S. Sumathi – författare

Offering a wide range of programming examples implemented in MATLAB®, Computational Intelligence Paradigms: Theory and Applications Using MATLAB® presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi–Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.Features:Explains the basic concepts of Python and its role in machine learningProvides comprehensive coverage of feature engineering including real-time case studiesPerceives the structural patterns with reference to data science and statistics and analyticsIncludes machine learning-based structured exercisesAppreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learningThis book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.Features:Explains the basic concepts of Python and its role in machine learningProvides comprehensive coverage of feature engineering including real-time case studiesPerceives the structural patterns with reference to data science and statistics and analyticsIncludes machine learning-based structured exercisesAppreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learningThis book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

Bioinformatics deals with computational and mathematical approaches for understanding and processing biological data. This book highlights the computational procedures and the applications evolving around biological data, including usage of decision sciences for a variety of applications, namely text mining, "OMIC" sciences, systems biology, analyzing biological/medical images, computer-aided diagnosis/treatment of diseases, decision sciences for public health with COVID-19 data, biodiversity, smart wearables, personalized medicine, data deluge issues, and knowledge management. Key Features: Presents exclusive material on decision sciences in bioinformatics Highlights the computational procedures and applications evolving around biological data Provides solutions to problems in bioinformatics with decision sciences Addresses the research gaps in bioinformatics Includes case studies emphasizing societal needsThis book is aimed at researchers and graduate students in bioinformatics and data analytics.

Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems.Focusing on the practical implementation of CI techniques, this book: Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarkingExplains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applicationsShows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® modelsIncludes experimental analyses and results of test systemsComputational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.

Offering a wide range of programming examples implemented in MATLAB®, Computational Intelligence Paradigms: Theory and Applications Using MATLAB® presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi–Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems.Focusing on the practical implementation of CI techniques, this book: Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarkingExplains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applicationsShows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® modelsIncludes experimental analyses and results of test systemsComputational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

Information is a valuable resource to an organization. Computer software provides an efficient means of processing information, and database systems are becoming an increasingly common means by which it is possible to store and retrieve information in an effective manner. This book provides comprehensive coverage of fundamentals of database management systems. This book is for those who wish a better understanding of relational data modeling, its purpose, its nature, and the standards used in creating relational data models.

Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

Information is a valuable resource to an organization. Computer software provides an efficient means of processing information, and database systems are becoming an increasingly common means by which it is possible to store and retrieve information in an effective manner. This book provides comprehensive coverage of fundamentals of database management systems. This book is for those who wish a better understanding of relational data modeling, its purpose, its nature, and the standards used in creating relational data models.

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining.

Readers will be informed about seeds, the science and technology associated with them. Seeds are fertilized mature ovules created through sexual reproduction in plants, and they are the least expensive and most crucial input in agriculture. It is estimated that high-quality seeds of improved varieties can lead to a 20-25% increase in yield. Seed technology is an interdisciplinary field that encompasses a range of activities, including variety development, evaluation, and release; seed development; seed processing; seed storage; seed testing; seed certification; seed quality control; and seed marketing. These activities aim to improve the genetic and physical characteristics of seeds. Each topic is discussed in a separate chapter, and this book will be highly beneficial to its readers.

This publication was created with the specific purpose of providing undergraduate students with a comprehensive guide to seed science and technology, tailored to the requirements of various institutions. Given the vast amount of available materials on the subject, the content has been carefully consolidated and presented in a clear, concise format.The text provides a detailed overview of the procedures involved in producing high-quality seeds for 40 horticultural crops. The techniques used in both the certified and foundation stages of seed production, from seed selection to storage, are covered in detail, along with the relevant quality standards and storage methods. The publication is intended to serve as a valuable resource for seed growers, researchers, teachers, students, planners, NGOs, and extension personnel, providing them with a thorough understanding of seed production in horticultural crops. We hope that readers will find this book, titled Scientific Seed Production of Horticultural Crops, to be a useful and informative guide to the subject. Please note that the information provided in this publication is accurate as of the knowledge cutoff date of September 2021.

This book provides readers with valuable information about the science and technology involved in seeds. Seeds are the result of sexual reproduction in plants and are considered the most crucial and cost-effective input in agriculture. The use of high-quality seeds from improved varieties can lead to a 20-25% increase in crop yield. Seed technology is an interdisciplinary field that encompasses a range of activities such as variety development, seed processing, storage, testing, certification, quality control, and marketing. These activities work together to improve the genetic and physical characteristics of seeds, ultimately leading to better crop yields. The book is organized into separate chapters, making it an invaluable resource for readers looking to gain a deeper understanding of seed technology.

This book has been specifically designed to meet the requirements of undergraduate students studying seed science and technology at various institutions. The abundance of available materials on this subject has been taken into consideration, and the aim is to present the information in a consolidated and simplified manner.The publication details the methods for producing high-quality seeds of 40 horticultural crops, covering the entire process from seed selection to storage. The book provides a comprehensive overview of the quality standards and appropriate storage methods involved in the certified and foundation stages of seed production.This book is intended to be a valuable resource for seed growers, researchers, teachers, students, planners, NGOs, and extension personnel. It is hoped that the information provided in this publication will enhance the readers understanding of seed production for horticultural crops. The title of this book, Scientific Seed Production of Horticultural Crops, reflects its focus on presenting the scientific aspects of seed production in a clear and accessible manner.