N. Gayathri – författare

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book:Converges Blockchain, Big Data and Machine learning in one volume.Connects Blockchain technologies with the data centric applications such Big data and E-Health.Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries.Covers big data analytics examples using R.Includes lllustrative examples in python for blockchain creation.

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book:Converges Blockchain, Big Data and Machine learning in one volume.Connects Blockchain technologies with the data centric applications such Big data and E-Health.Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries.Covers big data analytics examples using R.Includes lllustrative examples in python for blockchain creation.

Precision agriculture is a next-generation farming management concept that optimizes resource use, productivity, quality, profitability, and sustainability by observing and responding to crop variability. Precision agriculture employs digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), 5G communication, cybersecurity, edge computing, cloud-native principles, and blockchain to ensure crops and soil receive exactly what they need for optimal health and productivity.Artificial Intelligence for Precision Agriculture explores the latest developments in precision agriculture, detailing how AI contributes to its goals. The book discusses how precision agriculture solutions use IoT devices, data storage, AI analytics, connectivity, and cloud infrastructures to analyze factors such as soil type, terrain, weather, plant growth, and yield data. It also examines edge technologies—sensors, microchips, beacons, RFID tags, robots, drones, and actuators—that collect field data and transmit it to cloud-based AI platforms for analysis. The book shows how AI-driven insights guide actions in the field, such as crop rotation, optimal planting and harvesting times, and soil management, and help farmers apply the right amounts of water, fertilizers, and pesticides, reducing waste and environmental impact. Applications covered in the book include:Drone-based high-resolution field mappingTracking cropsCrop yield assessmentsData collection for irrigation, fertilization, and crop managementAdvanced weather monitoringEquipment managementWith chapters on AI model development, plant disease detection and remediation, sustainable farming techniques, data integration, AI-enabled data analytics, and knowledge visualization, this book is a comprehensive guide to technologies and applications in precision agriculture.

Precision agriculture is a next-generation farming management concept that optimizes resource use, productivity, quality, profitability, and sustainability by observing and responding to crop variability. Precision agriculture employs digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), 5G communication, cybersecurity, edge computing, cloud-native principles, and blockchain to ensure crops and soil receive exactly what they need for optimal health and productivity.Artificial Intelligence for Precision Agriculture explores the latest developments in precision agriculture, detailing how AI contributes to its goals. The book discusses how precision agriculture solutions use IoT devices, data storage, AI analytics, connectivity, and cloud infrastructures to analyze factors such as soil type, terrain, weather, plant growth, and yield data. It also examines edge technologies—sensors, microchips, beacons, RFID tags, robots, drones, and actuators—that collect field data and transmit it to cloud-based AI platforms for analysis. The book shows how AI-driven insights guide actions in the field, such as crop rotation, optimal planting and harvesting times, and soil management, and help farmers apply the right amounts of water, fertilizers, and pesticides, reducing waste and environmental impact. Applications covered in the book include:Drone-based high-resolution field mappingTracking cropsCrop yield assessmentsData collection for irrigation, fertilization, and crop managementAdvanced weather monitoringEquipment managementWith chapters on AI model development, plant disease detection and remediation, sustainable farming techniques, data integration, AI-enabled data analytics, and knowledge visualization, this book is a comprehensive guide to technologies and applications in precision agriculture.

The 24 chapters in this book provides a deep overview of robotics and the application of AI and IoT in robotics. It contains the exploration of AI and IoT based intelligent automation in robotics. The various algorithms and frameworks for robotics based on AI and IoT are presented, analyzed, and discussed. This book also provides insights on application of robotics in education, healthcare, defense and many other fields which utilize IoT and AI. It also introduces the idea of smart cities using robotics.

