Pramod Singh Rathore – författare

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

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.

GANs in Medical Diagnostics and Classification explores the role of Generative Adversarial Networks (GANs) in addressing underdiagnosed and overlooked health challenges. The book bridges cutting-edge AI research with real-world medical applications, presenting interdisciplinary insights into how GANs enhance diagnostics, treatment precision, and disease surveillance across domains such as antimicrobial resistance, rare genetic disorders, and environmental health risks. It also examines ethical, policy, and accessibility dimensions of AI in healthcare. By combining technical depth with practical case studies, this volume equips medical researchers, AI engineers, and healthcare professionals with actionable knowledge to tackle silent epidemics using GAN-based tools.Delivers real-world case studies that demonstrate GAN applications in diagnosing rare diseases, detecting antimicrobial resistance, and enhancing medical imagingIntegrates interdisciplinary perspectives from medicine, AI, ethics, and policy to provide a holistic view of AI-driven healthcare innovationExplores emerging technologies such as synthetic data generation, wearable diagnostics, and AI-enhanced telemedicine for future-ready healthcare solutions

Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and real-world applications of explainable AI (XAI) within the medical context. Covering a wide range of use cases-from radiology and pathology to genomics and clinical decision support systems-the book provides in-depth discussions on how XAI techniques can enhance interpretability, improve clinician trust, meet regulatory requirements, and ultimately lead to better patient outcomes. The book demystifies the workings of machine learning models and highlights techniques that make them interpretable.It is designed to empower not only AI researchers and developers but also healthcare administrators and policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's authors bring together theory, implementation strategies, ethical implications, and case studies under one cover, offering a multidisciplinary perspective that aligns computer science with medical practice and healthcare policy.Presents detailed coverage of XAI methods such as SHAP, LIME, and Grad-CAM applied to medical dataProvides numerous case studies in diagnostics, ICU prediction, and radiology using explainable modelsIncludes discussions on ethics, bias, and regulatory frameworks such as GDPR and HIPAA

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.

In an effort to investigate the revolutionary potential of augmented reality (AR) and virtual reality (VR) technologies in the field of mechanical and medical emergency management, Virtual Reality, Real Emergency was written. This book seeks to educate readers on how AR and VR may transform response to emergencies, boost training, and facilitate better decision-making under pressure. It aims to equip professionals and hobbyists alike to use these immersive technologies for saving lives and minimizing calamities by analyzing real-world applications, cutting-edge breakthroughs, and practical insights.The ground-breaking book Virtual Reality, Real Emergency: Integrating AR/VR in Computing and Medical Crisis Management has two goals: to teach and to give readers agency in times of crisis. Our goal is to educate both industry experts and casual observers on the revolutionary possibilities of AR and VR in the high-stakes arenas of mechanical and medical emergency response. The gap between theory and practice is something we want to close by providing real-world insights, practical counsel, and examples of achievement. Our goal is to educate the public on the potential life-saving benefits of AR and VR technology, demystify these tools for the general reader, and provide concrete measures for incorporating them into existing emergency response procedures.This book not only celebrates the technological advances that have been accomplished but also calls for more cooperation to ensure that these innovations are implemented in a way that puts people’s lives first during emergencies.

Blockchain Technology in Water Management and New Energy Economy explores the transformative role of blockchain in addressing two of the most pressing global challenges: efficient water resource management and the transition to a sustainable energy future. As climate change, population growth, and urbanization exert increasing pressure on natural resources, traditional systems for managing water and energy are proving inadequate. This book presents blockchain as a powerful enabler of transparency, decentralization, and trust in resource governance, offering innovative solutions that can reshape these critical sectors. The book is divided into two major sections. The first focuses on water management, covering how blockchain can enhance monitoring, prevent water theft, support real-time quality assessments, and enable equitable distribution through smart contracts. It examines case studies where decentralized ledger technology has improved water conservation, optimized irrigation systems in agriculture, and facilitated peer-to-peer water trading.The second section delves into the new energy economy, highlighting blockchain’s potential in supporting renewable energy integration, smart grids, decentralized energy markets, and carbon credit tracking. It discusses how blockchain can empower consumers to become "prosumers" — both producing and consuming energy — while maintaining secure and traceable transactions. Additionally, the book explores cross-cutting themes such as data privacy, regulatory challenges, scalability, and the socio-economic impact of blockchain adoption. Through real-world examples, technical insights, and forward-looking perspectives, this book provides a comprehensive guide for researchers, policy makers, engineers, and sustainability advocates seeking to understand and apply blockchain technology for a greener and more resource-efficient future.

