Chander Prabha – författare

This proposed book provides deeper insights into artificial intelligence techniques and procedures available for earth sciences. This book unveils several applications of metaheuristic approaches (i.e., swarm intelligence and IoT technologies) in collaboration with AI for earth sciences. It presents the science behind smart technologies that reveal the power of artificial intelligence and IoT. These methodologies help to extract meaningful insights from earth sciences big data analytics. These advanced technologies used in earth science practices can remove geographical barriers, locally adaptive, operationally feasible, and economically affordable. The areas can be explored with the aim of digitizing the whole world. Technological advancement also impacts the financial aspect involved in managing the earth sciences. Intelligent AI applications have made significant strides in the field of earth sciences, offering novel solutions to complex challenges, driving impactful research, and revolutionizing data analysis and interpretation. This intersection of artificial intelligence and earth sciences has paved the way for an enhanced understanding of our planet and its various phenomena.

This book discusses how Cloud, Fog, and Edge computing, alongside federated learning and blockchain, are revolutionizing healthcare systems by addressing the challenges of data management, privacy, and efficiency. Targeted at healthcare technology researchers, professionals, and advanced students, it explores how these technologies enhance patient care, data security, and organizational effectiveness. The book provides a detailed overview of how Cloud, Fog, and Edge computing work in healthcare, focusing on real-time data processing and secure data sharing. It covers integrating federated learning for privacy-preserving AI models, blockchain for ensuring data integrity, and the technical and regulatory challenges of implementing these systems in healthcare settings. Real-world case studies illustrate successful applications, while practical advice helps navigate common obstacles. A must-read for anyone involved in healthcare delivery, research, or policy, this book offers invaluable insights into the future of healthcare technology. It equips healthcare professionals and technologists with the knowledge to leverage these emerging tools to improve patient outcomes, safeguard electronic health records, and streamline healthcare delivery systems.

This book discusses how Cloud, Fog, and Edge computing, alongside federated learning and blockchain, are revolutionizing healthcare systems by addressing the challenges of data management, privacy, and efficiency. Targeted at healthcare technology researchers, professionals, and advanced students, it explores how these technologies enhance patient care, data security, and organizational effectiveness. The book provides a detailed overview of how Cloud, Fog, and Edge computing work in healthcare, focusing on real-time data processing and secure data sharing. It covers integrating federated learning for privacy-preserving AI models, blockchain for ensuring data integrity, and the technical and regulatory challenges of implementing these systems in healthcare settings. Real-world case studies illustrate successful applications, while practical advice helps navigate common obstacles. A must-read for anyone involved in healthcare delivery, research, or policy, this book offers invaluable insights into the future of healthcare technology. It equips healthcare professionals and technologists with the knowledge to leverage these emerging tools to improve patient outcomes, safeguard electronic health records, and streamline healthcare delivery systems.

"Smart Forestry 5.0 for Powered AI Sustainable Environment and Climate" explores the transformative potential of integrating advanced artificial intelligence (AI) technologies into forestry practices. This book delves into how the fifth industrial revolution can reshape the way we manage forests, promoting sustainability and resilience in the face of climate change. It combines theoretical insights with practical applications, providing a comprehensive guide for researchers, practitioners, and policymakers.The book is structured around several key themes which includes Foundations of Smart Forestry, AI and Data-Driven Decision Making, Innovative Applications of AI, Ecosystem Monitoring and Restoration, Collaborative Frameworks for Sustainable Practices, Ethical Considerations in Smart Forestry, Future Trends and Innovations that highlight the intersection of smart forestry and AI, emphasizing sustainability in forest management. It discusses innovative technologies and methodologies that enable efficient monitoring, management, and restoration of forest ecosystems.

"e;Smart Forestry 5.0 for Powered AI Sustainable Environment and Climate"e; explores the transformative potential of integrating advanced artificial intelligence (AI) technologies into forestry practices. This book delves into how the fifth industrial revolution can reshape the way we manage forests, promoting sustainability and resilience in the face of climate change. It combines theoretical insights with practical applications, providing a comprehensive guide for researchers, practitioners, and policymakers.The book is structured around several key themes which includes Foundations of Smart Forestry, AI and Data-Driven Decision Making, Innovative Applications of AI, Ecosystem Monitoring and Restoration, Collaborative Frameworks for Sustainable Practices, Ethical Considerations in Smart Forestry, Future Trends and Innovations that highlight the intersection of smart forestry and AI, emphasizing sustainability in forest management. It discusses innovative technologies and methodologies that enable efficient monitoring, management, and restoration of forest ecosystems.

