1.5

1

0.5

06-8 yr 8-10 yr 10-12 yr 12-14 yr 14-16 yr

Students

Responsiveness

Sa sfac on

Fig. 3. Analysis of student’s responsiveness and satisfaction of performance

The results show that they are as per the predictions made by the CCL scheme. Thescheme also finds out ways to ameliorate the teaching practices as video lectures,presentations to promote visual learning process. Other empirical practices includeworkshops, group discussion sessions, seminars, and technology exposure. These prac‐tices promote students for a qualitative learning process. The dismissive factors towards

2.5

2

1.5

1

0.5

00 1 2 3 4 5

-0.5

Responsiveness

Responsiveness (P)

Sa sfac on

Sa sfac on (P)Good

ness

Val

ue

Stages of Learning

Fig. 2. Goodness of the values of responsiveness and satisfaction of students.

Enhancing Job Opportunities in Rural India 7

CCL are the lack of resources like electricity, internet connectivity, inadequate curric‐ulum design and lack of technology usage as learning practices.

5 Future Research Directions

The initial introduction of cognitive style of learning as an instructional parameter at thecollege level; however, now it is also applicable and become effective to the secondaryand elementary levels as well. The certain dimensions of cognitive learning practicesdepend a lot upon interactions of host and receivers. The effects of CCL scheme will beto a greater extent if the supervisors are aware of the student capabilities and can super‐vise the learners to enhance their skills in a effectual manner. The current CCL paradigmcan be extended to introduce an Ingenious Learning System (ILS) which can adapt tothe current learner stature and supervises the learner accordingly.

6 Conclusion

The CCL scheme is program proposed for the basic education system in rural India. Themain objective is to combine practical aspects of the learning into the current curriculumand also improving the curriculum to make it a conceptual one. The up-skill and learningphase has a stupendous improvement in the problem solving of students and also theproficiencies of the teachers. This research proved efficacious to accomplish the ultimategoal of qualitative learning program.

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