T. Rajasanthosh Kumar – författare

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following:Developing a smart algorithm to integrate fault detection and classificationAlgorithms to investigate different testing scenarios for various anomalies in electric motorsData fusion to detect and assess electromechanical damageNeural networks for rolling bearing fault diagnosisEvolutionary algorithms to optimize deep learning models for water industry forecastsAI-based anomaly detection and root-cause analysisAn overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

The objective of this publication AI Modernisation Techniques for Contemporary Trends is to offer a comprehensive and practical framework for engineers, technicians, and individuals interested in converting conventional electromechanical systems into intelligent, interconnected, and efficient smart systems. The rising demand for smart technologies necessitates the need of upgrading current systems to effectively cater to these expectations. This book will serve as an extensive resource that aims to facilitate the integration of ancient and contemporary technologies, providing readers with guidance during the whole process of conversion.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following: Developing a smart algorithm to integrate fault detection and classification Algorithms to investigate different testing scenarios for various anomalies in electric motors Data fusion to detect and assess electromechanical damage Neural networks for rolling bearing fault diagnosis Evolutionary algorithms to optimize deep learning models for water industry forecasts AI-based anomaly detection and root-cause analysis An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following: Developing a smart algorithm to integrate fault detection and classification Algorithms to investigate different testing scenarios for various anomalies in electric motors Data fusion to detect and assess electromechanical damage Neural networks for rolling bearing fault diagnosis Evolutionary algorithms to optimize deep learning models for water industry forecasts AI-based anomaly detection and root-cause analysis An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

The objective of this publication AI Modernisation Techniques for Contemporary Trends is to offer a comprehensive and practical framework for engineers, technicians, and individuals interested in converting conventional electromechanical systems into intelligent, interconnected, and efficient smart systems. The rising demand for smart technologies necessitates the need of upgrading current systems to effectively cater to these expectations. This book will serve as an extensive resource that aims to facilitate the integration of ancient and contemporary technologies, providing readers with guidance during the whole process of conversion.

The objective of this publication AI Modernisation Techniques for Contemporary Trends is to offer a comprehensive and practical framework for engineers, technicians, and individuals interested in converting conventional electromechanical systems into intelligent, interconnected, and efficient smart systems. The rising demand for smart technologies necessitates the need of upgrading current systems to effectively cater to these expectations. This book will serve as an extensive resource that aims to facilitate the integration of ancient and contemporary technologies, providing readers with guidance during the whole process of conversion.

Future-proof your technical expertise by mastering the multidisciplinary synergy of AI-driven optimization and intelligent algorithms with this essential resource to optimization for the modern world. The rapid evolution of artificial intelligence, machine learning, and data-driven optimization has transformed the landscape of scientific research and industrial innovation. This book presents a diverse and multidisciplinary collection of contemporary studies that advance theoretical foundations, computational techniques, and application-driven solutions across multiple domains to reflect the growing importance of optimization, intelligent systems, and hybrid analytical frameworks in solving complex real-world problems. This essential guide focuses on next-generation technologies such as AI-driven cybersecurity, dynamic optimization in 6G networks, fuzzy logic-based energy management, automated neural machine translation, and GAN-driven aerodynamic shape design, illustrating the synergy between intelligent algorithms and modern engineering systems. Whether you are a researcher, practitioner, or simply an enthusiast, this book will serve as an enlightening resource, providing valuable insights into the ever-evolving realm of optimization for the modern world. Readers will find the volume: Provides comprehensive coverage of the backgrounds, foundations, and theoretical ideas of optimization methods and their potential applications;Introduces applications of fuzzy sets for solving optimization problems in real-life scenarios;Explores the transformative potential of fuzzy sets for optimization across several domains like healthcare, finance, and education.Audience Researchers, academics, industry experts, scientists, and technologists looking for insights into advances across optimization theory, machine learning, deep learning, cybersecurity, healthcare analytics, energy systems, and intelligent decision-making.

Future-proof your technical expertise by mastering the multidisciplinary synergy of AI-driven optimization and intelligent algorithms with this essential resource to optimization for the modern world. The rapid evolution of artificial intelligence, machine learning, and data-driven optimization has transformed the landscape of scientific research and industrial innovation. This book presents a diverse and multidisciplinary collection of contemporary studies that advance theoretical foundations, computational techniques, and application-driven solutions across multiple domains to reflect the growing importance of optimization, intelligent systems, and hybrid analytical frameworks in solving complex real-world problems. This essential guide focuses on next-generation technologies such as AI-driven cybersecurity, dynamic optimization in 6G networks, fuzzy logic-based energy management, automated neural machine translation, and GAN-driven aerodynamic shape design, illustrating the synergy between intelligent algorithms and modern engineering systems. Whether you are a researcher, practitioner, or simply an enthusiast, this book will serve as an enlightening resource, providing valuable insights into the ever-evolving realm of optimization for the modern world. Readers will find the volume: Provides comprehensive coverage of the backgrounds, foundations, and theoretical ideas of optimization methods and their potential applications;Introduces applications of fuzzy sets for solving optimization problems in real-life scenarios;Explores the transformative potential of fuzzy sets for optimization across several domains like healthcare, finance, and education. Audience Researchers, academics, industry experts, scientists, and technologists looking for insights into advances across optimization theory, machine learning, deep learning, cybersecurity, healthcare analytics, energy systems, and intelligent decision-making.

Future-proof your technical expertise by mastering the multidisciplinary synergy of AI-driven optimization and intelligent algorithms with this essential resource to optimization for the modern world. The rapid evolution of artificial intelligence, machine learning, and data-driven optimization has transformed the landscape of scientific research and industrial innovation. This book presents a diverse and multidisciplinary collection of contemporary studies that advance theoretical foundations, computational techniques, and application-driven solutions across multiple domains to reflect the growing importance of optimization, intelligent systems, and hybrid analytical frameworks in solving complex real-world problems. This essential guide focuses on next-generation technologies such as AI-driven cybersecurity, dynamic optimization in 6G networks, fuzzy logic-based energy management, automated neural machine translation, and GAN-driven aerodynamic shape design, illustrating the synergy between intelligent algorithms and modern engineering systems. Whether you are a researcher, practitioner, or simply an enthusiast, this book will serve as an enlightening resource, providing valuable insights into the ever-evolving realm of optimization for the modern world. Readers will find the volume: Provides comprehensive coverage of the backgrounds, foundations, and theoretical ideas of optimization methods and their potential applications;Introduces applications of fuzzy sets for solving optimization problems in real-life scenarios;Explores the transformative potential of fuzzy sets for optimization across several domains like healthcare, finance, and education. Audience Researchers, academics, industry experts, scientists, and technologists looking for insights into advances across optimization theory, machine learning, deep learning, cybersecurity, healthcare analytics, energy systems, and intelligent decision-making.