Handbook of Research on

Computational Intelligencefor Engineering, Science,and Business

Siddhartha BhattacharyyaRCC Institute of Information Technology, India

Paramartha Dutta

Visva Bharati University, India

Volume I

Information Science

REFERENCE

Detailed Table of Contents

Preface xxvi

Acknowledgment xxx

Volume I

Section 1

Overview of Computational Intelligence

Chapter 1

Computational Intelligence Using Type-2 Fuzzy Logic Framework

A. Neogi, The University ofBurdwan, India

A. C. Mondal, The University ofBurdwan, India

S.K. Mandal, National Institute ofTechnical Teachers' Training & Research, India

In this chapter, the authors expand the notion oftype-2 fuzzy sets. An introduction to standard and interval

(type-2) fuzzy sets and systems is explained in the early part of the discussion. The chapter also covers

the ideas of hybrid type-2 fuzzy system. Next, the authors study the applicability of type-2 fuzzy logic(FL) system in student's performance in oral presentation as it is clearly new field ofresearch topic and

have an excellent opportunity to combine several fuzzy set method developed in the recent years. The

proposed application shows the linkage of type-2 fuzzy system with TOPSIS. The present chapter also

highlights the possible future directions for type-2 FL system research. By the end of the chapter, the

authors hope that even those with little previous experience of fuzzy logic should be enabled to applythese methods in their own application areas and/or begin research in this fascinating and exciting area.

Chapter 2

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and

Theory ofFractals 30

Mofazzal H. Khondekar, Dr. B.C. Roy Engineering College, India

Dipendra N. Ghosh, Dr. B.C. Roy Engineering College, India

Koushik Ghosh, University Institute ofTechnology, University ofBurdwan, India

Anup Kumar Bhattacharya, National Institute ofTechnology, India

The present work is an attempt to analyze the various researches already carried out from the theoreti¬

cal perspective in the field of soft computing based time series analysis, characterization of chaos, and

theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction,

data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and

estimation of fractal dimension of time series. The present work is a study as a whole revealing the ef¬

fectiveness as well as the shortcomings of the various techniques adapted in this regard.

Chapter 3

Machine Intelligence Using Hierarchical Memory Networks 62

A. P. James, IIITM-Kerala, India

This chapter presents the fundamentals ofa hardware based memory network that can perform complexcognitive tasks. The network is designed to provide space dimensionality reduction, which enables desired

functionality in a random environment. Complex network functionality is achieved by simple network

cells that minimize the needed chip area for hardware implementation. Functionality of this networkis demonstrated by automatic character recognition with various input deformations. In the character

recognition, the network is trained to recognize characters deformed by random noise, rotation, scal¬

ing, and shifting. This example demonstrates how cognitive functionality of a hardware network can be

achieved through an evolutionary process, as distinct from design based on mathematical formalism.

Section 2

Image Processing and Segmentation

Chapter 4

Image Analysis and Understanding Based on Information Theoretical Region MergingApproaches for Segmentation and Cooperative Fusion 75

Felipe Calderero, Universitat Pompeu Fabra (UPF), SpainFerran Marques, Technical University ofCatalonia (UPC), Spain

This chapter addresses the automatic creation of simplified versions of the image, known as imagesegmentation or partition, preserving the most semantically relevant information of the image at differ¬

ent levels of analysis. From a semantic and practical perspective, image segmentation is a first and key

step for image analysis and pattern recognition since region-based image representations provide a first

level of abstraction and a reduction of the number of primitives, leading to a more robust estimation of

parameters and descriptors. The proposed solution is based on an important class of hierarchical bottom-

up segmentation approaches, known as region merging techniques. These approaches naturally providea bottom-up hierarchy, more suitable when no a priori information about the image is available, and an

excellent compromise between efficiency of computation and representation. The chapter is organizedin two parts dealing with the following objectives: (i) provide an unsupervised solution to the segmen¬tation of generic images; (ii) design a generic and scalable scheme to automatically fuse hierarchical

segmentation results that increases the robustness and accuracy ofthe final solution.

