B. Sundaravadivazhagan – författare

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Edge AI is the seamless and spontaneous combination of Edge or Fog computing and AI. It enables acquiring real-time insights, which, in turn, leads to the realization of real-time, people-centric, event-driven, business-critical, process-aware, and knowledge-filled software services and applications.Edge AI for Industry 5.0 and Healthcare 5.0 Applications looks at the unique contributions of Edge AI for developing solutions for Industry 5.0 and Healthcare 5.0. It explains how Industry 5.0 fine tunes the human-machine connection and leverages tiny, high-performance AI-centric processors in IoT edge devices for real-time decision-making and application processing.Focusing on Explainable AI (XAI), the book discusses:The role of XAI in Healthcare 5.0Best practices, challenges, and opportunities of applying XAI in healthcare settingHow to enhance transparency and trust of XAI in Healthcare 5.0XAI and its methods in predicting healthcare outcomesOther highlights of the book include:5G communication networks requirementsThe fusion of IoT, AI, Edge, Cloud, and blockchainTrustworthiness of blockchain technology in healthcare 5.0 and Industry 5.0The future of trust and the potential of blockchain technologyBy explaining how Edge AI can transform healthcare and industry, this book empowers researchers and professionals to envisage and implement sophisticated and smart digital solutions.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.This book:Illustrates the innovative ways fractals and graph theory can be combined, laying the groundwork for future applications across various industriesIntroduces the fundamental concepts and principles of both fractals and graph theory in detail, making it accessible to a broad audience, including those new to these topicsExplores practical applications in image processing, network optimisation, social network analysis, and more, demonstrating the real-world impact of these mathematical toolsAnalyses advanced techniques in graph theory, such as matching, domination, and colouring, with practical examples and case studiesHighlights the latest research advancements in fractal graph theory, showcasing its potential for future developments and applicationsThis book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

The concept of the metaverse signifies the forthcoming stage of development of the Internet, wherein it will facilitate the creation of virtual worlds that are enduring, decentralized, and capable of providing immersive experiences in real time. The metaverse has vast potential for utilization in the domains of life sciences and healthcare, hence motivating investigations in contemporary trends, early adoption use cases, and the forthcoming opportunities it presents. The metaverse also possesses the capacity to fundamentally transform decentralized clinical trials through the elimination of physical and geographical constraints. This change in thinking entails the relocation of clinical trials from conventional settings to the comfort and convenience of patients’ residences, resulting in improvements in health behavior, medication adherence, remote monitoring, and other associated factors.Applying Metaverse Technologies to Human-Computer Interaction for Healthcare focuses on the current developments in the metaverse, investigates its applications in the life sciences and healthcare industry based on metaverse powered human−computer interactions (HCI), analyzes early adoption use cases that provide measurable commercial benefits, and anticipates prospects in this rapidly evolving domain.The book examines the treatment, management, and prevention of illnesses with the use of immersive therapeutics that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). It examines applications in cognitive therapy, support groups, psychiatric examinations, rehabilitation, and even physical therapy The book covers how healthcare practitioners have the capability to provide such services as diagnosis, treatment, monitoring, and care in remote settings, through the utilization of AR headsets and wearable devices. It concludes by discussing the continuous development of technology to facilitate the growth and maturation of the metaverse, hence enabling substantial business benefits for the life sciences and healthcare industries.

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on artificial intelligence/machine learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.This book is suitable for researchers, scholars, and industry professionals working in different fields related to mobile networks and intelligent systems. Additionally, it serves as a hands‑on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligence’s impact, limitations, and solutions.It examines AI’s role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AI’s fundamental capacity to envision and implement sustainable business transformations.It addresses critical challenges in AI adoption, focusing on two key concerns:AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.With its comprehensive structure, the book provides a comprehensive examination of AI’s potential, its current limitations, and pathways to overcome these challenges for wider adoption.

