Ashutosh Kumar Dubey – författare

Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.

Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world.

This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action.

Interdisciplinary Engineering Sciences introduces and emphasizes the importance of the interdisciplinary nature of education and research from a materials science perspective. This approach is aimed to promote understanding of the physical, chemical, biological and engineering aspects of any materials science problem. Contents are prepared to maintain the strong background of fundamental engineering disciplines while integrating them with the disciplines of natural science. It presents key concepts and includes case studies on biomedical materials and renewable energy.

Aimed at senior undergraduate and graduate students in materials science and other streams of engineering, this book

Interdisciplinary Engineering Sciences introduces and emphasizes the importance of the interdisciplinary nature of education and research from a materials science perspective. This approach is aimed to promote understanding of the physical, chemical, biological and engineering aspects of any materials science problem. Contents are prepared to maintain the strong background of fundamental engineering disciplines while integrating them with the disciplines of natural science. It presents key concepts and includes case studies on biomedical materials and renewable energy.

Aimed at senior undergraduate and graduate students in materials science and other streams of engineering, this book

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity.

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.

This book is useful for research scholars and students involved in critical condition analysis and computation models.

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity.

Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.

This book is useful for research scholars and students involved in critical condition analysis and computation models.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems.

This book:

The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems.

This book:

The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

SUSTAINABLE MANAGEMENT OF ELECTRONIC WASTE

Written and edited by a group of industry professionals, this new volume provides cutting-edge insights into how the sustainability of managing electronic waste can be achieved, for engineers, scientists, and students.

As a result of the rapid advancement of technology and the globalization of the economy, waste electrical and electronic equipment (WEEE) management has become increasingly important. Manufacturers are especially concerned about the proper disposal of their waste, and researchers need to identify the obstacles and enablers that stand in the way of implementing a long-term WEEE management system in order to develop a long-term WEEE management system. Further, the literature did not adequately capture the perspectives of multiple stakeholders while also identifying the enablers required for the development of sustainable WEEE management policies, which was particularly important in developing countries.

This volume fills a gap in the literature by considering the perspectives of multiple stakeholders to identify enablers of sustainable WEEE management in emerging economies which was previously unexplored. This book focuses on the most recent technological advancements for the twenty-first century, emphasizing the synergies that exist between computer science, bioinformatics, and other sciences. The research and development of artificial intelligence, machine learning, blockchain technologies, quantum computing with cryptography, nanotechnology, sensors based on biotechnology, Internet of Things devices, nature-inspired algorithms, computer vision techniques, computational biology, and other topics are covered in this book, along with their applications in the fields of science, engineering, physical science, and economics. Modern environmental techniques are among the most innovative innovations emerging as a result of the insatiable demand for health standards in the modern world.

SUSTAINABLE MANAGEMENT OF ELECTRONIC WASTE

Written and edited by a group of industry professionals, this new volume provides cutting-edge insights into how the sustainability of managing electronic waste can be achieved, for engineers, scientists, and students.

As a result of the rapid advancement of technology and the globalization of the economy, waste electrical and electronic equipment (WEEE) management has become increasingly important. Manufacturers are especially concerned about the proper disposal of their waste, and researchers need to identify the obstacles and enablers that stand in the way of implementing a long-term WEEE management system in order to develop a long-term WEEE management system. Further, the literature did not adequately capture the perspectives of multiple stakeholders while also identifying the enablers required for the development of sustainable WEEE management policies, which was particularly important in developing countries.

This volume fills a gap in the literature by considering the perspectives of multiple stakeholders to identify enablers of sustainable WEEE management in emerging economies which was previously unexplored. This book focuses on the most recent technological advancements for the twenty-first century, emphasizing the synergies that exist between computer science, bioinformatics, and other sciences. The research and development of artificial intelligence, machine learning, blockchain technologies, quantum computing with cryptography, nanotechnology, sensors based on biotechnology, Internet of Things devices, nature-inspired algorithms, computer vision techniques, computational biology, and other topics are covered in this book, along with their applications in the fields of science, engineering, physical science, and economics. Modern environmental techniques are among the most innovative innovations emerging as a result of the insatiable demand for health standards in the modern world.

Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource.

Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models’ outputs.

Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.

Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource.

Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models’ outputs.

Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.