Gautam Srivastava – författare

Internet of Multimedia Things (IoMT): Techniques and Applications disseminates research efforts in the security and resilience of intelligent data-centric critical systems to support advanced research in this area. Sections cover the background of IoMT Architectures and Technologies, describe the problems that arise in IoMT Computing and protocols, and illustrate the application of IoMT on Industrial applications. The book will be beneficial for engineers, developers, solution designers, architects, system engineers and specialists from professional environments interested in the IoMT to seek appropriate solutions to their specific problems.

Addresses recent developments, along with relevant prospects on opportunities and challenges in IoMT Presents the concept and vision of the IoMT, whose potentialities are discussed with speci?c use-cases Discusses the distinct architectural design and characteristics of IoMT as compared to the existing multimedia systems

Artificial Intelligence and Data Science in Healthcare Applications provides a thorough in-depth examination of how AI and data science are transforming predictive analytics and outlier detection in every industry. With in-depth examinations of machine learning, neural networks, NLP, and ethics of AI, this book prepares readers with both theoretical principles and practical tools to create smart, scalable systems. Real-world healthcare, cybersecurity, and finance case studies exemplify real world applications, and tutorials with leading libraries serve as a starting point for implementation.Features:Offers a broad overview of both foundational and advanced topics in artificial intelligence and data science, focusing particularly on their applications in prediction and detection.Addresses the ethical implications and social impact of artificial intelligence and data science, discussing topics such as algorithmic bias, privacy, and the ethical use of predictive technologies.Discusses enhanced deep learning model to detect lung diseases from Chest-Xray.Highlights lung cancer prediction using variational autoencoders and early stopping for neural network clustering and optimal tuning.Evaluates mental well-being through wearable sensors utilizing machine learning.It will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, information technology, and biomedical engineering.

The book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field. This book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime. This volume aims to encourage industry professionals to initiate a set of architectural strategies to solve open research computation challenges. The authors achieve this by defining and exploring emerging trends in advanced optimization algorithms, AI techniques, and fog and Edge technologies for IoT applications. Solutions are also proposed to reduce the latency of real-time applications and improve other quality of service parameters using adaptive optimization algorithms in fog and Edge paradigms. The book provides information on the full potential of IoT-based intelligent computing paradigms for the development of suitable conceptual and technological solutions using adaptive optimization techniques when faced with challenges. Additionally, it presents in-depth discussions in emerging interdisciplinary themes and applications reflecting the advancements in optimization algorithms and their usage in computing paradigms. Audience Researchers, industrial engineers, and graduate/post-graduate students in software engineering, computer science, electronic and electrical engineering, data analysts, and security professionals working in the fields of intelligent computing paradigms and similar areas.

As AI technologies progress and influence more facets of our lives, the requirement for openness and interpretability becomes increasingly important. Explainable AI (XAI) has the potential to be a paradigm shift in the next generation of AI systems. XAI strives to make AI algorithms and methods understandable by tackling trust, bias, compliance, and accountability challenges. XAI improves model disclosure, produces intrinsically interpretable deep learning approaches, offers real-time rationales, and promotes legitimate AI practice. These advances assist in the development of a more ethically sound AI ecosystem.As the IoT evolves and supply chains become more complex, novel avenues for attack arise. The ever-changing threat landscape includes powerful adversaries such as malicious actors and hackers who are always refining their strategies, and demand ongoing monitoring and adaptive responses. Cybersecurity helps safeguard data, identify fraud, protect vital infrastructure, and ensure confidentiality. Considering the dynamic nature of the cybersecurity battlefront, a holistic approach must include pre-emptive threat intelligence, staff training, effective security tools, regular upgrades, and global collaboration. Explainable AI (XAI) explains security alerts, reduces false positives and enables faster incident response.The objective of this book is to explore how the integration of XAI-based cybersecurity algorithms and methods support threat detection and decision-making by preserving privacy and trust, ensuring interpretability and accountability, and optimizing computational and communication costs.This book will be a useful reference for computing and security researchers, scientists, and IT professionals in academia and industry, who are developing and designing innovative cyber threat and vulnerability detection systems and solutions, as well as advanced students and lecturers to better understand AI and XAI algorithms for cybersecurity applications.

