-
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: [email protected]
-
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: [email protected]
Members
Rafael Bello Perez, Universidad Central “Marta Abreu” de Las
Villas, Santa Clara, Cubae-mail: [email protected]
Emilio S. Corchado, University of Salamanca, Salamanca,
Spaine-mail: [email protected]
Hani Hagras, University of Essex, Colchester, UKe-mail:
[email protected]
László T. Kóczy, Széchenyi István University, Győr,
Hungarye-mail: [email protected]
Vladik Kreinovich, University of Texas at El Paso, El Paso,
USAe-mail: [email protected]
Chin-Teng Lin, National Chiao Tung University, Hsinchu,
Taiwane-mail: [email protected]
Jie Lu, University of Technology, Sydney, Australiae-mail:
[email protected]
Patricia Melin, Tijuana Institute of Technology, Tijuana,
Mexicoe-mail: [email protected]
Nadia Nedjah, State University of Rio de Janeiro, Rio de
Janeiro, Brazile-mail: [email protected]
Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw,
Polande-mail: [email protected]
Jun Wang, The Chinese University of Hong Kong, Shatin, Hong
Kong
e-mail: [email protected]
More information about this series at
http://www.springer.com/series/11156
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,
[email protected]
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,
[email protected]
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
http://crossmark.crossref.org/dialog/?doi=10.1007/978-3-319-76348-4_1&domain=pdf
-
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)Goo
dnes
s 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.
References
Greeno, J.G., Collins, A.M., Resnick, L.B.: Cognition and
learning. In: Handbook of EducationalPsychology, vol. 77, pp. 15–46
(1996)
Hannafin, M.J., Hannafin, K.M.: Cognition and student-centered,
web-based learning: issues andimplications for research and theory.
In: Learning and Instruction in the Digital Age, pp. 11–23.
Springer (2010)
Hill, J.R., Hannafin, M.J.: Cognitive strategies and learning
from the world wide web. Educ.Technol. Res. Dev. 45(4), 37–64
(1997)
Iiyoshi, T., Hannafin, M.J., Wang, F.: Cognitive tools and
student- centred learning: rethinkingtools, functions and
applications. Educ. Media Int. 42(4), 281–296 (2005)
Ohlsson, S.: Constraint-based modeling: from cognitive theory to
computer tutoring–and backagain. Int. J. Artif. Intell. Educ.
26(1), 457–473 (2016)
Pintrich, P.R.: The role of motivation in promoting and
sustaining self-regulated learning. Int. J.Educ. Res. 31(6),
459–470 (1999)
Qian, G., Alvermann, D.: Role of epistemological beliefs and
learned helpless-ness in secondaryschool students’ learning science
concepts from text. J. Educ. Psychol. 87(2), 282 (1995)
Rahman, A., Malik, A., Sikander, S., Roberts, C., Creed, F.:
Cognitive behaviour therapy-basedintervention by community health
workers for mothers with depression and their infants inrural
Pakistan: a cluster-randomised controlled trial. Lancet 372(9642),
902–909 (2008)
Roberson Jr., D.N., Merriam, S.B.: The self-directed learning
process of older, rural adults. AdultEduc. Q. 55(4), 269–287
(2005)
8 S. Nigam et al.
-
Roediger III, H.L.: Applying cognitive psychology to education:
Translational educationalscience. Psychol. Sci. Publ. Interest
14(1), 1–3 (2013)
Van Merriënboer, J.J., Ayres, P.: Research on cognitive load
theory and its design implicationsfor e-learning. Educ. Technol.
Res. Dev. 53(3), 5–13 (2005)
Enhancing Job Opportunities in Rural India 9