This book deep dives into the theoretical background of bioacoustics, signal processing techniques, feature extraction and pattern recognition algorithms, and technically advanced case studies in bioacoustics AI. By exploring the intricate characteristics of bioacoustic signals, this book offers a comprehensive understanding of the underlying principles and practical implementations. The fundamental chapters provide readers the basics by discussing the statistical and deterministic models of bioacoustic signals, including parametric and non-parametric approaches, time-frequency representations, and stochastic processes. Furthermore, this book delves into the complexities of bioacoustic signal generation and propagation, considering physiological factors, acoustic media, and signal degradation. The feature engineering methodology upon the complex and noisy sound data is understood and explored using advanced signal processing techniques, such as wavelet transforms, matching pursuit, higher-order statistics, and fractal analysis. The subsequent chapters focus on feature engineering and pattern recognition. The feature extraction methods under subject of discussion include time-domain, frequency-domain, and time-frequency features, as well as statistical and structural features. The advanced techniques related to deep learning such as convolutional neural networks and recurrent neural networks are also explored. Traditional classification techniques, including statistical pattern recognition and syntactic pattern recognition, are covered, followed by a deep dive into the application of deep learning for bioacoustic classification. The later chapters detail on the futuristic topics such as bioacoustic localization, source separation, change detection, and monitoring. The bioacoustic data collected with other sensor modalities are significant in the development of bioacoustic indices. This book leads the assessment techniques to determine the quality of ecosystem and its performance. Furthermore, the application of bioacoustic AI in man-machine interaction is examined.

This book deep dives into the theoretical background of bioacoustics, signal processing techniques, feature extraction and pattern recognition algorithms, and technically advanced case studies in bioacoustics AI. By exploring the intricate characteristics of bioacoustic signals, this book offers a comprehensive understanding of the underlying principles and practical implementations. The fundamental chapters provide readers the basics by discussing the statistical and deterministic models of bioacoustic signals, including parametric and non-parametric approaches, time-frequency representations, and stochastic processes. Furthermore, this book delves into the complexities of bioacoustic signal generation and propagation, considering physiological factors, acoustic media, and signal degradation. The feature engineering methodology upon the complex and noisy sound data is understood and explored using advanced signal processing techniques, such as wavelet transforms, matching pursuit, higher-order statistics, and fractal analysis. The subsequent chapters focus on feature engineering and pattern recognition. The feature extraction methods under subject of discussion include time-domain, frequency-domain, and time-frequency features, as well as statistical and structural features. The advanced techniques related to deep learning such as convolutional neural networks and recurrent neural networks are also explored. Traditional classification techniques, including statistical pattern recognition and syntactic pattern recognition, are covered, followed by a deep dive into the application of deep learning for bioacoustic classification. The later chapters detail on the futuristic topics such as bioacoustic localization, source separation, change detection, and monitoring. The bioacoustic data collected with other sensor modalities are significant in the development of bioacoustic indices. This book leads the assessment techniques to determine the quality of ecosystem and its performance. Furthermore, the application of bioacoustic AI in man-machine interaction is examined.

This book explores the distinct problems, trends, and future trajectories for constructing cohesive, sustainable data infrastructures that correspond with the United Nations Sustainable Development Goals (SDGs). In the contemporary digital ecosystem, the amalgamation of data across diverse platforms and environments—from cloud to edge to IoT—has become imperative for fostering creativity, sustainability, and efficiency. “Learning-Driven Data Fabric for Sustainable Cloud-to-Thing Continuum” examines the optimization of integration through sophisticated data fabrics enhanced by machine learning and AI. This book initiates its exploration by analyzing the fundamental concepts of a learning-driven data fabric that integrates cloud and IoT ecosystems, facilitating real-time decision-making and minimizing energy consumption. It offers comprehensive insights into how intelligent data management throughout the cloud-to-thing continuum may be utilized to enhance resource efficiency, facilitate smart city planning, and promote advancements in sectors such as healthcare, transportation, and agriculture. This book emphasizes how data fabrics may advance objectives related to affordable and clean energy (SDG 7), industrial innovation (SDG 9), and sustainable cities and communities (SDG 11), with sustainability as its central theme. This book illustrates how learning-driven data architectures are revolutionizing businesses and tackling global challenges through real-world case studies and upcoming trends. Subjects encompass edge computing, real-time data analytics, safe data transmission, and the reduction of carbon footprints via effective data processing. This study examines how data fabrics might alleviate the risks associated with cyberattacks and data breaches, while ensuring regulatory compliance and fostering sustainable, ethical AI operations. This book offers a detailed framework for utilizing data fabric technologies to create sustainable, safe, and intelligent cloud-to-thing ecosystems, regardless of whether you are a data scientist, IoT specialist, cloud architect, or policymaker. This book promotes data-driven decision-making throughout the infrastructure, enabling organizations to design scalable and sustainable solutions that advance global development objectives.