Industry 5.0 is the upcoming industrial revolution where people will be working together with smart machines and robots, thereby bringing human touch and intelligence back to the decision-making process. Challenges include the security and privacy of sensitive multimedia data and near zero latency for mission critical applications.Federated learning is a machine learning technique that trains algorithms across multiple decentralized edge devices or servers by holding local data samples without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all local datasets are uploaded to one server. This method enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights and access to heterogeneous data.The objective of this book is to show how federated learning can solve multimedia data processing and security challenges in Industry 5.0. The book introduces new research paradigms for the security and privacy preservation of multimedia data. It provides a detailed discussion on how federated learning can be used to handle big data, preserve privacy, reduce computational and communication costs; and shows how to integrate federated learning with other disruptive technologies including blockchain, digital twins and 5G and beyond.Federated Learning for Multimedia Data Processing and Security in Industry 5.0 is an essential reference for advanced students, lecturers, and academic and industry researchers working in the fields of machine learning federated learning, computer and network security, data science, multimedia, computer vision and Industry 5.0 applications.

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. The IoT discussed helps to design and develop the intelligent medical and industry solutions assisted by data analytics and machine learning.

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. The IoT discussed helps to design and develop the intelligent medical and industry solutions assisted by data analytics and machine learning.

This book offers the latest research results in security and privacy for Intelligent Edge Computing Systems.

This book offers the latest research results in security and privacy for Intelligent Edge Computing Systems.

- The book consists of systematic rendering of the subject with a whole chapter on "Ill Effects of Tobacco, Areca Nut and Alcohol." It is well-illustrated with appropriate clinical pictures and provides students with a clear understanding of this science.- Contains twelve chapters which reflect basic as well as applied aspects with emphasis on current developments in oral medicine. The organization of each chapters follows a logical sequence and is supported by high quality colored diagrams and photographs to enable the students comprehend the subject fully and logically

This book provides a comprehensive exploration of how artificial intelligence (AI) is revolutionizing cyber risk management, offering advanced methodologies to identify, assess, quantify, and mitigate threats effectively. Through the integration of AI, big data analytics, and emerging technologies, this book presents cutting-edge approaches to addressing cyber risks. Readers will gain insights into how AI enhances threat detection, fraud prevention, risk quantification, and incident response, equipping businesses to anticipate, measure, and mitigate cyber disruptions with greater precision.In today’s digital landscape, businesses increasingly depend on complex cyber systems including cloud computing and data infrastructure, enterprise systems, financial and payment, and other operational systems, which make them susceptible to a wide range of risks beyond conventional security threats. Downtimes, system failures, data breaches, and cyber infrastructure outages can cause significant operational, reputational, and financial disruptions. To navigate these challenges, organizations must move beyond traditional risk management approaches and adopt AI-driven solutions for more proactive and intelligent cyber risk management.Structured around key aspects of AI-driven cyber risk management, the book begins with foundational principles before delving into the rise of AI in cyber risk management, the role of big data in risk analysis, and the application of AI in threat intelligence and fraud detection. Readers will explore AI-powered risk quantification models, automated mitigation strategies, and the integration of AI into cybersecurity infrastructure, business continuity planning, and disaster recovery. Industry-specific case studies offer real-world insights into AI’s impact across different sectors, while discussions on emerging technologies, such as IoT, blockchain, large language models, advanced machine learning, and explainable AI, highlight the future trajectory of AI in cyber risk management.Aimed at professionals, researchers, and students, this book balances technical depth with practical clarity, making it an essential guide for those seeking to bridge the gap between AI innovation and real-world cyber risk applications. By addressing both the potential and challenges of AI-driven solutions, it presents a forward-thinking strategy for building resilient, AI-powered cyber risk management frameworks.