Advances in Intelligent Computing

178 Pages·2019·5.255 MB·English

by  J. K. Mandal, Paramartha Dutta, Somnath Mukhopadhyay

Studies in Computational Intelligence 687
J. K. Mandal · Paramartha Dutta 
Editors
Somnath Mukhopadhyay
Advances in
Intelligent
Computing
Studies in Computational Intelligence
Volume 687
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
⋅
J. K. Mandal Paramartha Dutta
Somnath Mukhopadhyay
Editors
Advances in Intelligent
Computing
123
Editors
J.K.Mandal SomnathMukhopadhyay
Department ofComputer Science Department ofComputer
andEngineering ScienceandEngineering
University of Kalyani AssamUniversity
Kalyani, West Bengal Silchar, Assam
India India
Paramartha Dutta
Department ofComputer
andSystemSciences
Visva-Bharati University
Santiniketan, Bolpur,West Bengal
India
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-981-10-8973-2 ISBN978-981-10-8974-9 (eBook)
https://doi.org/10.1007/978-981-10-8974-9
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Foreword
The neologism “Intelligent Computing” encompasses the theory, design, applica-
tion and development of nature-inspired computational paradigms, emphasizing
neural networks, connectionist systems, genetic algorithms, evolutionary pro-
gramming, fuzzy systems, metaheuristic algorithms and hybrid intelligent systems
in which these paradigms are contained. Intelligent computing isan emerging area
of research and has hitherto yielded a rich crop of mathematical models that are
being translated into a wealth of engineering applications in recent times.
Effective modelling of real-life multi-attribute decision-making, non-rigid reg-
istration of medical images, modelling of portfolio construction for Indian stock
market, network-based intrusion detection, estimation of missing entries in the
rating matrix of recommender systems, detection of primary user in cooperative
spectrum sensing to enhance spectrum availability information for cognitive radio
applications, recognition of handwritten words in Bengali, segregation of func-
tionally inactive genes and noise, detection of moving objects in video sequences
whichhavebeenoutlinedinthisbookexhibitthatevenimprecisionanduncertainty
could be effectively utilized in solving complex problems.
I am sure that the researchers will find in this book familiar as well as new
landscapes in intelligent computing.
Bangalore, India Lalit M. Patnaik
January 2018 INSA Senior Scientist and Adjunct Professor
Consciousness Studies Program
Indian Institute of Science
National Institute of Advanced Studies
http://www.lmpatnaik.in
v
Preface
The endeavour for realizing devices, systems having associated intelligence is one
of the recent research trends. The genesis of development of so-called intelligence
systems has its long past. By virtue of being naturally intelligent, human beings
started exploring ifsomeartificialanalogueofintelligenceisrealizable.Thiseffort
was the advent of research in realization of “Intelligent Systems” with the crys-
tallization of ingredients of artificial intelligence, courtesy research effort rendered
by researchers in the domain, people started thinking about embedding these arti-
ficial intelligence components in passive computing system to convert it to some-
what an “intelligent Entity”. This was the logical beginning of research effort
towards machine intelligence. Different computational techniques used for the
purpose of making a machine intelligent gradually became the focus of attention
oftherelevantresearchcommunity.Theformalizationofcomputationaltechniques
used for the purpose of achieving intelligent systems is what computational intel-
ligence all about.
Itisasmallbutsincereeffortonthepartofthepresenteditorstoofferavolume
where different aspects of computational intelligence have been reported. The
objective of this publication is to enlighten the researcher, scholars, students and
engineers about the state-of-the-art scenario regarding advances in intelligent
computing techniques and associated intelligence paradigm, the latest tools and
techniqueswhichareapplicabletoalmostallleadingfieldsofcurrentresearch.The
theme is illustrated in various chapters to encourage researchers to adopt it in
multidisciplinary research and engineering design. We hope that promising ideas
and outstanding research results of this issue will instil further development of
researchandwillenhancetechnologiesintermsofmethodologiesandapplications
of computational intelligence.
This edited book volume entitled Advances in Intelligent Computing is a col-
lectionofeightchapterswhicharepost-conferencepublicationsasextendedversion
papers from the first international conference on Computational Intelligence,
Communications, and Business Analytics (CICBA 2017), held at Kolkata, during
vii
viii Preface
24–25 March 2017 under the publication house of Springer Nature Singapore in
CCIS series. The chapters were initially peer-reviewed by the Editorial Review
Board members, and reviewers who themselves span over many countries. A brief
description of each of the chapters is as follows:
Chapter “Linear Programming-Based TOPSIS Method for Solving MADM
ProblemswithThreeParameterIVIFNs”dealswithdevelopingaTOPSISapproach
using fractional programming techniques for effective modelling of real-life
multi-attributedecision-making(MADM)problemsininterval-valuedintuitionistic
fuzzy(IVIF)settingsbyconsideringhesitancydegreeasadimensiontogetherwith
membership and non-membership degrees. In three-parameter characterization of
intuitionisticfuzzy(IF)sets,aweightedabsolutedistancebetweentwoIFsetswith
respecttoIF weights isdefined andemployedinTOPSIStoformulate intervals of
relativeclosenesscoefficients(RCCs).Thelowerandupperboundsoftheintervals
ofRCCsaregivenbyapairofnonlinearfractionalprogrammingmodelswhichare
further transformed into two auxiliary linear programming models using mathe-
maticalmethodsandfractionalprogrammingtechnique.Asimplertechniqueisalso
proposed for estimating the optimal degrees as performance values of alternatives
from the possibility degree matrix generated by pairwise comparisons of RCC
