Balasubramaniam S – författare

This book delves into the history of biometrics, the different systems that have been developed to date, problems that have arisen from these systems, the necessity of AI-based biometrics systems, different AI techniques developed to date (including machine learning, deep learning, natural language processing, and pattern recognition), their potential uses and applications, security and privacy issues in AI-based Biometric systems, current trends in AI-based biometrics, and presents case studies of AI-based biometrics.

This book delves into the history of biometrics, the different systems that have been developed to date, problems that have arisen from these systems, the necessity of AI-based biometrics systems, different AI techniques developed to date (including machine learning, deep learning, natural language processing, and pattern recognition), their potential uses and applications, security and privacy issues in AI-based Biometric systems, current trends in AI-based biometrics, and presents case studies of AI-based biometrics.

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.

This book explores the applications and advancements of federated learning across diverse sectors, focusing on its integration with cutting- edge technologies like Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. Real-world examples and case studies illustrate federated learning’s role in healthcare, smart cities, and maritime applications while addressing critical concerns such as security. It provides insights into federated learning’s transformative potential, offering practical strategies for intelligent systems and sustainable environments. The book particularly:Focuses on the federated learning–based model optimization, addressing the significance of IoT and federated learning in the evolution of intelligent systems for various applicationsDescribes the different optimization techniques of federated learning systems from a practical point of viewHighlights economic, social, and environmental impacts of smart technologies and provides insights into IoT, 5G/ 6G communication, and computing standardsProvides analysis of the use cases of federated learning regarding the development of IoT, AI, blockchain, digital twinsOffers strategies for overcoming challenges associated with federated learning systems, including connectivity, computation, threats, privacy, and security issuesIt covers fundamental concepts, practical implementations, and trends, to serve as a reference resource for professionals and researchers in the field.

Currently, computational intelligence approaches are utilised in various science and engineering applications to analyse information, make decisions, and achieve optimisation goals. Over the past few decades, various techniques and algorithms have been created in disciplines such as genetic algorithms, artificial neural networks, evolutionary algorithms, and fuzzy algorithms. In the coming years, intelligent optimisation algorithms are anticipated to become more efficient in addressing various issues in engineering, scientific, medical, space, and artificial satellite fields, particularly in early disease diagnosis. A metaheuristic in computer science is designed to discover optimisation algorithms capable of solving intricate issues. Metaheuristics are optimisation algorithms that mimic biological behaviours of animals or birds and are utilised to discover the best solution for a certain problem. A meta-heuristic is an advanced approach used by heuristics to tackle intricate optimisation problems. A metaheuristic in mathematical programming is a method that seeks a solution to an optimisation problem. Metaheuristics utilise a heuristic function to assist in the search process. Heuristic search can be categorised as blind search or informed search. Meta-heuristic optimisation algorithms are gaining popularity in various applications due to their simplicity, independence from data trends, ability to find optimal solutions, and versatility across different fields.Recently, many nature-inspired computation algorithms have been utilised to diagnose people with different diseases. Nature-inspired methodologies are now widely utilised across several fields for tasks such as data analysis, decision-making, and optimisation. Techniques inspired by nature are categorised as either biology-based or natural phenomena-based. Bioinspired computing encompasses various topics in computer science, mathematics, and biology in recent years. Bio-inspired computer optimisation algorithms are a developing method that utilises concepts and inspiration from biological development to create new and resilient competitive strategies. Bio-inspired optimisation algorithms have gained recognition in machine learning and deep learning for solving complicated issues in science and engineering. Utilising BIAs learning methods with machine learning and deep learning shows great promise for accurately classifying medical conditions.This book explores the historical development of bio-inspired algorithms and their application in machine learning and deep learning models for disease diagnosis, including COVID-19, heart diseases, cancer, diabetes and some other diseases. It discusses the advantages of using bio-inspired algorithms in disease diagnosis and concludes with research directions and future prospects in this field.

This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.

