Hybrid Soft Computing Techniques for Machine Learning and Optimization (Paperback)

Nikhil Kumar Marriwala

ISBN 13: 9798369368688

Published by IGI Global, Hershey, PA, 2025

About this Item

Paperback. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798369368688

Bibliographic Details

• Title: Hybrid Soft Computing Techniques for Machine Learning and Optimization (Paperback)
• Publisher: IGI Global, Hershey, PA
• Publication Date: 2025
• Language: English
• ISBN 13: 9798369368688
• Binding: Paperback
• Condition: new

About this title

Synopsis

Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more.