Chapter 5

Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSlGActivation Function 122

Sourav De, The University ofBurdwan, India

Siddhartha Bhattacharyya, RCC Institute ofInformation Technology, India

Susanta Chakraborty, Bengal Engineering and Science University, India

The proposed chapter is intended to propose a self supervised image segmentation method by a multi-

objective genetic algorithm based optimized MUSIG (OptiMUSlG) activation function with a multi-

layer selforganizing neural network architecture to segment multilevel gray scale intensity images. The

multiobjective genetic algorithm based parallel version oftheOptiMUSIG (ParaOptiMUSIG) activation

function with a parallel selforganizing neural network architecture is also discussed to segment true color

images. These methods are quite efficient enough to overcome the drawbacks of the single objectivebased OptiMUSIG and ParaOptiMUSIG activation functions to segment gray scale and true color images,respectively. The proposed multiobjective genetic algorithm based optimization methods are applied on

three standard objective functions to measure the quality ofthe segmented images. These functions form

the multiple objective criteria ofthe multiobjective genetic algorithm based image segmentation method.

Chapter 6

A Novel Fuzzy Rule Guided Intelligent Technique for Gray Image Extraction and Segmentation ..163

Koushik Mondal, IITIndore, India

Image segmentation and subsequent extraction from a noise-affected background, has all along remaineda challenging task in the field of image processing. There are various methods reported in the literature

to this effect. These methods include various Artificial Neural Network (ANN) models (primarily super¬

vised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, etcetera.

Providing an extraction solution working in unsupervised mode happens to be even more interesting a

problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and

imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain

knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that

effort in this respect appears to be quite rudimentary. This chapter proposes a fuzzy rule guided novel

technique that is functional devoid ofany external intervention during execution. Experimental results

suggest that this approach is an efficient one in comparison to different other techniques extensivelyaddressed in literature. In order to justify the supremacy of performance of our proposed technique in

respect of its competitors, the author takes recourse to effective metrices like Mean Squared Error (MSE).Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).

Chapter 7

Graph Based Segmentation of Digital Images 182

B.K. Tripathy, VIT University, India

P. V.S.S.R. Chandra Mouli, VIT University, India

Image Segmentation is the process of dividing an image into semantically relevant regions. The problemis still an active area due to wide applications in object detection and recognition, image retrieval, im¬

age classification, et cetera. The problem is challenging due to its subjective nature. Many researchers

addressed this problem by exploring graph theoretic principles. The key idea is the transformation of

segmentation problem into graph partitioning problem by representing the image as a graph. The aimof this chapter is to study various graph based segmentation algorithms.

Chapter 8

Development of a Stop-Line Violation Detection System for Indian Vehicles 200

Satadal Saha, MCKV Institute ofEngineering, India

Subhadip Basu, Jadavpur University, India

Mita Nasipuri, Jadavpur University, India

In the present work, the authors designed and developed a complete system for generating the list ofall

violating vehicles that has violated the stop-line at a road crossing automatically from video snapshotsof road-side surveillance cameras using background subtraction technique. It then localizes the license

plates of the vehicles by analyzing the vertical edge map of the images, segments the license plate

characters using connected component labeling algorithm, and recognizes the characters using back

propagation neural network. Considering round-the-clock operations in a real-life test environment, the

developed system could successfully track 92% images of vehicles with violations on the stop-line in a

red traffic signal. The performance of the system is evaluated with a dataset of 4717 images collected

from 13 different camera views in 4 different environmental conditions. The authors have achieved

around 92% plate localization accuracy over different views and weather conditions. The average platelevel recognition accuracy of 92.75% and character level recognition accuracy of 98.76% are achieved

over the localized vehicle images.

Chapter 9

A Comparative Study of Unsupervised Video Shot Boundary Detection Techniques UsingProbabilistic Fuzzy Entropy Measures 228

Biswanath Chakraborty, RCC Institute ofInformation Technology, India

Siddhartha Bhattacharyya. RCC Institute ofInformation Technology, India

Susanta Chakraborty, Bengal Engineering and Science University, India

The performance of video shot boundary detection technique in unsupervised video sequence can be

improved by the use of different probabilistic fuzzy entropies. In this chapter, the authors present a new

technique for identifying as to whether there are any appreciable changes from one video context to

another in the available sequence of image frames extracted from a mixture of a numbers of video files.

They then compared their technique with an existing technique and found improved performance ofthevideo shot boundary detection techniques using probabilistic fuzzy entropies.

