Ujjwal Maulik's Books

Quantum Inspired Computational Intelligence

Research and Applications

By: Siddhartha Bhattacharyya,Ujjwal Maulik,Paramartha Dutta

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Quantum Inspired Computational Intelligence

Research and Applications

By: Siddhartha Bhattacharyya,Ujjwal Maulik,Paramartha Dutta

Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence—or soft computing—is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information. - Brings together quantum computing with computational intelligence to achieve enhanced performance and robust solutions - Includes numerous case studies, tools, and technologies to apply the concepts to real world practice - Provides the missing link between the research and practice

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508

Publisher

Morgan Kaufmann

Published Date

ISBN 10

0128044373

ISBN 13

9780128044377

Computational Intelligence and Pattern Analysis in Biology Informatics

By: Ujjwal Maulik,Sanghamitra Bandyopadhyay,Jason T. Wang

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Computational Intelligence and Pattern Analysis in Biology Informatics

By: Ujjwal Maulik,Sanghamitra Bandyopadhyay,Jason T. Wang

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters.

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Page Count

552

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118097807

ISBN 13

9781118097809

Quantum Inspired Meta-heuristics for Image Analysis

By: Sandip Dey,Siddhartha Bhattacharyya,Ujjwal Maulik

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Quantum Inspired Meta-heuristics for Image Analysis

By: Sandip Dey,Siddhartha Bhattacharyya,Ujjwal Maulik

Introduces quantum inspired techniques for image analysis for pure and true gray scale/color images in a single/multi-objective environment This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis. Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions. Provides in-depth analysis of quantum mechanical principles Offers comprehensive review of image analysis Analyzes different state-of-the-art image thresholding approaches Detailed current, popular standard meta-heuristics in use today Guides readers step by step in the build-up of quantum inspired meta-heuristics Includes a plethora of real life case studies and applications Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counterparts Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.

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Page Count

358

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119488788

ISBN 13

9781119488781

Soft Computing for Image and Multimedia Data Processing

By: Siddhartha Bhattacharyya,Ujjwal Maulik

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Soft Computing for Image and Multimedia Data Processing

By: Siddhartha Bhattacharyya,Ujjwal Maulik

Proper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.

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Page Count

278

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642402550

ISBN 13

9783642402555

Quantum Inspired Computational Intelligence

Research and Applications

By: Siddhartha Bhattacharyya,Ujjwal Maulik,Paramartha Dutta

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Quantum Inspired Computational Intelligence

Research and Applications

By: Siddhartha Bhattacharyya,Ujjwal Maulik,Paramartha Dutta

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508

Publisher

Morgan Kaufmann

Published Date

ISBN 10

0128044373

ISBN 13

9780128044377

Multiobjective Genetic Algorithms for Clustering

Applications in Data Mining and Bioinformatics

By: Ujjwal Maulik,Sanghamitra Bandyopadhyay,Anirban Mukhopadhyay

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Multiobjective Genetic Algorithms for Clustering

Applications in Data Mining and Bioinformatics

By: Ujjwal Maulik,Sanghamitra Bandyopadhyay,Anirban Mukhopadhyay

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

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Page Count

292

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642166156

ISBN 13

9783642166150

Advanced Methods for Knowledge Discovery from Complex Data

By: Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook

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Advanced Methods for Knowledge Discovery from Complex Data

By: Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.

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