This book explores the distinct problems, trends, and future trajectories for constructing cohesive, sustainable data infrastructures that correspond with the United Nations Sustainable Development Goals (SDGs). In the contemporary digital ecosystem, the amalgamation of data across diverse platforms and environments—from cloud to edge to IoT—has become imperative for fostering creativity, sustainability, and efficiency. “Learning-Driven Data Fabric for Sustainable Cloud-to-Thing Continuum” examines the optimization of integration through sophisticated data fabrics enhanced by machine learning and AI. This book initiates its exploration by analyzing the fundamental concepts of a learning-driven data fabric that integrates cloud and IoT ecosystems, facilitating real-time decision-making and minimizing energy consumption. It offers comprehensive insights into how intelligent data management throughout the cloud-to-thing continuum may be utilized to enhance resource efficiency, facilitate smart city planning, and promote advancements in sectors such as healthcare, transportation, and agriculture. This book emphasizes how data fabrics may advance objectives related to affordable and clean energy (SDG 7), industrial innovation (SDG 9), and sustainable cities and communities (SDG 11), with sustainability as its central theme. This book illustrates how learning-driven data architectures are revolutionizing businesses and tackling global challenges through real-world case studies and upcoming trends. Subjects encompass edge computing, real-time data analytics, safe data transmission, and the reduction of carbon footprints via effective data processing. This study examines how data fabrics might alleviate the risks associated with cyberattacks and data breaches, while ensuring regulatory compliance and fostering sustainable, ethical AI operations. This book offers a detailed framework for utilizing data fabric technologies to create sustainable, safe, and intelligent cloud-to-thing ecosystems, regardless of whether you are a data scientist, IoT specialist, cloud architect, or policymaker. This book promotes data-driven decision-making throughout the infrastructure, enabling organizations to design scalable and sustainable solutions that advance global development objectives.

This book ‘Quantum AI-powered and HPC applications for sustainable environment and climate change’ offers an in-depth exploration of how Quantum Artificial Intelligence (AI) -powered and High-Performance Computing (HPC) based technologies can revolutionize our approach to environmental sustainability and climate change mitigation. Through a comprehensive analysis, the book delves into the urgent climate challenges of our era, showcasing the transformative potential of cutting-edge technologies to address these issues effectively.The fundamental chapters provide a foundational understanding of the pressing environmental challenges of the 21st century and the transformative potential of quantum AI-powered and HPC. These chapters explore the power of environmental data analysis, the importance of balancing data security and privacy, and the advancements in federated learning for climate and environmental science. Additionally, they introduce the principles of quantum computing and its revolutionary applications in solving complex environmental problems.The subsequent chapters provide detailed insights into specific applications of quantum AI-powered and HPC in environmental science. Topics include quantum image recognition for precise Earth observation, advancements in extreme weather forecasting, and quantum-enhanced sensing technologies for detecting environmental contaminants. These chapters also cover the optimization of natural resource management, advanced climate modeling powered by quantum computing and HPC, and the importance of explainable AI for building trust and transparency in environmental solutions.The final chapters deal with the broader implications and future directions of quantum AI-powered and HPC applications in promoting environmental sustainability. They examine strategies for improving energy efficiency in quantum computing, ensuring data privacy and security in federated environmental systems, and leveraging quantum and HPC technologies for innovative renewable energy solutions. The book concludes with insights into building a resilient future by adapting to environmental changes and promoting sustainable development through advanced technologies. The book also delves with HPC applications that are useful for environmental sustainability and climate modelling.By addressing these topics, this book aims to bridge the gap between current environmental challenges and the potential of advanced technologies to offer sustainable solutions. It is targeted at researchers, practitioners, policymakers, students, and industry professionals in the fields of environmental science, computer science, and quantum computing, as well as anyone interested in the intersection of technology and environmental sustainability.