Pronaya Bhattacharya – författare

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields. The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets.Provides a comprehensive reference for signal processing techniques in modern measurement systemsHighlights the latest innovations and future directions that drive transformative capabilitiesOffers a roadmap for signal processing advances across application domains like 6G networks, pervasive health monitoring, and industry 4.0Discusses emerging trends in areas like photonic signal processing, virtual/augmented reality, additive manufacturing, and autonomous robotsBrings critical analysis of signal processing and uncertainty modeling for enabling next-generation smart measurement systems

This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning.The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.

This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to create opportunities for healthcare communities to build effective FL solutions around the presented themes, and to support work in related areas that will benefit from reading the book. It also intends to present breakthroughs and foster innovation in FL-based research, specifically in the IoMT domain. The emphasis of this book is on understanding the contributions of IoMT to healthcare analytics, and its aim is to provide insights including evolution, research directions, challenges, and the way to empower healthcare services through federated learning.The book also intends to cover the ethical and social issues around the recent advancements in the field of decentralized Artificial Intelligence. The book is mainly intended for undergraduates, post-graduates, researchers, and healthcare professionals who wish to learn FL-based solutions right from scratch, and build practical FL solutions in different IoMT verticals.

With research perspectives on elements of psychology and technology, Emotional Intelligence in the Digital Era: Concepts, Frameworks, and Applications provides insight into the intersection of emotional intelligence and digital technologies, including artificial intelligence (AI). It explains how technology has the potential to enhance emotional intelligence and promote positive psychological outcomes. It also discusses the role of technology in facilitating social connections that support mental health and promote self-reflection and personal growth.The main themes explored in the book revolve around understanding human psychology in the context of digital technology in the post-pandemic environment. The book delves into how emotional intelligence plays a crucial role in adapting to virtual environments. It also examines how AI and other digital technologies can enhance engagement and effectiveness. Emphasizing the need for a balanced approach to integrating technology in the field of psychology, the book highlights the potential benefits of technology in promoting emotional intelligence and positive psychological outcomes while also acknowledging the potential risks and ethical considerations associated with its use.Covering such aspects of human psychology as cognitive and emotional processing, personality, motivation, and decision-making, the book discusses the role of technology in improving human psychological perspectives and emotional intelligence, especially within a post-pandemic context. It also examines the use of virtual reality, teletherapy, and other digital tools in mental health interventions. Written for researchers, academics, and mental health professionals, the book is a deep dive into research investigating human psychology, emotional intelligence, and technology.

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.This book:Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computationExamines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real lifeServes as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of ThingsCovers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cyberneticsIllustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industriesThe text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

This book discusses the transformative potential of quantum computing in reshaping the landscape of supply chain management. It bridges the gap between these two dynamic fields, offering a comprehensive guide to the application of quantum principles in supply chain operations. Through detailed examples and case studies, it highlights how quantum computing can tackle industry-specific issues, such as managing global supply chain disruptions, enhancing production schedules, and enabling real-time decision-making. This book is for researchers, professionals, and technologists interested in quantum computing and supply chain practices.Features:Provides an in-depth analysis of quantum computing technologies and their capacity to solve complex optimisation problems at scales unimaginable with traditional computingExamines the impact of quantum computing on manufacturing and logistics, with a focus on sectors such as automotive and aerospaceReal-world scenarios illustrate how quantum solutions can streamline operations and drive efficiencyExplores quantum algorithms and their use in addressing challenges like route optimisation, inventory management, and demand forecasting, offering strategies to reduce costs and improve resilienceConsiders the current limitations, ethical implications, and the path to widespread adoption of quantum computing in supply chains, emphasising the need for interdisciplinary collaboration

This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis.This book:Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computationExamines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real lifeServes as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of ThingsCovers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cyberneticsIllustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industriesThe text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

The food-water nexus is a highly complex and interconnected system. XR is a powerful tool for visualizing, understanding, and managing these complexities. This book explores the application of extended reality technologies—including virtual reality, augmented reality, and mixed reality—to address challenges in managing the complex interrelationships between food and water resources. This book describes new ways to support decision-making in resource management by leveraging XR technologies. The immersive and interactive nature of XR allows stakeholders to understand the consequences of their decisions better, leading to more informed and sustainable food and water management choices.Key Features:Demonstrates practical XR applications for food-water nexus management through case studies that combine cutting-edge technology with real-world sustainability challengesPresents a comprehensive framework for integrating AI with extended reality technologies for precision agriculture and water conservationExplores innovative visualization techniques that transform complex environmental data into intuitive immersive experiencesAnalyzes the potential and limitations of XR technologies for addressing digital divide challenges in global agricultural systems

Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs.The editors cover current and emerging frameworks, architectures, and solutions that address the requirements of secure big data analytics for the healthcare industry. The book also addresses the challenges of deploying security-based healthcare analytics for massive BDA (big data analytics) applications, through smart optimized network communication infrastructures, dense connectivity, and AI-driven models.Topics include big data analytics, trustworthy data sharing, security challenges and privacy preserving techniques, authentication and access control schemes, deep learning models, risk modelling, and blockchain integration. The book provides a great reference for researchers in academia, network professionals, healthcare industry professionals, and researchers working towards emerging secure BDA solutions in 5G and beyond networks.

This new volume is an exploration that weaves together the varied threads of green energy technologies, sustainable practices, and innovative strategies across multiple sectors. This book emerges at a crucial juncture in our global environmental narrative, where the urgency of sustainable solutions need to meet an unprecedented technological evolution. It serves as a comprehensive guide for those committed to the cause of advancing green energy and sustainable methodologies.Green Energies: Synergetic Approaches to Energy, Technology, and Sustainability first covers the foundational aspects of the green energy transition, delving into the complexities and opportunities that arise from shifting away from traditional energy systems. This exploration is not just about identifying challenges but also about highlighting robust solutions and strategic frameworks that can support this significant transformation. The first chapters lay a solid groundwork by discussing energy management systems and the pivotal role of emerging technologies such as solar photovoltaic cells, setting the stage for a deeper inquiry into sustainability.The technological vanguards of the green revolution are addressed as well, discussing how cutting-edge advancements such as Industry 4.0 and green AI are reshaping the landscape of energy production and consumption. Through rigorous analysis and detailed case studies, the book explores how these technologies offer both incremental improvements and radical shifts toward more efficient, sustainable energy practices.The book also details specific applications of green energy principles in various sectors that include finance, urban development, and manufacturing, presenting a tapestry of real-world scenarios and success stories that illustrate how integrated approaches can empower communities and drive policy changes.A call to action, this volume offers insights into harnessing the potential of eco-technologies and innovative thinking to make a lasting impact on our planet health and our society well-being.

In the ever-evolving landscape of digital marketing, influencer marketing has emerged as a game-changer, captivating audiences and driving brand engagement like never before.

As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.

This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.

The book "Advanced wearable sensing in clinical practice towards healthcare 5.0" provides an overview of the state of the art that caters various professionals from clinical, scientific, and engineering backgrounds, as well as the novices that are interested in wearable sensing technology. It presents a comprehensive overview of modern wearable technology and unmet challenges. The book covers research hotspots in wearable technology, including advanced materials, low-cost wearable sensing systems, bio-signal processing, multimodal data analysis, artificial intelligence, and internet-of-medical-things. The readers from various research backgrounds (e.g., biomedical engineering, artificial intelligence, material sciences, etc.) can get an in-depth understanding of current trends and future directions. It summarizes different technologies regarding the clinical needs, theoretical basis, latest technical innovations, challenges in implementation, and limitations. These details will help improve existing designs and algorithms to cater different application scenarios. The book also provides the details of the implementation of advanced wearable sensors in different clinical settings including population screening, clinical diagnosis, to remote healthcare monitoring. The targeted diseases include cardiovascular, neurological, respiratory, metabolic, and other chronic diseases. By analyzing relevant standards and regulations, the challenges for next-generation wearable sensors are discussed in the context of healthcare ecosystem from a perspective of healthcare 5.0. In summary, the book focus on technical innovations but also delves into clinical and regulatory aspects. It caters a broad spectrum of readers. Clinical researchers, healthcare professionals, and students can obtain a better grasp of current trends of wearable technology. Biomedical engineers can understand the clinical needs, the state of the art, and the challenges in implementation, to develop better wearable sensors and algorithms for signal processing and data analysis. Public health researchers and policymakers can update the regulations and clinical guidelines to enable the integration of advanced wearable sensing technologies into current healthcare ecosystems.