Ashutosh Kumar Dubey - Böcker

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.

Includes real-time applications that illustrates artificial intelligence and machine learning for various renewable systems Features a templated approach that can be used to explore results, with scientific implications followed by detailed case studies Covers computational capabilities and varieties for renewable system design

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.

Includes case studies on the application of AI and machine learning for monitoring climate change effects and management Features applications of software and algorithms for modeling and forecasting climate change Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

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 bookExplores interdisciplinary research aspects in a coherent manner for materials science researchers Presents key concepts of engineering sciences as relevant for materials science in terms of fundamentals and applications Discusses engineering mechanics, biological and physical sciences Includes relevant case studies and examples

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.

Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier’s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source

Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future.Offers a holistic understanding of the application of computational automation in water quality managementContains practical and unique updates to help learners how to apply computational techniques to address water quality challengesProvides a comprehensive and multidisciplinary perspective on water quality management

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 bookExplores interdisciplinary research aspects in a coherent manner for materials science researchers Presents key concepts of engineering sciences as relevant for materials science in terms of fundamentals and applications Discusses engineering mechanics, biological and physical sciences Includes relevant case studies and examples

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:Presents data-driven intelligent heuristics for improving and advancing environmental sustainability in both eco-cities and smart cities. Discusses various efficient control systems for distributed and hybrid renewable energy sources and enhance the scope of smart energy management systems for developing even intelligent systems. Showcases how distributed energy systems improve the data-driven robots in the Internet of Medical Things. Highlights practical approaches to optimize power generation, reduce costs through efficient energy, and reduce greenhouse gas emissions to the possible minimum. Covers bio-inspired swarm models, smart data-driven sensing to combat environmental issues, and futuristic data-driven enabled schemes in blockchain-fog-cloud assisted medical eny ecosystem.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.

The 24 chapters in this book provides a deep overview of robotics and the application of AI and IoT in robotics. It contains the exploration of AI and IoT based intelligent automation in robotics. The various algorithms and frameworks for robotics based on AI and IoT are presented, analyzed, and discussed. This book also provides insights on application of robotics in education, healthcare, defense and many other fields which utilize IoT and AI. It also introduces the idea of smart cities using robotics.

SUSTAINABLE MANAGEMENT OF ELECTRONIC WASTEWritten 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.

Lead the sustainable energy revolution with this guide to mastering the AI-driven algorithms and smart material innovations that are revolutionizing solar energy. The integration of artificial intelligence into solar energy systems represents the next frontier in sustainable development, promising to improve efficiency, reduce costs, and increase the viability of solar energy as a mainstream energy source. This book will delve into the transformative role of artificial intelligence in enhancing various aspects of solar energy systems. It will begin by exploring how AI can significantly boost the energy efficiency of solar panels, showcasing innovative algorithms and techniques designed to optimize energy capture and conversion. The development of smart materials for enhanced energy storage will also be covered, emphasizing the latest advancements in material science driven by AI to improve the storage capabilities and longevity of solar panels. Further, it will address integrated waste management options for exhausted solar panels, providing insights into sustainable practices and AI-driven solutions for recycling and repurposing solar panel components. It will discuss the significance of AI in solar energy conservation and climate change management, illustrating how AI technologies are being harnessed to predict, monitor, and mitigate environmental impacts. Additionally, the book will explore the future scope of photovoltaic-based solar energy in a changing environment, highlighting AI’s role in achieving sustainability and adapting to evolving climatic conditions. Using case studies and real-world applications, this book will demonstrate successful implementations of AI in the solar energy sector. Topics such as predictive maintenance, solar irradiance forecasting, optimal placement of solar panels, and AI-enhanced solar tracking systems will be featured to provide a comprehensive understanding of how AI is revolutionizing the solar energy landscape.

Revolutionize your approach to environmental protection with this groundbreaking resource, which details how to replace labor-intensive manual analysis with deep learning and explainable AI (XAI) to achieve precise, real-time identification and scalable monitoring of microplastic pollution. AI-driven microplastic monitoring sits at the intersection of environmental science, artificial intelligence, and data analytics, representing a rapidly developing frontier in both research and industry. Microplastic pollution, which has become a critical environmental and public health concern, is challenging to monitor using traditional techniques due to the vast scale, complexity, and minute size of microplastics. Conventional methods, such as manual filtration, microscopic examination, and chemical analysis, are often labor-intensive, time-consuming, and limited in their ability to provide real-time, large-scale data. This book is a groundbreaking exploration of how artificial intelligence, particularly deep learning and explainable AI (XAI), is revolutionizing microplastic research. It highlights innovative applications of deep learning for precise identification and classification of microplastics, while emphasizing the role of XAI in providing transparency and interpretability to AI-driven methods. By integrating these approaches with advanced sensing technologies and predictive models, the book addresses key limitations of traditional methods, offering robust solutions for scalable and accurate monitoring. Additionally, the book considers the ethical, regulatory, and policy implications of deploying AI in environmental science, providing a balanced perspective on the potential benefits and challenges. With contributions from leading researchers and practitioners, this book is an essential resource for environmental scientists, data scientists, policymakers, and technologists committed to sustainable solutions for combating microplastic pollution.

