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

Nikhil Kumar Marriwala

/ ISBN 13: 9798369368640

Verlag: IGI Global, 2025

Hardcover. 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9798369368640

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor: Dr Vinod Kumar Shukla is currently working with Amity University, Dubai, U.A.E., as Associate Professor and Head of Academics for the Engineering Architecture and Interior Design department. He has more than 15 years of experience. He has completed his PhD in "Semantic Web and Ontology" and is an active member of IEEE. He has appeared in the prestigious Stanford's list, USA of top 2% of scientists for in 2022. He has authored 100+ research publications in journals, book chapters and Conferences, and have 500+ plus Scopus citations. He has edited books in the field of Data Science, Artificial Intelligence, Blockchain, Industry 4.0 and Healthcare, and People Analytics. He is the recipient of various awards such as the "Global Academic Excellence Award- 2021", "Faculty Achievement Award" under the category of research publication in continuous two years 2021, 2022, "Best Paper Award" in the International Conference on Machine Intelligence and Data Science Application- 2020". His research area includes cyber security, the Internet of Things, and Blockchain. Currently, he is working on the books and exploring the transition of technologies and their possible impact from Industry 4.0 to Industry 5.0.

Bibliografische Details

Titel: Hybrid Soft Computing Techniques for Machine...
Verlag: IGI Global
Erscheinungsdatum: 2025
Einband: Hardcover
Zustand: new