Chapter 10

A Hierarchical Multilevel Image Thresholding Method Based on the Maximum Fuzzy Entropy

Principle 241

Pearl P. Guan, City University ofHong Kong, Hong Kong

Hong Yan. City University of Hong Kong, Hong Kong & University ofSydney, Australia

Image thresholding and edge detection are crucial in image processing and understanding. In this chapter,the authors propose a hierarchical multilevel image thresholding method for edge information extraction

based on the maximum fuzzy entropy principle. In orderto realize multilevel thresholding, a tree structure

is used to express the histogram of an image. In each level of the tree structure, the image is segmentedby three-level thresholding based on the maximum fuzzy entropy principle. In theory, the histogram

hierarchy can be combined arbitrarily with multilevel thresholding. The proposed method is proven by

experimentation to retain more edge information than existing methods employing several grayscaleimages. Furthermore, the authors extend the multilevel thresholding algorithm for color images in the

application of content-based image retrieval, combining with edge direction histograms. Compared to

using the original images, experimental results show that the thresholding images outperform in achiev¬

ing higher average precision and recall.

Chapter 11

Adaptive Median Filtering Based on Unsupervised Classification of Pixels 273

./. K. Mandal, University ofKalyani. India

Somnath Mukhopadhyay, Aryabhatta Institute ofEngineering & Management Durgapur, India

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital

images through unsupervised classification of pixels. This approach coagulates directional weightedmedian filtering with unsupervised pixel classification based adaptive window selection toward detec-

tion and filtering of impulses in digital images. K-means based clustering algorithm has been utilized

to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median

filtering approach has been proposed to obtain best possible restoration results. Results demonstratingthe effectiveness ofthe proposed technique are provided for numeric intensity values described in terms

offeature vectors. Various benchmark digital images are used to show the restoration results in terms of

PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

Section 3

Database Oriented Techniques

Chapter 12

Data Clustering Algorithms Using Rough Sets 297

B.K. Tripathy, VIT University, India

Adhir Ghosh, VIT University, India

Developing Data Clustering algorithms have been pursued by researchers since the introduction of

k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to

handle categorical data. In order to handle the situations where objects can have memberships in multiple

clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a).

There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004;

Peters 2006,2007). The MMRalgorithm (Parmar et al 2007), its extensions (Tripathy et al 2009,2011 a,

201 lb) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this

chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of

all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also

present problems for future studies in the direction of the topics covered.

Chapter 13

Evolution of Genetic Algorithms in Classification Rule Mining 328

Dipankar Dutta, University Institute of Technology, The University ofBurdwan, India

Jaya Sil, Bengal Engineering and Science University, India

Classification is one of the most studied areas of data mining, which gives classification rules duringtraining or learning. Classification rule mining, an important data-mining task, extracts significant rules

for classification of objects. In this chapter class specific rules are represented in IF <Antecedent>

THEN <Consequent> form. With the popularity of soft computing methods, researchers explore dif¬

ferent soft computing tools for rule discovery. Genetic algorithm (GA) is one of such tools. Over time,new techniques of GA for forming classification rules are invented. In this chapter, the authors focus

on an understanding of the evolution of GA in classification rule mining to get an optimal rule set that

builds an efficient classifier.

Chapter 14

Database Anonymization Techniques with Focus on Uncertainty and Multi-Sensitive Attributes...

364

B. K. Tripathy, VIT University, India

Publication of Data owned by various organizations for scientific research has the danger of sensitive

information of respondents being disclosed. The policy of removal or encryption of identifiers cannot

avoid the leakage of information through quasi-identifiers. So, several anonymization techniques like

k-anonymity, 1-diversity, and t-closeness have been proposed. However, uncertainty in data cannot be

handled by these algorithms. One solution to this is to develop anonymization algorithms by using roughset based clustering algorithms like MMR, MMeR, SDR, SSDR, and MADE at the clustering stage of

existing algorithms. Some ofthese algorithms handle both numerical and categorical data. In this chap¬ter, the author addresses the database anonymization problem and briefly discusses k-anonymizationmethods. The primary focus is on the algorithms dealing with 1-diversity of databases having single or

multi-sensitive attributes. The author also proposes certain algorithms to deal with anonymization of

databases with involved uncertainty. Also, the aim is to draw attention of researchers towards the vari¬

ous open problems in this direction.

