Shubham Mahajan - Böcker

Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applicationsCombines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologiesProvides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book

Innovations in Biomedical Engineering: Trends in Scientific Advances and Application addresses the burgeoning demand for a comprehensive resource that not only showcases the latest advancements in this dynamic field but also shows how these innovations can be effectively translated into real-world applications. In essence, the book acts as a bridge, connecting discoveries, research, and innovations in biomedical engineering to tangible, real-world applications.Provides a comprehensive overview of the most recent advancements in biomedical engineeringIncludes real-world case studies that offer insights into the practical application of these innovationsPresents in-depth discussions on ethical and regulatory considerations that are guiding biomedical engineeringDiscusses the key theme of collaboration between engineers and clinicians

Reliability Analysis and Modeling for Complex Systems is a crucial resource for engineers and technologists grappling with modern challenges. As technology advances and safety concerns mount, the complexity of systems like autonomous vehicles and critical infrastructure demands innovative reliability assessment methods. This book bridges theory and practice, offering practical solutions for professionals navigating the intricate world of reliability engineering. Through real-world case studies and interdisciplinary insights, it equips readers to address the multifaceted challenges of ensuring dependability in today's interconnected technological landscape.Dives deeply into advanced probabilistic modeling and analysis techniques tailored for complex systems. This content addresses the needs of reliability engineers and researchers who seek to apply cutting-edge methods to their projectsIncludes interdisciplinary perspectives, such as human factors and cyber-physical systems, which is essential for addressing the real-world challenges the target audience faces. This approach will help readers tackle complex system reliability from multiple anglesReal-world case studies that bridge theory and practice, helping practitioners and academics understand how to apply reliability analysis to complex systems in various domains

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. The book introduces federated learning, emphasizing its advantages over traditional centralized machine learning in healthcare. It provides a historical context and discusses the technological advancements that led to the emergence of metaverse healthcare. Privacy-preserving methods crucial for protecting sensitive healthcare data within federated learning environments are also examined, underscoring the importance of secure communication protocols. Other important points include the transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences.The role of telemedicine in facilitating remote diagnostics and consultations via virtual platforms, and the applications of augmented reality wearables for real-time health monitoring and wellness tracking are detailed. Additionally, the book discusses federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, its role in predictive modeling for disease risks and prevention, as well as virtual health coaches offering personalized guidance for wellness management. The challenges and ethical dilemmas of metaverse healthcare and federated learning, along with potential solutions, are also considered.Explains privacy-preserving techniques in federated learning, such as federated averaging, differential privacy, and secure aggregation, thus ensuring the protection of sensitive healthcare dataPresents use cases and case studies that demonstrate the practical applications of federated learning in virtual healthcare settingsIllustrates its impact on patient care, medical research, and healthcare innovationContains contributions from leading experts in the fields of healthcare, artificial intelligence, and virtual reality, providing valuable insights and perspectives on the intersection of federated learning and metaverse healthcare

Smart Cities of AI Robots and Autonomous Vehicles: The Vision for 2030 explores the transformative potential and challenges of integrating AI-powered robots and autonomous vehicles in urban landscapes, envisioning a futuristic smart city landscape by 2030. It examines how artificial intelligence (AI) robots and self-driving cars will transform daily life, healthcare, education, industry, agriculture, and transportation.Provides a thorough examination of the current state of AI technologies, robotics, and their applications within urban settingsDiscusses the challenges and opportunities associated with the widespread adoption of autonomous vehicles in urban transportation systems, exploring topics such as safety, regulatory frameworks, infrastructure requirements, and societal implicationsIncludes case studies and examples from existing smart city initiatives around the world

Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction explores the transformative potential of intelligent systems in enhancing human well-being through advancements in neurocognitive technologies, robotics, and AI-driven healthcare. This book addresses the fragmented knowledge at the intersection of these fields, enabling readers to understand the latest advancements and discover practical strategies for implementation. It highlights the importance of ethical considerations and governance in deploying AI and robotics in healthcare settings. Each chapter covers various aspects, including personalized medicine, human-robot interaction, cognitive robotics, wearable technologies, and the role of AI in mental health. The book also discusses the implications of cloud computing, big data, and human-computer interaction in enhancing cognitive and emotional well-being.Explores how intelligent systems enhance human well-being through neurocognitive technologies, robotics, and AI-driven healthcareProvides real-world case studies and applications demonstrating how intelligent systems are deployed in healthcare, rehabilitation, and personal well-beingAddresses ethical concerns and governance frameworks for the responsible development and deployment of AI and robotics in healthcare

LLMs in Practice: Real World Applications, Challenges and Success Stories offers a deeply applied, interdisciplinary perspective on how Large Language Models (LLMs) are being integrated into the real world-spanning industries, healthcare, education, governance, mental health, creative domains, and intelligent systems. The book presents a blend of technical insights, sector-specific applications, governance frameworks, and ethical considerations. Designed for both academic and professional audiences, it equips readers to responsibly deploy LLMs while fostering innovation, equity, and scalability. LLMs in Practice: Real World Applications, Challenges & Success Stories addresses a significant gap in current literature by offering a focused and practice-oriented examination of how Large Language Models (LLMs) are being applied across diverse real-world domains. While there is widespread academic and public interest in generative AI, there exists no single resource that cohesively captures its deployment frameworks, sector-specific applications, ethical considerations, and pedagogical integration-especially from a multidisciplinary and global perspective. This book provides deployment guidance, prompt optimization, and reliability strategies; governance frameworks, risk mitigation tools, and audit strategies; and offers case studies, instructional models, project templates, career-aligned examples, and skill-building paths.Provides a comprehensive understanding of how LLMs are transforming sectors such as healthcare, education, law, and businessServes as a reference for researchers, practitioners, and innovators seeking to design, evaluate, and scale generative AI systemsSupports educators and students by offering structured resources for teaching, learning, and project-based engagement with LLMsPromotes responsible innovation by highlighting frameworks for ethical governance, transparency, and inclusive AI adoption across varied socio-economic contexts

Metaverse and AI in Healthcare: A Federated Learning Approach addresses the transformative integration of artificial intelligence and metaverse technologies in healthcare. The book fills a critical gap by exploring how federated learning enables secure, decentralized data sharing and personalized medicine in virtual health platforms, meeting urgent demands for privacy, interoperability, and innovation. The book is structured into four parts covering foundational AI and federated learning concepts, augmented reality and metaverse applications, legal and cybersecurity challenges, and emerging strategic trends.Contributors from academia and industry present chapters on predictive modeling, cybersecurity frameworks, AR fitness, legal perspectives, and AI-driven medical tourism which are supported by case studies and technical explanations. This reference equips graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare with practical knowledge and forward-looking analysis.Explores cutting-edge AI and federated learning applications in healthcareProvides practical guidance on privacy and cybersecurity in digital healthIntegrates emerging metaverse and augmented reality technologiesPresents multidisciplinary perspectives with diverse expert contributorsIncludes real-world case studies and future trends in healthcare innovation

Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational useProvides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagementAddresses ethical, legal, and interoperability challenges unique to healthcare NLP applicationsExplores cutting-edge technologies, including large language models and federated learning in real-world medical contextsEquips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection.Features:Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computingCovers deep learning algorithms in computer visionShowcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processingHighlight optimization problems such as image segmentation and minimized feature design vectorPresents platform and simulation tools for image processing and segmentationThe book aims to get the readers familiar with the fundamentals of computational intelligence as well as the recent advancements in related technologies like smart applications of digital images, and other enabling technologies from the context of image processing and computer vision. It further covers important topics such as image watermarking, steganography, morphological processing, and optimized image segmentation. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including electrical engineering, electronics, communications engineering, and computer engineering.