The book presents a forward-thinking and interdisciplinary collection of research contributions that seamlessly integrate technological innovation with the principles of sustainability. The core purpose of the book is to foster dialogue and collaboration focused on building intelligent, secure, and environmentally responsible computing systems. As the digital landscape rapidly evolves, ICSDC 2025 addresses the critical need for advanced distributed and intelligent technologies that align with global sustainable development goals.A prominent feature of ICSDC 2025 is its emphasis on Digital Twin technologies. These virtual counterparts of physical systems allow for continuous monitoring, simulation, and lifecycle management of assets. They are increasingly being adopted in sectors such as industrial automation, infrastructure development, environmental monitoring, and healthcare. By providing real-time feedback and scenario modeling, digital twins promote more informed decision-making and efficient use of energy, materials, and human resources. The conference is deeply aligned with the vision of Industry 5.0, which emphasizes human-centric design, ethical AI, and intelligent automation. ICSDC 2025 highlights innovations that prioritize sustainability, resilience, and collaboration between humans and machines. Topics such as federated learning, cyber-physical systems, and context-aware computing are featured prominently, showcasing the potential of emerging technologies to enable a more inclusive and sustainable industrial future. The book includes research on low-power computing architectures, green AI models, edge intelligence, privacy-preserving mechanisms, and secure distributed systems.The intended audience of ICSDC 2025 includes academic researchers, industry practitioners, system architects, and policy decision-makers who are working at the intersection of technology and sustainability. It serves as a strategic reference point for those seeking to design, implement, and scale digital ecosystems that are not only smart and secure but also equitable, human-centric, and environmentally sustainable.

The book presents a forward-thinking and interdisciplinary collection of research contributions that seamlessly integrate technological innovation with the principles of sustainability. The core purpose of the book is to foster dialogue and collaboration focused on building intelligent, secure, and environmentally responsible computing systems. As the digital landscape rapidly evolves, ICSDC 2025 addresses the critical need for advanced distributed and intelligent technologies that align with global sustainable development goals.A prominent feature of ICSDC 2025 is its emphasis on Digital Twin technologies. These virtual counterparts of physical systems allow for continuous monitoring, simulation, and lifecycle management of assets. They are increasingly being adopted in sectors such as industrial automation, infrastructure development, environmental monitoring, and healthcare. By providing real-time feedback and scenario modeling, digital twins promote more informed decision-making and efficient use of energy, materials, and human resources. The conference is deeply aligned with the vision of Industry 5.0, which emphasizes human-centric design, ethical AI, and intelligent automation. ICSDC 2025 highlights innovations that prioritize sustainability, resilience, and collaboration between humans and machines. Topics such as federated learning, cyber-physical systems, and context-aware computing are featured prominently, showcasing the potential of emerging technologies to enable a more inclusive and sustainable industrial future. The book includes research on low-power computing architectures, green AI models, edge intelligence, privacy-preserving mechanisms, and secure distributed systems.The intended audience of ICSDC 2025 includes academic researchers, industry practitioners, system architects, and policy decision-makers who are working at the intersection of technology and sustainability. It serves as a strategic reference point for those seeking to design, implement, and scale digital ecosystems that are not only smart and secure but also equitable, human-centric, and environmentally sustainable.