The Conference brought together innovative academics and industrial experts in the field of Medical, Biological and Pharmaceutical Sciences to a common forum. The primary goal of the conference was to promote research and developmental activities in Medical, Biological and Pharmaceutical Sciences. Another goal was to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world.

The Conference brought together innovative academics and industrial experts in the field of Medical, Biological and Pharmaceutical Sciences to a common forum. The primary goal of the conference was to promote research and developmental activities in Medical, Biological and Pharmaceutical Sciences. Another goal was to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world.

Generative Adversarial Networks (GANs) play a crucial dual role in cybersecurity, serving both as powerful defensive tools and sophisticated attack vectors that security professionals must understand and counter. GANs are invaluable for generating synthetic datasets to train cybersecurity models when real attack data is scarce or sensitive, creating realistic network traffic patterns for testing intrusion detection systems, and augmenting threat intelligence by simulating various attack scenarios without exposing actual vulnerabilities.Exploring the application of GAN models in intrusion detection, anomaly detection, and cybercrime, Generative Adversarial Networks for Cybersecurity: Protecting Data and Networks covers how GANs can be applied to pinpoint security holes, vulnerabilities, viruses, malware, phishing attacks, and other security risks. It explains how advanced GANs integrated with such digital technologies as the Internet of Things (IoT), cloud-native computing, edge analytics, serverless technology, and blockchain to protect and secure data and information from security breaches. The book also discusses how GANs can identify outliers, performance bottlenecks, and other issues in cloud infrastructure modules, applications, and data. Other topics featured in the book include:GAN-based security’s ethical and privacy concernsGANs and explainable artificial intelligence (AI)Building trustworthy sixth-generation (6G) networks with Generative Adversarial Learning (GAL)Intrusion detection systems enhanced by GANsGANs are a valuable tool for enhancing cybersecurity efforts by generating synthetic data and images that can show unusual patterns in data. This book helps researchers, academics, and professionals realize the potential of this powerful tool by presenting the latest innovations and applications of GANs in cybersecurity.

The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potentialKey use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industrySmart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communicationEvent-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twinsClearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.

This book explores the essential role of quantum computing and artificial intelligence in advancing healthcare. This comprehensive guide explores the practical applications and real-world use cases, exploring their transformative potential across various sectors. It covers nanodiagnostic tools known for accuracy, along with advanced imaging techniques. Through real-world examples, it offers valuable insights on nanomaterials to improve medical solutions.  This book is divided into three sections. The first section examines the fundamentals of quantum computing and its practical applications. The second section explores how quantum computing offers a myriad of opportunities to various industries, transitions between classical and quantum networks, and post-quantum cryptography. The third section further explores the exciting potential of quantum machine learning for Industry 4.0, as well as the applications of quantum computing and AI applications in the emerging Industry 5.0 landscape.

Comprehensive resource on graph representation learning (GRL), exploring fundamental principles, advanced methodologies, and case studies A Complete Guide to Graph Representation Learning with Case Studies provides a concise understanding of the subject of graph representation learning (GRL), a rapidly advancing field in the domain of machine learning. The book explores basic concepts to state-of-the-art techniques, enabling readers to progress from a fundamental understanding of the approach to mastering its application. The authors also cover the topics of graph embedding methods, graph neural network (GNN) -based approaches, and the latest trends in GRL such as deep learning, transfer learning, graph pooling, alignment, and matching, and graph machine learning. The book includes examples of applications of graph learning methods with real-world case studies in which the covered methods can be utilized. It also includes innovative solutions to graph machine learning problems such as node classification, link prediction, and unsupervised learning, and discusses neighborhood overlap visualization techniques and overlapping neighborhoods in heterogeneous graphs. Finally, the book provides an overview of open and ongoing research directions and student projects, providing a glimpse into potential avenues for future work. The book also includes information on: Node-level features such as node degree, node centrality, closeness, betweenness, eigenvector, page rank centrality, clustering coefficient, closed triangles, egograph, and motifsNeighborhood sampling techniques such as breadth-first sampling, depth-first sampling, snowball sampling, random walk, shallow walk, edge sampling, link-based sampling, and metapath-based samplingDeep learning models including Graph Autoencoder (GAE), Variational Graph Encoder (VGAE), and Graph Attention Network (GAN)Graph alignment and matching, covering subgraph matching and embedding for matchingA Complete Guide to Graph Representation Learning with Case Studies is a thorough and up-to-date reference on the subject for engineers and researchers in data science and machine learning as well as graduate students in related programs of study.