Chapter 15

Using Data Masking for Balancing Security and Performance in Data Warehousing 384

Ricardo Jorge Santos, CISUC - FCTUC - University ofCoimbra, Portugal

Jorge Bernardino, CISUC - ISEC - Polytechnic Institute ofCoimbra, PortugalMarco Vieira, CISUC -FCTUC - University ofCoimbra, Portugal

Data Warehouses (DWs) are the core ofsensitive business information, which makes them an appealingtarget. Encryption solutions are accepted as the best way to ensure strong security in data confidentialitywhile keeping high database performance. However, this work shows that they introduce massive stor¬

age space and performance overheads to a magnitude that makes them unfeasible for DWs. This work

proposes a data masking technique for protecting sensitive business data in DWs which balances securitystrength with database performance, using a formula based on the mathematical modular operator and

simple arithmetic operations. The proposed solution provides apparent randomness in the generationand distribution of the masked values, while introducing small storage space and query execution time

overheads. It also enables a false data injection method for misleading attackers and increasing the

overall security strength. It can be easily implemented in any DataBase Management System (DBMS)and transparently used, without changes to application source code. Experimental evaluations using a

real-world DWand TPC-H decision support benchmark implemented in leading DBMS Oracle 11 g and

Microsoft SQL Server 2008 demonstrate its overall effectiveness. Results show the substantial savingsof its implementation costs when compared with state of the art encryption solutions provided by those

DBMS and that it outperforms those solutions in both data querying and insertion of new data.

Volume II

Chapter 16

Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in

Distributed Environments 410

Elina Pacini, Institutefor Information and Communication Technologies, Universidad

Nacional de Cuyo, ArgentinaCristian Mateos, Instituto Superior de Ingenieria de Software, Consejo Nacional de

Investigaciones Cientificas y Tecnicas, ArgentinaCarlos Garcia Garino, Institute for Information and Communication Technologies,

UniversidadNacional de Cuyo, Argentina

Scientists and engineers are more and more faced to the need ofcomputational power to satisfy the ever-

increasing resource intensive nature of their experiments. An example ofthese experiments is Parameter

Sweep Experiments (PSE). PSEs involve many independentjobs, since the experiments areexecuted under

multiple initial configurations (input parameter values) several times. In recent years, technologies such

as Grid Computing and Cloud Computing have been used for running such experiments. However, for

PSEs to be executed efficiently, it is necessary to develop effective scheduling strategies to allocatejobs

to machines and reduce the associated processing times. Broadly, the job scheduling problem is known

to be NP-complete, and thus many variants based on approximation techniques have been developed. In

this work, the authors conducted a survey ofdifferent scheduling algorithms based on Swarm Intelligence

(SI), and more precisely Ant Colony Optimization (ACO), which is the most popular SI technique, to

solve the problem ofjob scheduling with PSEs on different distributed computing environments.

Section 4

Classification, Design, and Modeling

Chapter 17

Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection 449

Basabi Chakraborty, Iwate Prefectural University, Japan

Selecting an optimum subset of features from a large set of features is an important pre- processing step

forpattern classification, data mining, or machine learning applications. Feature subset selection basically

comprises of defining a criterion function for evaluation of the feature subset and developing a search

strategy to find the best feature subset from a large number of feature subsets. Lots of mathematical and

statistical techniques have been proposed so far. Recently biologically inspired computing is gaining

popularity for solving real world problems for their more flexibility compared to traditional statistical

or mathematical techniques. In this chapter, the role of Particle Swarm Optimization (PSO), one of the

recently developed bio-inspired evolutionary computational (EC) approaches in designing algorithmsfor producing optimal feature subset from a large feature set, is examined. A state of the art review

on Particle Swarm Optimization algorithms and its hybrids with other soft computing techniques for

feature subset selection are presented followed by author's proposals of PSO based algorithms. Simple

simulation experiments with benchmark data sets and their results are shown to evaluate their respec¬

tive effectiveness and comparative performance in selecting best feature subset from a set of features.

Chapter 18

An Evolving System in the Text Classification Problem 467

Elias Oliveira, Universidade Federal do Espirito Santo, Brazil

Patrick Marques CiareIJi, Universidade Federal do Espirito Santo, Brazil

Evandro Ottoni Teatini Salles, Universidade Federal do Espirito Santo. Brazil

Traditional machine learning techniques have been successful in yielding good results when the data

are stable along the time horizon. However, in many cases, these techniques may be inefficient for data

that are constantly expanding and changing over time. To address this problem, new learning techniques

have been proposed in the literature. In this chapter, the authors discuss some improvements on their

technique, called Evolving Probabilistic Neural Network (ePNN), and present the aspects of this recent

learning paradigm. This technique is based on the Probabilistic Neural Networks. In this chapter the au¬

thors compare their technique against two other competitive techniques that can be found in the literature:

Incremental Probabilistic Neural Network (IPNN) and Evolving Fuzzy Neural Network (EFuNN). To

show the better performance of their technique, the authors present and discuss a series ofexperimentsthat demonstrate the efficiency of ePNN over both the IPNN and EFuNN approaches.

Chapter 19

Fuzzy Based Modeling, Control, and Fault Diagnosis of Permanent Magnet

Synchronous Generator 487

N. Selvaganesan, Indian Institute ofSpace Science and Technology (IIST), Govt, ofIndia. India

This chapter presents the design methodology of fuzzy based modeling, control, and fault diagnosisof Permanent Magnet Synchronous Generator (PMSG) system. The fuzzy based modeling scheme for

PMSG is developed using the general Takagi-Sugeno fuzzy model. Subsequently, fuzzy controller is

designed and simulated to maintain three phase output voltage as constant by controlling the speed of

generator. The feasibility ofthe fuzzy model and control scheme is demonstrated using various operatingconditions by MATLAB simulation. Also, fuzzy based fault detection scheme for PMSG is developedand presented. The positive and negative sequence currents are used as fault indicators and given as

inputs to fuzzy fault detector. The fuzzy inference system is created, and rule base is evaluated, relatingthe sequence current component to the type of faults. The feasibility of this scheme is demonstrated for

different types of fault under various operating conditions using MATLAB/Simulink.

Chapter 20

Algorithms and Principles for Intelligent Design of Flapping Wing Micro Aerial Vehicles 521

Ajay Bangalore Harish. Indian Institute ofScience, India

Dineshkumar Harursampath, Indian Institute ofScience. India

Almost all Micro Aerial Vehicles (MAVs) designed so far facilitate the flapping motion oftheir wings bymeans of a mounted actuating mechanism, driven, for example, by a piezoelectric crystal. The develop¬ments over the past decade or so in smart material technologies like the invention of Piezoelectric Fiber

Reinforced Composite (PFRC) materials and innovative manufacturing techniques to reduce cost have

resulted in favorable materials for dynamic actuating applications. Thus, the concept ofactively deform-

able wings to produce combined flapping and feathering actions is evolving as an attractive enabler for

design of future MAVs. A smart material like PFRC can both sense and actuate in a collocated fashion,thus building an additional level of computational intelligence into the MAV itself. Such a promisingopportunity indicates an urgent need for reliable design tools to accelerate development of MAVs. In

this work, the authors propose a modular design tool specifically for design of self-actuating flappingwing MAVs.

Chapter 21

Fuzzy-Controlled Energy-Conservation Technique (FET) for Mobile ad hoc Networks 556

Anuradha Banerjee. Kalyani Government Engineering College. India

Nodes in ad hoc networks have limited battery power; hence, they require energy efficient techniquesto improve average node lifetime and network performance. Maintaining energy efficiency in network

communication is really challenging because highest energy efficiency is achieved if all the nodes are

switched off and maximum network throughput is obtained if all the nodes are fully operational, i.e.

always turned on. A promising energy conservation technique for the ad hoc networks must maintain

effective packet forwarding capacity while turning off the network interface of very busy nodes for

some time and redirecting the traffic through some comparatively idle nodes roaming around them. This

also helps in fair load distribution in the network and maintenance of network connectivity by reducingthe death rate (complete exhaustion of nodes). The present chapter proposes a fuzzy-controlled energyconservation technique (FET) that identifies the busy and idle nodes to canalize some traffic of busynodes through the idle ones. In simulation section, the FET embedded versions of several state-of-the-

art routing protocols in ad hoc networks are compared with their ordinary versions and the results quite

emphatically establish the superiority of FET-embedded versions in terms of packet delivery ratio, mes¬

sage cost, and network energy consumption. End-to-end delay also reduces significantly.

Chapter 22

Decision Fusion of Multisensor Images for Human Face Identification in Information Security 571

Mrinal Kanti Bhowmik, Tripura University, India

Priya Saha, Tripura University, India

Goutam Majumder, Tripura University, India

Debotosh Bhattacharjee, Jadavpur University, India

The chapter is mainly focused on theoretical discussions and experimental observations on decision

fusion along with feature level multisensor fusion technique for human face identification especiallyuseful in information security system in order to obtain better recognition rate. Feature level multisensor

fusion ofoptical and infrared images is performed to resolve the difficulties of individual interpretationof visual and infrared images as a first step of the face identification system. ROC curve analysis is also

reported to verify the recognition accuracy after classification offused images using Support Vector Ma¬

chine (SVM). The authors have performed experiments on IRIS face database in three different groups:full dataset, and two subsets with variation in expression and illumination. Classification accuracy was

obtained in two subsets and full dataset as 95%, 94.12%, and 99.08%, respectively after decision fusion.