The purpose of this book is first to study MATLAB programming concepts, then the basic concepts of modeling and simulation analysis, particularly focus on digital communication simulation. The book will cover the topics practically to describe network routing simulation using MATLAB tool. It will cover the dimensions' like Wireless network and WSN simulation using MATLAB, then depict the modeling and simulation of vehicles power network in detail along with considering different case studies.Key features of the book include: Discusses different basics and advanced methodology with their fundamental concepts of exploration and exploitation in NETWORK SIMULATION.Elaborates practice questions and simulations in MATLABStudent-friendly and ConciseUseful for UG and PG level research scholarAimed at Practical approach for network simulation with more programs with step by step comments.Based on the Latest technologies, coverage of wireless simulation and WSN concepts and implementations

The objective of this book is to teach what IoT is, how it works, and how it can be successfully utilized in business. This book helps to develop and implement a powerful IoT strategy for business transformation as well as project execution. Digital change, business creation/change and upgrades in the ways and manners in which we work, live, and engage with our clients and customers, are all enveloped by the Internet of Things which is now named "Industry 5.0" or "Industrial Internet of Things." The sheer number of IoT(a billion+), demonstrates the advent of an advanced business society led by sustainable robotics and business intelligence. This book will be an indispensable asset in helping businesses to understand the new technology and thrive.

ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS Written and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library. This groundbreaking new volume: Clarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks Covers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day Contains numerous examples, offering critical solutions to engineers and scientists Presents these new applications of AI and data mining that are of prime importance to human civilization as a whole

DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud securityIs a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this areaDiscusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas

Resource optimization has always been a thrust area of research, and as the Internet of Things (IoT) is the most talked about topic of the current era of technology, it has become the need of the hour. Therefore, the idea behind this book was to simplify the journey of those who aspire to understand resource optimization in the IoT. To this end, included in this book are various real-time/offline applications and algorithms/case studies in the fields of engineering, computer science, information security, and cloud computing, along with the modern tools and various technologies used in systems, leaving the reader with a high level of understanding of various techniques and algorithms used in resource optimization.

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.

This book is a comprehensive exploration of bio-inspired optimization techniques and their potential applications in healthcare. Bio-Inspired Optimization for Medical Data Mining is a groundbreaking book that delves into the convergence of nature’s ingenious algorithms and cutting-edge healthcare technology. Through a comprehensive exploration of state-of-the-art algorithms and practical case studies, readers gain unparalleled insights into optimizing medical data processing, enabling more precise diagnosis, optimizing treatment plans, and ultimately advancing the field of healthcare. Organized into 15 chapters, readers learn about the theoretical foundation of pragmatic implementation strategies and actionable advice. In addition, it addresses current developments in molecular subtyping and how they can enhance clinical care. By bridging the gap between cutting-edge technology and critical healthcare challenges, this book is a pivotal contribution, providing a roadmap for leveraging nature-inspired algorithms. In this book, the reader will discover Cutting-edge bio-inspired algorithms designed to optimize medical data processing, providing efficient and accurate solutions for complex healthcare challenges;How bio-inspired optimization can fine-tune diagnostic accuracy, leading to better patient outcomes and improved medical decision-making;How bio-inspired optimization propels healthcare into a new era, unlocking transformative solutions for medical data analysis;Practical insights and actionable advice on implementing bio-inspired optimization techniques and equipping effective real-world medical data scenarios;Compelling case studies illustrating how bio-inspired optimization has made a significant impact in the medical field, inspiring similar success stories. Audience This book is designed for a wide-ranging audience, including medical professionals, healthcare researchers, data scientists, and technology enthusiasts.

