Kanak Kalita – författare

The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications.

The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases.

In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field.

Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence.

Audience

The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.

The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications.

The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases.

In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field.

Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence.

Audience

The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.

This book is an essential guide for anyone looking to drive sustainable technological innovation, providing a comprehensive toolkit of decision-making methods and real-world applications to effectively manage technology in the era of Industry 5.0.

Sustainable technological innovation is critical for building a more sustainable future. As the world faces increasing environmental challenges, there is a pressing need for new and innovative technologies that can reduce resource consumption, mitigate environmental impacts, and promote sustainable development. This book focuses on the vital role of decision-making processes in achieving sustainability through technological innovation in the context of Industry 5.0. By delving into various decision-making methods and approaches employed to facilitate sustainable technological innovation across essential industries such as manufacturing, agriculture, and energy, the book will present both theoretical and applied research on managing technology, including decision-making connected to Industry 4.0 and 5.0, artificial intelligence, and other revolutionary techniques.

The book covers a wide range of topics, including multiple attribute decision theory, multiple objective decision-making, patent mining, big data analytics, and other decision-making methods and techniques, and features case studies and reviews that highlight real-world applications of sustainable technological innovation in different industries. The exploration of various decision-making methods and approaches for sustainable technological innovation makes this book an essential guide for those looking toward a sustainable Industry 5.0.

Readers will find the book:

Audience

Researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics, specifically interested in decision analytics and machine learning algorithms.

This book is an essential guide for anyone looking to drive sustainable technological innovation, providing a comprehensive toolkit of decision-making methods and real-world applications to effectively manage technology in the era of Industry 5.0.

Sustainable technological innovation is critical for building a more sustainable future. As the world faces increasing environmental challenges, there is a pressing need for new and innovative technologies that can reduce resource consumption, mitigate environmental impacts, and promote sustainable development. This book focuses on the vital role of decision-making processes in achieving sustainability through technological innovation in the context of Industry 5.0. By delving into various decision-making methods and approaches employed to facilitate sustainable technological innovation across essential industries such as manufacturing, agriculture, and energy, the book will present both theoretical and applied research on managing technology, including decision-making connected to Industry 4.0 and 5.0, artificial intelligence, and other revolutionary techniques.

The book covers a wide range of topics, including multiple attribute decision theory, multiple objective decision-making, patent mining, big data analytics, and other decision-making methods and techniques, and features case studies and reviews that highlight real-world applications of sustainable technological innovation in different industries. The exploration of various decision-making methods and approaches for sustainable technological innovation makes this book an essential guide for those looking toward a sustainable Industry 5.0.

Readers will find the book:

Audience

Researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics, specifically interested in decision analytics and machine learning algorithms.

Master the complexity of modern networks with this essential guide, which provides the state-of-the-art AI and machine learning techniques needed to execute seamless sensor data fusion and energy-efficient aggregation across Industrial IoT and smart city environments.

The use of artificial intelligence and machine learning techniques for data aggregation and fusion is becoming increasingly important, as these technologies can help extract important features and knowledge from data. Sensor data aggregation and fusion are essential components of IoT and Industrial IoT systems, as they enable the combination of data from multiple sources to provide a more comprehensive view of the system being monitored. This book is a comprehensive guide to the state-of-the-art techniques and methods used for sensor data aggregation and fusion in IoT and Industrial IoT environments, covering the fundamental principles of data aggregation and fusion, as well as the latest advancements and applications in the field. The book takes a practical approach to the subject matter, providing a deeper understanding of the challenges and opportunities associated with sensor data aggregation and fusion in IoT and Industrial IoT environments. It covers topics such as machine learning-based data aggregation, intelligent multi-sensor fusion, data aggregation and fusion in smart cities, and energy-efficient data aggregation and fusion. Written by leading experts in the field, the book will provide a comprehensive overview of the latest advancements in sensor data aggregation and fusion in IoT and Industrial IoT environments.

