Automated Machine Learning and Industrial Applicat ions

E.N Gangadevi

ISBN 10: 1394272391 / ISBN 13: 9781394272396

Published by Wiley-Scrivener, 2025

About this Item

Seller Inventory # 1417565870

Bibliographic Details

• Title: Automated Machine Learning and Industrial Applicat ions
• Publisher: Wiley-Scrivener
• Publication Date: 2025
• Language: English
• ISBN 10: 1394272391
• ISBN 13: 9781394272396
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The book provides a comprehensive understanding of Automated Machine Learning’s transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise.

Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with machine learning models and techniques without requiring much knowledge in machine learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal machine learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML.

Automated Machine Learning and Industrial Applications offers an overview of the basic architecture, evolution, and applications of AutoML. Potential applications in healthcare, banking, agriculture, aerospace, and security are discussed in terms of their frameworks, implementation, and evaluation. This book also explores the AutoML ecosystem, its integration with blockchain, and various open-source tools available on the AutoML platform. It serves as a practical guide for engineers and data scientists, offering valuable insights for decision-makers looking to integrate machine learning into their workflows.

Readers will find the book:

• Aims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0;
• Serves as an effective guide for professionals, researchers, industrialists, data scientists, and application developers;
• Explores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students.

Audience

Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications.

About the Author

E. Gangadevi, PhD is an assistant professor in the Department of Computer Science at Loyola College, Chennai, India. She has published two patents, six books, over 18 research papers in international journals, and many book chapters. Her areas of research are machine learning, deep learning, IoT, and cloud computing.

M. Lawanya Shri, PhD is an associate professor in the School of Information Technology and Engineering at Vellore Institute of Technology, India. She has published two books, two patents, and over 50 articles and papers in refereed journals and international conferences. Her research interests include blockchain technology, machine learning, cloud computing, and IOT.

Balamurugan Balusamy, PhD is an associate dean at Shiv Nadar University, Delhi, India with over 12 years of teaching experience. He has published more than 200 papers in international journals, 80 books, and given over 195 talks at various international events and symposia. His contributions focus on engineering education, blockchain, and data sciences.

Rajesh Kumar Dhanaraj, PhD is a professor in the School of Computing Science and Engineering at Symbiosis University, Pune, India. He has contributed to over 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and conferences. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.