Artificial Intelligence and Cybersecurity in Healthcare provides a crucial exploration of AI and cybersecurity within healthcare Cyber Physical Systems (CPS), offering insights into the complex technological landscape shaping modern patient care and data protection. As technology advances, healthcare has transformed, particularly through the implementation of CPS that integrate the digital and physical worlds, enhancing system efficiency and effectiveness. This increased reliance on technology raises significant security concerns. The book addresses the integration of AI and cybersecurity in healthcare CPS, detailing technological advancements, applications, and the challenges they present. AI applications in healthcare CPS include remote patient monitoring, AI chatbots for patient assistance, and biometric authentication for data security. AI not only improves patient care and clinical decision-making by analyzing extensive data and optimizing treatment plans, but also enhances CPS security by detecting and responding to cyber threats. Nonetheless, AI systems are susceptible to attacks, emphasizing the need for robust cybersecurity. Significant issues include the privacy and security of sensitive healthcare data, potential identity theft, and medical fraud from data breaches, alongside ethical concerns such as algorithmic bias. As the healthcare industry becomes increasingly digital and data-driven, integrating AI and cybersecurity measures into CPS is essential. This requires collaboration among healthcare providers, tech vendors, regulatory bodies, and cybersecurity experts to develop best practices and standards. This book aims to provide a comprehensive understanding of AI, cybersecurity, and healthcare CPS. It explores technologies like augmented reality, blockchain, and the Internet of Things, addressing associated challenges like cybersecurity threats and ethical dilemmas.

Written and edited by a team of experts in the field, this exciting new volume explores the real-world applications and methods for using Internet of Things (IoT) to make homes and buildings smart and sustainable and to continue working toward a “greener” world. Sustainable Smart Homes and Buildings with Internet of Things (IoT) is a book that explores the integration of renewable energy sources and IoT technology in the design and management of smart homes and buildings. The book covers various topics related to the subject, including energy efficiency, real-time monitoring, control and optimization of renewable energy sources, smart grid integration, energy storage systems, and microgrids. The book explains how IoT technology can be used to collect data from various sensors and devices installed in smart homes and buildings to create a real-time monitoring and control system for renewable energy sources, which can help optimize energy usage and reduce waste. It also discusses the challenges and opportunities associated with the integration of renewable energy sources in smart homes and buildings, and how these challenges can be addressed through the use of IoT technology. The book is intended for architects, engineers, building managers, energy professionals, and researchers interested in the design and management of sustainable smart homes and buildings. It provides practical insights, case studies, and examples that illustrate the benefits of using renewable energy sources and IoT technology to create energy-efficient, environmentally friendly, and comfortable living spaces.

This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. “Deep Learning Techniques for Automation and Industrial Applications” presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization. This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained. Audience The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful.

Targeted Chemotherapy with Personalized Immunotherapy: An AI Approach is an essential guide for healthcare teams, offering groundbreaking insights into novel immunotherapies and personalized treatments to improve cancer patient care and quality of life. In the last 20 years, there have been significant leaps forward in the treatment of cancer. We now have a far better understanding of how our cells interact with one another, how cancer suppresses and hides from the immune system, and how to support the body in reacting to stop the spread of cancer. Nevertheless, there is still a great deal more to learn in this field. Researchers are working to develop methods that will help pinpoint the most effective treatment for patients. Through this research, they have discovered that, for certain patients, the best results may be reached by combining precisely targeted chemotherapy with personalized immunotherapy. Instead of treating patients with medications that are detrimental to the body as a whole, researchers now aim to identify the molecules that play an essential part in the communication that takes place between cells. This study will help pave the way for the development of novel immunotherapies that will help the body in its fight against cancer. In order to accurately plan cancer treatment, participation from a number of different members of the healthcare team is essential. This book is a comprehensive guide for all members of this team, providing insights into groundbreaking new treatments to cure more patients and improve quality of life.

