Surekha P. – författare

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.

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, "e;OMIC"e; 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 needs This book is aimed at researchers and graduate students in bioinformatics and data analytics.

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, "e;OMIC"e; 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 needs This 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.

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 watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.

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 watermarking Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models Includes experimental analyses and results of test systems

Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.