A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs.Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed.This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.

Classical computers encode information in binary bits that can either be 0s or 1s. In a quantum computer, the basic unit of memory is a quantum bit or qubit. These qubits play a similar role in terms of storing information, but use physical systems, such as the spin of an electron or the orientation of a photon, to do so. In situations where there are a large number of possible feature combinations, quantum computers can consider them simultaneously, speeding up the data processing time.In healthcare, where there are often large numbers of possible factors to consider, quantum computers can address them simultaneously, thereby allowing doctors to compare much, much more data, and all permutations of that data, in parallel to discover the best patterns that describe it, and therefore predict the best treatment options.This edited book explores the field of quantum computing and machine learning for medical data processing, and is a useful resource for computer engineers, researchers, healthcare technologists and scientists specialising in quantum computing, quantum AI, data processing, deep learning, machine learning, smart healthcare, and medical data systems.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare.

This book provides basic concepts and deep knowledge about various security mechanisms that can be implemented in IoT through Blockchain technology. This book gives descriptive analysis of Blockchain integrated with IoT applications and platforms for the development of IoT solutions along with possible topologies to that integration.

This book provides basic concepts and deep knowledge about various security mechanisms that can be implemented in IoT through Blockchain technology. This book gives descriptive analysis of Blockchain integrated with IoT applications and platforms for the development of IoT solutions along with possible topologies to that integration.

This book discusses how changes in climate can affect biodiversity, the natural world, and human society. The exploitation of natural resources, improperly managed development projects, human activities, and other factors are causing regular changes in the global environmental circumstances. Because of its severe effects on living things, climate change is one of the main problems that has attracted the attention of scientists, policy officials, academics, businesses, and other stakeholders worldwide. The main adverse implications of climate change are human health problems, changes in mean sea level, lower agricultural output, the spread of vectors, and degradation of the ecosystem. Utilizing cutting-edge techniques such as robotics, deep learning, machine learning, and artificial intelligence help construct greener, more intelligent energy systems or boost energy efficiency to mitigate the harmful consequences of climate change. Furthermore, by using real-time data analysis, these strategies might be useful in monitoring and anticipating climate challenges. It helps in developing novel approaches to quantifying the effects of climate change by taking into account the infrastructure that is currently in place, plant growth, power use, and emissions from vehicles and industries. In order to reduce the consequences, recent techniques are helpful in the research and management of climate-related concerns are also thoroughly explored. This is also helpful for future projections of weather phenomena along with climate extremes, etc. These methods are rapidly growing and widely applied in engineering and research. Therefore, it is anticipated that a large number of researchers are able to provide excellent reviews, case studies, and research findings regarding how these strategies might assist the globe to cope with climate variability. Additionally, this book also includes other strategies for lowering the risks associated with climate change as well as various forecasting techniques for climate uncertainty. Additionally, this book contributes to the accomplishment sustainable development goals (SDGs).

This book is to explore and explain the strategically sound capabilities at the synchronization between quantum computing and artificial intelligence (AI). The reader will be presented with an introduction and a deeper review of the technological trends and transitions being unearthed in the quantum computing and AI domains.

The goal of this book is to facilitate and stimulate cross-disciplinary research in the emerging paradigm of Medical Imaging. Especially this book is to focus on analysing and articulating proven and potential security measures to tightly secure Medical Image applications and services, which are being hosted and delivered through cloud infrastructures and platforms. This book will illustrate the prominent advancements in image processing and how intelligent image-processing techniques can be developed and deployed in the industrial market and for academicians. The readers will get to know all the right and relevant details to be empowered to successfully contribute to their personal and professional growth. The main focus of this book is to bring all the related technologies, novel findings, and managerial applications of Medical Imaging on a single platform to provide great readability, easy understanding, and smooth adaptability of various basic and advanced concepts to Researchers in Medical Engineers, Machine Learning and Data Analysis.