The book comprehensively covers a wide range of evolutionary computer vision methods and applications, feature selection and extraction for training and classification, and metaheuristic algorithms in image processing. It further discusses optimized image segmentation, its analysis, pattern recognition, and object detection.Features:Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computingCovers deep learning algorithms in computer visionShowcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processingHighlight optimization problems such as image segmentation and minimized feature design vectorPresents platform and simulation tools for image processing and segmentationThe book aims to get the readers familiar with the fundamentals of computational intelligence as well as the recent advancements in related technologies like smart applications of digital images, and other enabling technologies from the context of image processing and computer vision. It further covers important topics such as image watermarking, steganography, morphological processing, and optimized image segmentation. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in fields including electrical engineering, electronics, communications engineering, and computer engineering.

Unlock the transformative potential of deep learning in your professional and academic endeavors with Deep Learning in Engineering, Energy and Finance: Principals and Applications. This comprehensive guide seamlessly bridges the gap between theoretical concepts and practical implementations, providing you with the knowledge and tools to revolutionize industries and drive innovation. Delve into real-world applications and cutting-edge research that showcase how deep learning is redefining engineering processes, optimizing energy systems, and reshaping financial markets.This book:Explores deep learning applications across engineering, energy, and finance, highlighting diverse use cases and industry-specific challenges.Discovers how deep learning is driving breakthroughs in predictive maintenance, energy optimization, algorithmic trading, and risk management.Illustrates all the concepts connected to Deep Learning from head and heart with real-time practical examples and case studies.Stresses on skills needed to tackle future challenges, with a focus on emerging deep learning technologies oriented towards Solar Energy, SOM’s, Stock Market, Speech Technology and Many more.Whether you're a student eager to explore the latest advancements or a seasoned R&D professional seeking to enhance your skill set, this book offers invaluable insights and practical guidance to elevate your expertise.

This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.

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.

This book demystifies the concept of AI-driven creativity with an in-depth understanding of the underlying principles, algorithms, and applications of generative AI. It begins with a coverage of AI fundamentals, followed by a technical discussion including detailed explanations of model architectures, training processes, and optimisation techniques. It explores case studies showcasing how generative AI is reshaping industries such as entertainment, marketing, design, and healthcare. The book also tackles ethical considerations, addressing issues related to bias, transparency, and responsible AI development.Features:Provides deep understanding of generative AI, including in-depth coverage of various generative models such as GANs, VAEs, and RNNs.Includes practical, real-world examples of how generative AI is applied in different industries.Dedicates significant attention to ethical considerations.Equips readers with the knowledge and tools to build, train, and deploy their own generative AI systems.Offers thorough explanations of AI algorithms, neural network architectures, and training processes.This book is aimed at graduate students and researchers in computer engineering, AI and ML.

This book is an essential resource for anyone looking to understand the cutting-edge applications and evolving technologies of Unmanned Aerial Systems, showcasing how they enhance safety and efficiency in monitoring, emergency response, and smart city development. With the evolution of Unmanned Aircraft Systems (UAS), its applications can be observed in the fields of monitoring for fire detection, sustainable computing, emergencies, and law enforcement. They can be useful for monitoring or screening applications, as well as the deployment of smart cities, security monitoring, and communication establishments at rare locations or unapproachable locations. Thus, the wireless ad-hoc networks of Unmanned Aerial Vehicles (UAVs) and infrastructure-based UAVs can be utilized in this proposal. Unmanned aircraft systems (UAS) extend human potential and allow us to execute dangerous or difficult tasks safely and efficiently, saving time, money, and, most importantly, lives. UAS can help police, fire, and other public workers save lives in emergencies like natural disasters, locate missing animals and children, or help fight fighters. Unmanned Aircraft Systems contains novel contributions and emerging trends in the area of Unmanned Aerial Vehicles (UAV), drones, and aircraft without a human pilot aboard. It has three segments incorporating technological advancements and future trends in UAS, the policies and security aspects of UAVs, and their applications as an intelligent system. Along with these state-of-the-art techniques, this book also incorporates advances in AI and machine learning, deep learning, IoT technology, cybersecurity and Blockchain, UAV regulation policies in the United States and Europe, SOTA in ITS, and many more technological advancements, which makes this book the pioneer and benchmarking reference in these areas.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;methods capable of retrieving photometric and geometric transformed images;concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;a comprehensive study of content-based image-retrieval techniques for feature extraction;machine learning approaches to understanding angiogenesis;handwritten image enhancement based on neutroscopic-fuzzy.Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.