This book provides in-depth explanations and discussions of the latest applications of Artificial Intelligence (AI), machine learning, and the Internet of Medicine, offering readers the cutting edge on this rapidly growing technology that has the potential to transform healthcare and improve patient outcomes. Over the past five years, there have been significant advances in healthcare through the use of artificial intelligence (AI) and machine learning (ML) technologies. AI and machine learning in medical imaging has significantly improved the accuracy and speed of medical imaging analysis, accelerated the drug discovery process by identifying potential drug targets and predicting the efficacy and safety of new drugs, and enabled personalized medicine by analyzing large amounts of patient data to identify individualized treatment plans based on a patient’s genetic makeup and medical history. Internet of Medicine (IoM) refers to the integration of the Internet of Things (IoT) and connected medical devices with healthcare systems and processes to enable remote monitoring, diagnosis, and treatment of patients. IoM is a subset of the larger Internet of Things concept, which involves the connection of everyday devices and appliances to the internet for various purposes. IoM has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing efficiency. Some of the specific applications of IoM include remote patient monitoring, real-time data analysis, predictive analytics, smart hospitals, and personalized medicine.

Integrating Neurocomputing with Artificial Intelligence provides unparalleled insights into the cutting-edge convergence of neuroscience and computing, enriched with real-world case studies and expert analyses that harness the transformative potential of neurocomputing in various disciplines. Integrating Neurocomputing with Artificial Intelligence is a comprehensive volume that delves into the forefront of the neurocomputing landscape, offering a rich tapestry of insights and cutting-edge innovations. This volume unfolds as a carefully curated collection of research, showcasing multidimensional perspectives on the intersection of neuroscience and computing. Readers can expect a deep exploration of fundamental theories, methodologies, and breakthrough applications that span the spectrum of neurocomputing. Throughout the book, readers will find a wealth of case studies and real-world examples that exemplify how neurocomputing is being harnessed to address complex challenges across different disciplines. Experts and researchers in the field contribute their expertise, presenting in-depth analyses, empirical findings, and forward-looking projections. Integrating Neurocomputing with Artificial Intelligence serves as a gateway to this fascinating domain, offering a comprehensive exploration of neurocomputing’s foundations, contemporary developments, ethical considerations, and future trajectories. It embodies a collective endeavor to drive progress and unlock the potential of neurocomputing, setting the stage for a future where artificial intelligence is not merely artificial, but profoundly inspired by the elegance and efficiency of the human brain.

Empower your clinical practice to lead the future of vision science with this guide to mastering generative AI strategies that transform complex imaging data into high-precision, ethical diagnostic solutions. Generative AI is a game-changer for both artificial intelligence and healthcare, especially in the field of vision science. As ophthalmologists and other eye care professionals work toward increasing the accuracy of diagnoses and treatment outcomes to improve patient-centered service delivery, generative AI is showing emerging potential as a tool to accomplish these goals. Generative AI, particularly generative adversarial networks, shows potential as a solution for image enhancement, data management, and personalized patient care. This cross-disciplinary work addresses not only the technical challenges in building effective AI systems but also the ethical considerations of using these technologies in clinical scenarios. The book bridges the knowledge gap between AI researchers and healthcare professionals by breaking down complex AI concepts into clear, accessible language, providing practical examples of real-world applications, and guiding readers through the??ethical and regulatory challenges of integrating AI into clinical practice. It demonstrates how advanced AI can be applied effectively in ophthalmology to improve diagnostics, treatment planning, and??accessibility.

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.

This new volume looks at the electrifying world of blockchain technology and how it has been revolutionizing the Internet of Things and cyber-physical systems. Aimed primarily at business users and developers who are considering blockchain-based projects, the volume provides a comprehensive introduction to the theoretical and practical aspects of blockchain technology. It presents a selection of chapters on topics that cover new information on blockchain and bitcoin security, IoT security threats and attacks, privacy issues, fault-tolerance mechanisms, and more. Some major software packages are discussed, and it also addresses the legal issues currently affecting the field. The information presented here is relevant to current and future problems relating to blockchain technology and will provide the tools to build efficient decentralized applications.Blockchain technology and the IoT can profoundly change how the world—and businesses—work, and this book provides a window into the current world of blockchain. No longer limited to just Bitcoin, blockchain technology has spread into many sectors and into a significant number of different technologies.