This practical guide gives valuable insights for integrating advanced technologies in healthcare, empowering researchers to effectively navigate and implement federated systems to enhance patient care. Federated Intelligent Systems for Healthcare: A Practical Guide explores the integration of federated learning and intelligent systems within the healthcare domain. This volume provides an in-depth understanding of how federated systems enhance healthcare practices, detailing their principles, technologies, challenges, and opportunities. Additionally, this book addresses secure and privacy-preserving sharing of medical data, applications of artificial intelligence and machine learning in healthcare, and ethical considerations surrounding the adoption of these advanced technologies. With a focus on practical implementation and real-world use cases, Federated Intelligent Systems for Healthcare: A Practical Guide equips healthcare professionals, researchers, and technology experts with the knowledge needed to navigate the complexities of federated intelligent systems in healthcare and harness their potential to transform patient care and medical advancements. Readers will find the book: Provides cutting-edge research from industry experts to unlock the future of healthcare with innovative insights that embrace federated intelligence and shape the future;Presents novel technologies and conceptual and visionary-based scenarios;Discusses real-world case studies and implementations that illustrate how federated intelligence is practically applied across various healthcare scenarios, from personalized diagnostics to population-level insights;Stands as a pioneer in the exploration of federated intelligent systems in healthcare.Audience Data scientists, IT, healthcare and business professionals working towards innovations in the healthcare sector. The book will be especially helpful to students and educators.

This book is essential for anyone eager to understand the groundbreaking advancements in generative AI and its transformative effects across industries, making it a valuable resource for both professional growth and creative inspiration. Generative AI: Disruptive Technologies for Innovative Applications delves into the exciting and rapidly evolving world of generative artificial intelligence and its profound impact on various industries and domains. This comprehensive volume brings together leading experts and researchers to explore the cutting-edge advancements, applications, and implications of generative AI technologies. This volume provides an in-depth exploration of generative AI, which encompasses a range of techniques such as generative adversarial networks, recurrent neural networks, and transformer models like GPT-3. It examines how these technologies enable machines to generate content, including text, images, and audio, that closely mimics human creativity and intelligence. Readers will gain valuable insights into the fundamentals of generative AI, innovative applications, ethical and social considerations, interdisciplinary insights, and future directions of this invaluable emerging technology. Generative AI: Disruptive Technologies for Innovative Applications is an indispensable resource for researchers, practitioners, and anyone interested in the transformative potential of generative AI in revolutionizing industries, unleashing creativity, and pushing the boundaries of what’s possible in artificial intelligence. Audience AI researchers, industry professionals, data scientists, machine learning experts, students, policymakers, and entrepreneurs interested in the innovative field of generative AI.

Increased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making.This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19.Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed.Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.

This book delves into the exciting and rapidly evolving world of generative artificial intelligence (AI) and its profound impact on various industries and domains. This comprehensive book brings together leading experts and researchers to explore the cutting-edge advancements, applications, and implications of generative AI technologies.It provides an in-depth exploration of generative AI, which encompasses a range of techniques such as generative adversarial networks (GANs), recurrent neural networks (RNNs), and transformer models like GPT-3. It examines how these technologies enable machines to generate content, including text, images, audio, and more, that closely mimics human creativity and intelligence.The book's multidisciplinary approach allows it to cater to a wide range of readers with varying levels of expertise and interests in generative AI.

This book introduces a novel approach that combines AI-driven analytics with IoT-enabled systems to provide actionable insights for crop management, resource optimization, and decision-making. Offering fresh perspectives on the integration of artificial intelligence with connected farming technologies, this work highlights practical strategies for boosting efficiency, sustainability, and productivity in modern agriculture. Covering a broad scope from precision farming to predictive maintenance, the book is designed for researchers, practitioners, policymakers, and advanced students. Its applications range from improving yield forecasts to reducing environmental impact, making it a valuable resource for both academic study and real-world agricultural innovation.

In recent years, the fields of Augmented Reality (AR), Virtual Reality (VR), Internet of Things (IoT), and Artificial Intelligence (AI) have witnessed significant advancements. This edited book explores the intersection of AR, VR, IoT, and AI, and their combined applications. It delves into the latest research, trends and practical implementations, providing readers with a comprehensive understanding of this rapidly evolving landscape.

The proceedings cover recent contributions and novel developments from researchers across industry and academia in data science, data engineering, artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems from technological and social perspectives.

This volume constitutes peer-reviewed proceedings of the First International Conference on Data Engineering and Machine Intelligence, ICDEMI 2023. The research problems about data engineering and machine learning are covered in this book. The proceedings cover recent contributions and novel developments from researchers across industry and academia in data science, data engineering, artificial intelligence, big data, cloud computing, sustainability, and knowledge-based expert systems from technological and social perspectives. This book will serve as a valuable reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners.