Global Trends in Intelligent Computing Research and Development

Global Trends in Intelligent Computing Research and Development

B.K. Tripathy, D. P. Acharjya

Copyright: © 2014 |Pages: 601

ISBN13: 9781466649361|ISBN10: 1466649364|EISBN13: 9781466649378

DOI: 10.4018/978-1-4666-4936-1

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MLA

Tripathy, B.K., and D. P. Acharjya, editors. Global Trends in Intelligent Computing Research and Development. IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4936-1

APA

Tripathy, B. & Acharjya, D. P. (Eds.). (2014). Global Trends in Intelligent Computing Research and Development. IGI Global. https://doi.org/10.4018/978-1-4666-4936-1

Chicago

Tripathy, B.K., and D. P. Acharjya, eds. Global Trends in Intelligent Computing Research and Development. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4936-1

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As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data.

Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Computational Intelligence and Robotics (ACIR) Book Series

Dedication

Editorial Advisory Board and List of Reviewers

Preface

Acknowledgment

Chapters

Classification and Clustering

Chapter 1

Clustering-Based Stability and Seasonality Analysis for Optimal Inventory Prediction (pages 1-18)

Manish Joshi, Pawan Lingras, Gajendra Wani, Peng Zhang

This chapter exemplifies how clustering can be a versatile tool in real life applications. Optimal inventory prediction is one of the important issues faced by owners of retail chain stores. Researchers have made several attempts to...

Chapter 2

Efficient Color Image Segmentation by a Parallel Optimized (ParaOptiMUSIG) Activation Function (pages 19-50)

Sourav De, Siddhartha Bhattacharyya, Susanta Chakraborty

The optimized class responses from the image content has been applied to generate the optimized version of MUSIG (OptiMUSIG) activation function for a multilayer self organizing neural network architecture to effectively segment...

Chapter 3

Learning Using Soft Computing Techniques (pages 51-67)

V. Susheela Devi

This chapter focuses on a few key applications in the field of classification and clustering. Techniques of soft computing have been used to solve these applications. The first application finds a new similarity measure for time...

Chapter 4

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding (pages 68-110)

Sandip Dey, Siddhartha Bhattacharyya, Ujjwal Maulik

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold...

Chapter 5

Feature Selection Algorithms for Classification and Clustering in Bioinformatics (pages 111-130)

Sujata Dash, Bichitrananda Patra

This chapter discusses some important issues such as pre-processing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. Although these concepts are...

Chapter 6

Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey (pages 131-145)

Natthakan Iam-On, Tossapon Boongoen

A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the...

Foundations of Knowledge Representation

Chapter 7

Multi-Objective Genetic and Fuzzy Approaches in Rule Mining Problem of Knowledge Discovery in Databases (pages 147-179)

Harihar Kalia, Satchidananda Dehuri, Ashish Ghosh

Knowledge Discovery in Databases (KDD) is the process of automatically searching patterns from large volumes of data by using specific data mining techniques. Classification, association, and associative classification (integration...

Chapter 8

Rough Sets and Approximate Reasoning (pages 180-228)

B. K. Tripathy

Several models have been introduced to capture impreciseness in data. Fuzzy sets introduced by Zadeh and Rough sets introduced by Pawlak are two of the most popular such models. In addition, the notion of intuitionistic fuzzy sets...

Chapter 9

Case-Based Reasoning and Some Typical Applications (pages 229-267)

Durga Prasad Roy, Baisakhi Chakraborty

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that...

Chapter 10

Hybrid Genetic Algorithm: An Optimization Tool (pages 268-305)

Kedar Nath Das

Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle...

Chapter 11

Knowledge Representation Using Formal Concept Analysis: A study on Concept Generation (pages 306-336)

Ch. Aswani Kumar, Prem Kumar Singh

Introduced by Rudolf Wille in the mid-80s, Formal Concept Analysis (FCA) is a mathematical framework that offers conceptual data analysis and knowledge discovery. FCA analyzes the data, which is represented in the form of a formal...

Foundations of Computational Intelligence

Chapter 12

Heterogeneous Data Structure “r-Atrain” (pages 338-359)

Ranjit Biswas

The data structure “r-Train” (“Train” in short) where r is a natural number is a new kind of powerful robust data structure that can store homogeneous data dynamically in a flexible way, in particular for large amounts of data. But a...

Chapter 13

Comparative Analysis of Hybrid Soft Set Methods (pages 360-383)

Debadutta Mohanty

The whole mathematical scenario has changed with the advent of the Rough Set Theory, a powerful tool to deal with uncertainty and incompleteness of knowledge in information system. With the advancement of research, the Soft Set...

Chapter 14

Diagram Drawing Using Braille Text: A Low Cost Learning Aid for Blind People (pages 384-406)

Anirban Mukherjee, Utpal Garain, Arindam Biswas

This chapter presents a novel system for drawing geometric diagrams on the Braille medium in order to make the diagrams tactile and accessible by blind people. The computer graphics algorithms for drawing digital shapes have been...

Chapter 15

Application of Functional Approach to Lists for Development of Relational Model Databases and Petri Net Analysis (pages 407-444)

Sasanko Sekhar Gantayat, B. K. Tripathy

The concept of list is very important in functional programming and data structures in computer science. The classical definition of lists was redefined by Jena, Tripathy, and Ghosh (2001) by using the notion of position functions...

Chapter 16

Soft Sets: Theory and Applications (pages 445-494)

Pinaki Majumdar

This chapter is about soft sets. A brief account of the developments that took place in last 14 years in the field of Soft Sets Theory (SST) has been presented. It begins with a brief introduction on soft sets and then it describes...

Information Science and Neural Network

Chapter 17

Adaptive and Neural pH Neutralization for Strong Acid-Strong Base System (pages 496-515)

J. Abdul Jaleel, Anish Benny, David K. Daniel

The control of pH is of great importance in chemical processes, biotechnological industries, and many other areas. High performance and robust control of pH neutralization is difficult to achieve due to the nonlinear and time-varying...

Chapter 18

A Shannon-Like Solution for the Fundamental Equation of Information Science (pages 516-524)

Alexandre de Castro

In a seminal paper published in the early 1980s titled “Information Technology and the Science of Information,” Bertram C. Brookes theorized that a Shannon-Hartley's logarithmic-like measure could be applied to both information...

Back Materials

Compilation of References

About the Contributors

Index

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