intervals.Thevalidityandeffectivenessoftheproposedapproacharedemonstrated
in the chapter through some numerical examples.
InChapter“AComparativeStudyofBio-inspiredAlgorithmsforMedicalImage
Registration”, the challenge of determining optimal transformation parameters for
imageregistrationhasbeentreatedtraditionallyasamultidimensionaloptimization
problem. Non-rigid registration of medical images has been approached in this
chapter using the particle swarm optimization algorithm, dragonfly algorithm and
the artificial bee colony algorithm. Brief introductions to these algorithms have
been presented in the chapter. Results of medical image registration using the
proposed algorithms have been analysed in the chapter.The simulationshows that
thedragonflyalgorithmresultsinhigherqualityimageregistration,buttakeslonger
to converge. The trade-off issue between the quality of registration and the com-
puting time has been brought forward.
In Chapter “Different Length Genetic Algorithm-Based Clustering of Indian
Stocks for Portfolio Optimization”, authors proposed a model for portfolio con-
structionusingDifferentLengthGeneticAlgorithm(GA)-basedclusteringofIndian
stocks. Stocks of different companies, chosen from different industries, are classi-
fied on their returns per unit of risk using an unsupervised method of Different
LengthGeneticAlgorithm.Afterthattheobtainedcentroidsareagainclassifiedby
theproposedalgorithm.Verticalclustering(clusteringofstocksbyreturnsperunit
of risk for each day) followed by horizontal clustering (clustering of the centroids
overtime)eventuallyproducesalimitednumberofstocks.TheMarkowitzmodelis
thenappliedtodeterminetheweightsofthestocksintheportfolio.Theresultsare
also compared with some well-known existing algorithms.
Preface ix
In Chapter “An Evolutionary Matrix Factorization Approach for Missing Value
Prediction”, authors addressed the issue of performance degradation of fuzzy
c-means (FCM) clustering. They stated that FCM suffers from low signal-to-noise
ratio (SNR) due to the non-spherical nature of the dataset developed from the
energy values of the sensed signal. To address this problem, authors explored the
scope of possibilistic fuzzy c-means (PFCM) algorithm on energy detection-based
spectrum sensing (SS). PFCM combines the fuzzy membership function and the
possibilisticinformationintheclusteringprocesstopartitiontheinseparableenergy
data into the respective clusters. Differential evolution (DE) algorithm is applied
along with PFCM to maximize the probability of PU detection (PD) under the
constraint of a target false alarm probability (PFA).
In Chapter “Differential Evolution in PFCM Clustering for Energy Efficient
Cooperative Spectrum Sensing”, authors proposed a Memetic Algorithm (MA)-
basedwrapper-filterfeatureselectionmethodwhichisappliedfortherecognitionof
handwritten words’ images in segmentation-free approach. They considered two
state-of-the-art feature vectors describing texture and shape of the word images,
respectively, for feature dimension reduction. Authors have shown experimental
results on handwritten Bangla word samples comprising 50 popular city names of
West Bengal, a state of India.
In Chapter “Feature Selection for Handwritten Word Recognition Using
Memetic Algorithm”, authors proposed a clustering method that clusters
co-expressed genes and segregates functionally inactive genes and noise. The
proposed method formed a cluster if the difference in expression levels of genes
with a specified gene is less than a threshold t in each experiment condition;
otherwise, the specified gene is marked as functionally inactive or noise. The
proposed method is applied on 10 yeast gene expression data and the result shows
that it performs well over existing one.
In Chapter “A Column-Wise Distance-Based Approach for Clustering of Gene
ExpressionDatawithDetectionofFunctionallyInactiveGenesandNoise”,authors
haveproposedatechniquetodetectmovingobjectsinthevideounderdynamicas
well as static background condition. The method consists block-based background
modelling, current frame updating, block processing of updated current frame and
elimination of background using bin histogram approach. Then the enhanced
foreground objects are obtained in the post-processing stage using morphological
operations. They have shown that the approach effectively minimizes the effect of
dynamic background to extract the foreground information. They have applied the
technique on Change Detection CDW-2012 dataset and compared the results with
the other state-of-the-art methods.
In Chapter “Detection of Moving Objects in Video Using Block-Based
Approach”, authors proposed an efficient technique for detecting moving objects
in the video under dynamic as well as static background condition. The method
consists block-based background modelling, current frame updating, block pro-
cessing of updated current frame and elimination of background using bin his-
togram approach. After that an enhanced foreground objects are obtained in the
post-processing stage using morphological operations. The proposed approach
x Preface
effectively minimizes the effect of dynamic background to extract the foreground
information. Authors have applied the proposed technique on Change Detection
CDW-2012 dataset and compared the results with the other state-of-the-art meth-
ods. The experimental results prove the efficiency of the proposed approach com-
pared to the other state-of-the-art methods in terms of different evaluation metrics.
In conclusion, this volume is composed with maximum nourishing aiming to
shieldallmainaspectsoftheareaofintelligentcomputingwithproposingcompact
backgroundinformationaswellasend-pointapplicationsfromthebestsinthearea
reinforcedbyyoungerinvestigatorsinthefield.Itisdesignedtobeaone-stop-shop
for interested readers, but by no means aims to completely interchange all other
sources in this vigorously developing area of research.
Enjoy reading it.
Kalyani, India J. K. Mandal
Santiniketan, India Paramartha Dutta
Silchar, India Somnath Mukhopadhyay