The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape.

In today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. Medical Analytics for Clinical and Healthcare Applications provides a comprehensive examination of how data-driven methodologies are revolutionizing the medical field. This book offers a deep dive into innovative techniques, real-world applications, and emerging trends in medical analytics, showcasing how these advancements are transforming disease detection, diagnosis, treatment planning, and healthcare management.

Spanning sixteen chapters across five subsections, this edited volume covers a wide array of topics—from foundational principles of medical data analysis to cutting-edge applications in predictive healthcare and medical data security. Readers will encounter state-of-the-art methodologies, including machine learning models, predictive analytics, and deep learning techniques applied to various healthcare challenges such as mental health disorders, cancer detection, and hospital mortality predictions. Medical Analytics for Clinical and Healthcare Applications equips readers with the knowledge to harness the power of medical analytics and its potential to shape the future of healthcare. Through its interdisciplinary approach and expert insights, this volume is poised to serve as a valuable resource for advancing healthcare technologies and improving the overall quality of care.

Readers will find the volume:

Audience

Healthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics.

The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape.

In today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. Medical Analytics for Clinical and Healthcare Applications provides a comprehensive examination of how data-driven methodologies are revolutionizing the medical field. This book offers a deep dive into innovative techniques, real-world applications, and emerging trends in medical analytics, showcasing how these advancements are transforming disease detection, diagnosis, treatment planning, and healthcare management.

Spanning sixteen chapters across five subsections, this edited volume covers a wide array of topics—from foundational principles of medical data analysis to cutting-edge applications in predictive healthcare and medical data security. Readers will encounter state-of-the-art methodologies, including machine learning models, predictive analytics, and deep learning techniques applied to various healthcare challenges such as mental health disorders, cancer detection, and hospital mortality predictions. Medical Analytics for Clinical and Healthcare Applications equips readers with the knowledge to harness the power of medical analytics and its potential to shape the future of healthcare. Through its interdisciplinary approach and expert insights, this volume is poised to serve as a valuable resource for advancing healthcare technologies and improving the overall quality of care.

Readers will find the volume:

Audience

Healthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics.

This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches.

The chapters are meticulously selected and organized in three sections—metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights.

Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.

This book unravels the intriguing interplay between macroscopic manufacturing processes and microscopic fabrication techniques. It dives into the sophisticated world of precision manufacturing, where high accuracy, controlled processes enable the production of complex components and products. It covers micro and nano fabrication, which revolutionizes conventional manufacturing by creating minuscule yet highly functional parts, some even smaller than the width of a human hair. This book explores various topics, from precise machining techniques to nanoimprint technology, reflecting the vast breadth and depth of this field. The aim is to provide readers with a comprehensive understanding of how these micro and macro scales intertwine, opening new frontiers in manufacturing. By showcasing the latest research findings and their practical applications, this book elucidates the enormous potential and implications of this burgeoning field. The contents are laid out in a user-friendly manner to communicate complex ideas in an accessible, engaging way, making it a valuable resource for anyone curious about the next big leap in manufacturing technology.

This book unravels the intriguing interplay between macroscopic manufacturing processes and microscopic fabrication techniques. It dives into the sophisticated world of precision manufacturing, where high accuracy, controlled processes enable the production of complex components and products. It covers micro and nano fabrication, which revolutionizes conventional manufacturing by creating minuscule yet highly functional parts, some even smaller than the width of a human hair. This book explores various topics, from precise machining techniques to nanoimprint technology, reflecting the vast breadth and depth of this field. The aim is to provide readers with a comprehensive understanding of how these micro and macro scales intertwine, opening new frontiers in manufacturing. By showcasing the latest research findings and their practical applications, this book elucidates the enormous potential and implications of this burgeoning field. The contents are laid out in a user-friendly manner to communicate complex ideas in an accessible, engaging way, making it a valuable resource for anyone curious about the next big leap in manufacturing technology.