Shubham Mahajan – författare

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

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 applications- Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies- Provides 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

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 engineering- Includes real-world case studies that offer insights into the practical application of these innovations- Presents in-depth discussions on ethical and regulatory considerations that are guiding biomedical engineering- Discusses 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

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 projects- Includes 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 angles- Real-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

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 data- Presents use cases and case studies that demonstrate the practical applications of federated learning in virtual healthcare settings- Illustrates its impact on patient care, medical research, and healthcare innovation- Contains 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

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 settings- Discusses 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 implications- Includes 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

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 healthcare- Provides real-world case studies and applications demonstrating how intelligent systems are deployed in healthcare, rehabilitation, and personal well-being- Addresses ethical concerns and governance frameworks for the responsible development and deployment of AI and robotics in healthcare

Quantum Computing and Instrumentation for Smart Agriculture provides a detailed overview of quantum technologies and their applications in agriculture, exploring its transformative potential in improving agricultural practices Organized in five parts, the first section of the book introduces quantum technologies, their historical context, and the need for innovation in agriculture. The following sections discuss specific applications, including quantum computing for data analysis, quantum and nano-enabled sensors for precision farming, and quantum imaging for crop monitoring. In addition to addressing technological advancements, the book highlights sustainable practices and the future of quantum agriculture. This book is a valuable resource for researchers, scientists, and professionals interested in the integration of quantum and nanoscience with agricultural innovation, offering insights into innovative solutions that improve productivity while minimizing environmental impact.Reviews the fundamentals of quantum agriculture and discusses the emerging challenges and opportunities in the fieldCovers quantum innovations and state-of-the-art tools and techniques for improving crop yield and sustainabilityFeatures case studies and real-world examples on the successful implementations of quantum technologies in agriculture

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. The book addresses a significant gap in current literature by offering a focused, practice-oriented examination on how 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 socioeconomic contexts

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. The book addresses a significant gap in current literature by offering a focused, practice-oriented examination on how 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 business- Serves as a reference for researchers, practitioners, and innovators seeking to design, evaluate, and scale generative AI systems- Supports educators and students by offering structured resources for teaching, learning, and project-based engagement with LLMs- Promotes responsible innovation by highlighting frameworks for ethical governance, transparency, and inclusive AI adoption across varied socioeconomic 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

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 healthcare- Provides practical guidance on privacy and cybersecurity in digital health- Integrates emerging metaverse and augmented reality technologies- Presents multidisciplinary perspectives with diverse expert contributors- Includes 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

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 use- Provides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagement- Addresses ethical, legal, and interoperability challenges unique to healthcare NLP applications- Explores cutting-edge technologies, including large language models and federated learning in real-world medical contexts- Equips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

Autonomous Intelligence: Designing the Future of Agentic Systems provides insights into how autonomous technologies-from robots to AI-driven systems- are transforming industries and daily life. It explores how these intelligent agents operate independently, making decisions and taking actions without constant human intervention. Through clear explanations and real-world examples, the book serves as a gateway for both novices and experts as it simplifies the complexities of autonomous systems. Autonomous Intelligence: Designing the Future of Agentic Systems is broken down into six parts: Foundations of AI and Autonomy; Understanding Agentic Intelligence; Technology Enablers and Architectures; Design, Safety, and Deployment; Sectoral Applications of Agentic AI; Societal, Ethical, and Future Considerations. It considers how these technologies function, as well as addressing concerns about their integration, their limitations, their potential benefits, and the possible risks. In a rapidly evolving field, the book looks at latest trends, applications, and research, and at the ethical and legal considerations, especially in areas like employment, privacy, and accountability. Ranging from academic and theoretical concepts to case studies and practical examples, Autonomous Intelligence: Designing the Future of Agentic Systems allows the reader to confidently engage with, apply, and respond to agentic intelligence in their study, work, and daily lives.Explains the concepts behind agentic intelligence and autonomous systems, particularly how they operate without constant human interventionOffers case studies from various industries to demonstrate how autonomous systems are used todayCombines insights from technology, ethics, policy, and business to offer a cross-disciplinary perspectiveDiscusses emerging innovations and the future potential of agentic intelligenceConsiders the ethical and social considerations of agentic intelligence to help the reader think critically about the broader impact of these technologies on society

Technological Fusion in Experimental Medicine and Biology examines how artificial intelligence, omics technologies, nanotechnology, and biomedical engineering are converging to reshape experimental medicine. The book presents an integrated, end-to-end translational framework connecting hypothesis development, experimentation, multimodal data integration, AI-driven analysis, and clinical translation. It covers the core enabling technologies and the models that link diverse tools into coherent biomedical workflows. Content is structured across foundational concepts, technologies, workflow integration, application domains, and real-world case studies, supported by schematic workflows, decision frameworks, and ethical and regulatory perspectives. Chapters emphasize deep learning, reproducible research, and data governance, with case studies in cancer, cardiovascular, and neurological research. The book provides researchers, educators, and lab leaders with a unified framework for interdisciplinary research, teaching, and translational innovation.Provides interdisciplinary integration through a unified translational workflow that links computational biology, medicine, and engineeringOffers practical case studies and reproducible workflows that demonstrate real-world application across domainsDelivers future-focused insights with AI, multimodal data, and digital-twin concepts to guide ongoing research and innovation

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 computing Covers deep learning algorithms in computer vision Showcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processing Highlight optimization problems such as image segmentation and minimized feature design vector Presents platform and simulation tools for image processing and segmentation

The 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 computing Covers deep learning algorithms in computer vision Showcases novel solutions such as multi-resolution analysis in imaging processing, and metaheuristic algorithms for tackling challenges associated with image processing Highlight optimization problems such as image segmentation and minimized feature design vector Presents platform and simulation tools for image processing and segmentation

The 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.

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.

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.

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.