Chapter 23

Modernization of Healthcare and Medical Diagnosis System Using Multi Agent System (MAS):A Comparative Study 592

Shibakali Gupta, University Institute ofTechnology, Burdwan University, India

Sripati Mukherjee, Burdwan University, India

Sesa Singha Roy, Tata Consultancy Service, India

The healthcare system that prevailed some years ago was a mere pen and paper based system. A number

ofworkers, staff, and written records were the main components ofthe prevailing system of healthcare.

This had a number of drawbacks, and a number of mishaps occurred due to mismanagement ofdata andinformation. There was a need for development. Then, the concept oftelemedicine came, which revolu¬

tionized the healthcare paradigm to a great extent. With the advancement of telemedicine, many majorproblems of the prevailing system were removed. But, still there were many other aspects which could

be further improved to make healthcare facilities more enhanced. Keeping this in mind, the concept of

Multi Agent System (MAS) was introduced in the healthcare system later. MASes are considered as the

best and most appropriate technology that can be used in the development of applications in healthcare

paradigm where the presence of multiple agents, heterogeneous and loosely coupled components, the

data management in a dynamic and distributed environment, and multi-user collaborations are consid¬

ered the most pertinent requirements for healthcare system. This chapter focuses mainly about MAS,its applications, and some systems that were developed by the authors.

Section 5

Applications

Chapter 24

Watermarking of Data Using Biometrics 623

Swanirbhar Majumder, Deemed University, India

Tirtha Sankar Das, RCC Institute ofInformation Technology, India

These days, for the copyright protection and security of multimedia data in this age of the tech-savvyworld, watermarking is a very important technique. Moreover, with the inclusion of biometrics for the

watermarking schemes, the concept of "something you are" is included in the watermark and/or cover

image. This thereby increases the security intensity in the multimedia data. And to give a glimpse ofthe

technique the concepts of Watermarking, biometric and watermarking using biometrics is discussed.

Finally, a particular case of real time watermarking of data using biometric is discussed by specifyinga practical example.

Chapter 25

Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorptionfrom Aqueous Solution by Orange Peel Ash 649

Siddhartha Bhattacharjee, Tata Consultancy Services, India

Siddhartha Bhattacharyya, RCC Institute ofInformation Technology, India

Naba Kumar Mondal, The University ofBurdwan, India

The chapter describes a multilayer quantum backpropagation neural network (QBPNN) architecture

to predict the removal of phenol from aqueous solution by orange peel ash, guided by the applicationof three types of activation functions and characterized by backpropagation of errors. These activation

functions are Sigmoid function, tanh function and tan 1.5h function. First by a classical multilayer neural

network architecture with three types ofactivation functions is discussed in this chapter. It takes 6000000

iterations to train the network with a learning rate of 0.01. Among these three types of activation func¬

tions tan 1.5 function shows the best prediction result. Next, QBPNN is discussed in this chapter. It takes

22000 iterations to train the network with the same learning rate. Here also tanl.Sh function shows the

best result in prediction of removal of phenol. Thus QBPNN is much faster than the classical multilayerneural network architecture. Different graphs are also given for comparison between the experimentaloutput and network output using different activation functions. This particular chapter basically deals witha model application by which experimental results can be comparing with the model output. Because of

their reliable, robust, and salient characteristics in capturing the non-linear relationships existing between

variables (multi-input/output) in complex systems, it has become apparent that numerous applications of

ANNs/QBNN have been successfully conducted in various parts of environmental engineering. Fuzzy

Logic is also used as alternate method to predict the removal of phenol from aqueous solution by orange

peel ash, but QBPNN shows the best result.