Networked Sensing Systems is essential for anyone seeking innovative and sustainable solutions across diverse sectors. It explores the integration of cutting-edge IoT technologies and digital transformation aimed at enhancing resource efficiency and addressing climate change challenges. With today’s advancements in wireless and mobile connectivity, Internet of Things (IoT) sensor technologies, and digital innovation, sustainability principles are increasingly reinforcing one another. To transition to more resource-efficient solutions, use resources responsibly, and streamline operations, businesses must embrace digital transformation. Potential application areas include energy management, air pollution monitoring, fleet management, water management, and agriculture. Simultaneously, the expansion of IoT deployments and their integration into the contexts of 5G and emerging 6G mobile networking necessitate that the solutions themselves be green and sustainable. This includes incorporating energy- and environmentally-conscious technical solutions for communications. By offering previously unattainable solutions, networked sensing can contribute to a more sustainable society by enabling the collection of data from heterogeneous sources in unique and novel ways. Additionally, the networking-based solutions themselves must be sustainable and environmentally friendly. For example, optimizing network architecture and relocating network equipment to strategic locations can significantly reduce energy waste. These goals drive the search for improved sensing technologies, emphasizing energy-efficient mobile sensing devices. The goal of Networked Sensing Systems is to present and highlight the latest developments in sustainable networked sensing systems across a variety of contexts, all united by the aim of enhancing human well-being and combating climate change. Regardless of the area of expertise, this work seeks to offer practical solutions to the major challenges of building a sustainable smart society 5.0. This book serves as a platform to discuss networked sensing systems for a sustainable society, focusing on systems and applications based on mobile computing and wireless networks, while adopting multidisciplinary approaches that emphasize the human element in addressing these challenges.

This book provides a timely and much-needed examination of how Federated and Trustworthy Artificial Intelligence (AI) can address the real-time challenges posed by climate change on cyber-physical systems (CPS) and Internet of Things (IoT) infrastructures. With extreme weather events, energy shortages, agricultural disruptions, and urban vulnerabilities on the rise, there is an urgent demand for intelligent, distributed, and resilient solutions that can safeguard critical infrastructures while ensuring sustainability and trust. The central premise of this book is that traditional centralized AI is no longer sufficient. Centralized models face serious limitations: they consume vast energy resources, expose sensitive environmental and operational data, and often collapse under communication bottlenecks during crises. In contrast, federated learning enables distributed IoT devices and CPS nodes to collaboratively train models without sharing raw data, reducing latency, preserving privacy, and improving system adaptability. Coupled with trustworthy AI principles—explainability, fairness, robustness, and transparency—this approach becomes indispensable for climate-resilient operations. This book emphasizes real-time, high-impact applications where federated and trustworthy AI are urgently required: -Smart energy grids that must dynamically balance renewable sources under fluctuating demand and unpredictable climate patterns.-Precision agriculture where IoT-enabled drones and sensors monitor soil, water, and crops under changing rainfall and temperature conditions, ensuring food security.-Disaster management systems that coordinate distributed sensors and autonomous agents for early warning, evacuation, and recovery during floods, wildfires, or storms.-Sustainable transportation networks that adapt to heatwaves, fuel constraints, and emissions targets while ensuring public safety.-Water and waste management systems that require predictive, trustworthy decision-making to remain functional during droughts and pollution spikes.

As the field of Artificial Intelligence continues to evolve, Generative AI has emerged as a powerful tool in the realm of software development. AI models capable of generating high-quality, human-like code can significantly accelerate the software development process, assist in troubleshooting, and even automate tasks traditionally performed by human developers. Generative AI models, such as OpenAI, DeepMind, and others, are already demonstrating the ability to write functional code, generate documentation, and assist in code review. These models are not only reducing the time it takes to develop applications but are also making software development more accessible to a broader audience, including non-technical users. This book is intended to serve as a comprehensive guide for developers, technology leaders, and anyone interested in understanding how to leverage Generative AI in software development. It will cover the underlying technology behind these models, practical use cases, integration into development workflows, and ethical considerations. Additionally, it explores how the democratization of coding through AI will shape the future of software engineering. By providing insights into real-world applications and best practices for integrating generative AI into the software lifecycle, this book empowers readers to stay ahead of the technological curve in a rapidly evolving industry.