The book offers cutting-edge insights and practical applications for Edge AI, making it essential for anyone looking to stay ahead in the rapidly evolving landscape of artificial intelligence and Edge computing. Edge of Intelligence: Exploring the Frontiers of AI at the Edge examines the transformative potential of edge AI, showcasing how artificial intelligence is being seamlessly integrated with Edge computing to revolutionize various industries. This book offers a comprehensive overview of the latest research, trends, and practical applications of Edge AI, providing readers with valuable insights into how this cutting-edge technology is enhancing efficiency, reducing latency, and enabling real-time decision-making. From optimizing vehicular networks in the era of 6G to the innovative use of AI in crop monitoring and educational technology, this book covers a broad spectrum of topics, making it an essential read for anyone interested in the future of AI and Edge computing. Featuring contributions from leading experts and researchers, Edge of Intelligence highlights real-world examples and case studies that demonstrate the practical implementation of edge AI in diverse sectors such as smart cities, recruitment, and nano-process optimization. The book also addresses critical issues related to privacy, security, and the fusion of blockchain with edge computing, providing a holistic view of the challenges and opportunities in this rapidly evolving field. Audience Engineers, data scientists, IT professionals, researchers, and academics in the fields of artificial intelligence, computer science, and telecommunications, as well as industry professionals in sectors such as the automotive, agriculture, education, and urban planning industries.

In an era where technology is rapidly advancing, Smart Systems: Engineering and Managing Information for Future Success offers a comprehensive exploration of cutting-edge technologies that are reshaping industries and societies.

NanoMind" serves as a captivating exploration at the crossroads of nanotechnology and machine learning, revealing the profound synergies between these two groundbreaking fields. At its core, the book is a guided tour through the intricate realms of nanoscale materials and artificial intelligence, illuminating their combined potential to reshape the landscape of innovation. The journey begins with an introductory chapter, setting the stage for the convergence of nanotechnology and machine learning. Readers are introduced to the fundamental principles and applications of nanoscale materials, laying the groundwork for a deeper understanding of the nano-world. Simultaneously, the basics of machine learning are demystified, providing a comprehensive overview of the methodologies and concepts that underpin AI. "Nanoscale Sensing and Imaging" takes readers into the realm of advanced technologies, showcasing how machine learning enhances our ability to sense and image materials at the nanoscale. The narrative then seamlessly transitions to the creation and optimization of materials with nano-enhancements in "Nano-Enabled Materials." This section demonstrates how the marriage of nanotechnology and machine learning can lead to the development of materials with unprecedented properties, fostering innovation in diverse industries. The exploration extends into the field of healthcare with "Machine Learning in Nanomedicine," unveiling how AI at the nanoscale is transforming diagnostics, drug delivery, and personalized medicine. "Quantum Computing and Nanotechnology" delves into the cutting-edge intersection of quantum computing, nanotech, and machine learning, showcasing the potential for revolutionary advancements in computation and problem-solving. However, the journey isn't without its challenges. The book devotes a section to "Challenges and Ethical Considerations," addressing the complexities and ethical dimensions associated with the convergence of nanotechnology and machine learning. This discussion ensures that readers are equipped to navigate the ethical landscape responsibly.