Chapter 26

Computational Intelligence for Pathological Issues in Precision Agriculture 672

Sanjeev S. Sannakki, Gogte Institute ofTechnology, India

Vijay S. Rajpurohit, Gogte Institute of Technology, India

V. B. Nargund, University ofAgricultural Sciences, India

Amn R. Kumar, Ashokrao Mane Group ofInstitutions, India

Prema S. Yallur, Ashokrao Mane Group ofInstitutions, India

Plant Pathology is the scientific study of plant diseases, caused by pathogens and environmental condi¬

tions (physiological factors). Detection and grading of plant diseases by machine vision is an essential

research topic as it may prove useful in monitoring large fields of crops. This can be of great benefit to

those users, who have little or no information about the crop they are growing. Also, in some develop¬

ing countries, farmers may have to go long distances to contact experts to dig up information which is

expensive and time consuming. Therefore, looking for a fast, automatic, less expensive, and accurate

method to detect plant diseases is of great realistic significance. Such an efficient system can be mod¬

eled by integrating the various tools/techniques of information and communication technology (ICT)in agriculture. The objective of the present chapter is to model an intelligent decision support systemfor detection and grading of plant diseases which encompasses image processing techniques and soft

computing/machine learning techniques.

Chapter 27

Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering 699

Anasua Sarkar, Government College ofEngineering and Leather Technology, India

Ujjwal Maulik, Jadavpur University, India

Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modi¬

fied symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex

or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this

chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clusteringalgorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is

to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory

helps in faster convergence and initial automatic optimal classification, thereby solving the problemof unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts

to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is

compared with another newly implemented rough-improved symmetry-based clustering algorithm and

existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its

superiority in terms ofvalidity. The statistical analyses are also performed to establish the signifizzvcanceof this rough modified symmetry-based clustering approach.

Chapter 28

Computer Intelligence in Healthcare 716

Pramit Ghosh, RCC Institute ofInformation Technology, India

Debotosh Bhattacharjee, Jadavpur University, India

Mita Nasipuri, Jadavpur University, India

Dipak Kumar Basu, Jadavpur University, India

Low cost solutions for the development of intelligent bio-medical devices that not only assist peopleto live in a better way but also assist physicians for better diagnosis are presented in this chapter. Twosuch devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical

analysis reveals that cold and fever are the main culprits for the loss ofman-hours throughout the world,and early pathological investigation can reduce the vulnerability ofdisease and the sick period. To reduce

this cold and fever problem a household cooling system controller, which is adaptive and intelligent in

nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based

on the real time data, namely room temperature, humidity, and time for which system is active, which are

collected from environment. To control the speed in an adaptive and intelligent manner, an associative

memory neural network (Kramer) has been used. This embedded system is able to learn from trainingset; i.e., the user can teach the system about his/her feelings through training data sets. When the system

starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental

parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision

and controls the speed of the fan.

Chapter 29

Intelligence in Web Technology 739

Sourav Mailra, Burdwan University, India

A. C. Mondal, Burdwan University, India

End users also start days with Internet. This has become the scenario. One of the most burgeoning needs

of computer science research is research on web technologies and intelligence, as that has become

one of the most emerging nowadays. A big area of other research areas like e-marketing, e-leaming,e-governance, searching technologies, et cetera will be highly benefited if intelligence can be added to

the Web. The objective of this chapter is to create a clear understanding of Web technology research and

highlight the ways to implement Semantic Web. The chapter also discusses the tools and technologiesthat can be applied to develop Semantic Web. This new research area needs enough care as sometimes

data are open. Thus, software engineering issues are also a focus.

Chapter 30

Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportionof Different Component in Manhole Gas Mixture 758

Varun Kumar Ojha, Visva Bharati University, India

Paramartha Dutta, Visva Bharati University, India

This chapter deals with the comparison of performances ofdifferent intelligent techniques for detectingproportion ofdifferent component gases present in manhole gas mixture. Toxic gases found in manhole

gas mixture are Hydrogen Sulfide (H2S). Ammonia (NH3). Methane (CH4), Carbon Dioxide (C02),Nitrogen Oxide (NOx), Carbon Monoxide (CO), et cetera. Detection of these toxic gases is essential

since these gases influence human health even due to very short exposure. This study is centered on

design issues of an intelligent sensory system for detecting proportion of different components in man¬

hole gas mixture and comparison of different intelligent techniques applied for this. The investigationencompasses linear regression based statistical technique, backpropagation algorithm, neuro genetictechniques (using genetic algorithm to train neural network), and neuro swarm techniques (using particleswarm optimization algorithm to train neural network).

Compilation of References xxxi

About the Contributors ciii

Index cxvii