Stay ahead of the technological curve with this essential book, which provides a comprehensive guide to the transformative convergence of Virtual Reality (VR), Augmented Reality (AR), and 6G communication. Virtual Reality and Augmented Reality with 6G Communication delves into the transformative landscape where cutting-edge technologies meet. This book explores the convergence of Virtual Reality (VR) and Augmented Reality (AR) with groundbreaking 6G technology, providing a comprehensive examination of use cases, applications, and the challenges associated with this synergy. As we stand on the precipice of a technological renaissance, this book serves as a comprehensive guide, navigating the uncharted territories where immersive experiences meet cutting-edge connectivity. This book comprehensively covers the basics of these innovative technologies by diving into the foundational realms of VR, AR, and 6G technology. Through in-depth explanations, this essential reference provides a profound understanding of the intricate mechanisms that power these transformative domains, setting the stage for the unparalleled convergence that awaits.

A detailed guide to building cutting-edge recommendation systems In Next-Generation Recommendation Systems: A Comprehensive Guide to Enabling Technologies and Tools and their Business Benefits, a team of experienced technologists and educators, each with a proven track record in the field, delivers an expert guide to building robust recommendation systems that can interface with complex databases. The authors’ deep understanding of the subject matter is evident as they explain how to use the latest AI technologies, including LLMs, graph neural networks, diffusion models, and generative adversarial networks, to create recommendation engines that users enjoy and that drive business revenue. The book does not just delve into theoretical concepts, but also connects them to advanced implementation techniques. It demonstrates the application of practical and adaptable techniques, such as graph embeddings and Bayesian networks, to solve real-world problems faced by platform users and businesses. Readers will find the knowledge and tools to tackle these challenges head-on. Comprehensive coverage of practical generative AI techniques, including large language models and diffusion modelsDetailed exploration of graph neural networks and knowledge graph embeddings to solve common recommendation engine problemsPractical guidance on implementing generative adversarial networks and variational autoencoders to address mode collapse and information bottleneck challengesIn-depth analysis of hybrid recommendation architectures that combine content-based, collaborative, and knowledge-based filteringReal-world deployment strategies using cloud-native computing environments are not just theoretical concepts in this book. They are actionable strategies that have been tested and proven effective. This emphasis on real-world applicability will reassure readers about the book’s relevance to their professional or academic pursuits. Perfect for data scientists, AI specialists, software engineers, architects, and graduate students, Next-Generation Recommendation Systems is an essential, up-to-date resource for everyone involved in the design, deployment, and optimization of recommendation systems that connect to large, complex datasets.

Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.Some unique features of this book include:Detailed overview of various security analytics techniques and toolsComprehensive descriptions of the emerging and evolving aspects of artificial intelligence (AI) technologiesIndustry case studies for practical comprehension and applicationThis book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.

Demonstrating the benefits of the real-time capture of data using AI, this new volume discusses how AI services and applications are indispensable for setting up and sustaining real-time enterprises, using historical as well as runtime data to extract actionable insights in the needed time, leading to insights. Chapters showcase the applications of real-time artificial intelligence in diverse sectors, such as using deep learning for plant disease detection, using AI for IT entrepreneurship, in healthcare for breast cancer and eclampsia screening and monitoring, for fraud detection in online transactions, for traffic sign detection and classification in autonomous vehicles, and more.

This book is primed to demystify the emerging and evolving trend of embodied systems. These systems, such as robots, drones, etc., are physical entities that are embedded and empowered with software systems.