Convergence of Cloud Computing, AI, and Agricultural Science explores the transformative potential of integrating cutting-edge technologies into the field of agriculture. With the rapid advancements in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), this research presents a comprehensive framework for monitoring agriculture farms remotely using a smart cloud-based system. The book delves into the application of AI-based machine learning models, such as the Support Vector Machine (SVM), to accurately classify and process the collected data. This advanced research reference book also explores how digital information can provide farmers with information about international markets, enabling them to make informed decisions regarding their crops. With its academic tone and in-depth exploration of cloud computing in smart agriculture, this book serves as an essential resource for researchers, academics, and professionals in the fields of agriculture, computer science, and environmental science. By examining the convergence of cloud computing, AI, and agricultural science, it provides a roadmap for harnessing technology to revolutionize farming practices and ensure sustainable agri-food systems in the digital era.

Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.

CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.

Today, the convergence of Artificial Intelligence (AI) and Big Data has revolutionized industries worldwide, driving business growth and reshaping societies. While these technologies have yielded remarkable benefits, many developing countries face challenges in harnessing their potential due to inadequate data collection and availability. Emerging Advancements in AI and Big Data Technologies in Business and Society delves into the profound impact of AI and Big Data on the digital landscape and their transformative influence on social, economic, and political spheres. With a historical overview of AI's evolution and its operational definition, this book explores interconnected subfields such as problem-solving, intelligent agents, natural language processing, computer vision, and machine learning. AI is hailed as the fourth industrial revolution and the widespread use of AI technologies prompts discussions about their applications, performances, and societal impact. This book serves as a comprehensive guide for academics, researchers, and students in universities and engineering schools. It also caters to policymakers, government officers, corporate leaders, technology directors, and managers seeking to understand the potential of AI and Big Data. Additionally, libraries and information centers catering to the needs of these professionals will find this book an essential resource.

With the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI), the traditional methods of farming are undergoing transformation. By harnessing the power of data-driven insights and automation, farmers can now make informed decisions in real time, optimize resource utilization, and mitigate risks associated with crop management and livestock welfare. This book serves as a guide to the integration of IoT and AI in agriculture and discusses the methodologies, applications, and challenges in this rapidly evolving field. It details various aspects of smart farming—from crop monitoring and precision agriculture to livestock management and food supply chain transparency—and provides insight into the potential of IoT and AI to revolutionize agricultural practices and address the global challenges of food security, environmental sustainability, and economic development. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan)

The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable. The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies. Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.

The effectiveness of federated learning in high‐performance information systems and informatics‐based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‐based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications. Features: • Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‐edge and wireless‐edge architecture for intelligent IoT applications. • Comprises real‐life case illustrations and examples to help consolidate understanding of topics presented in each chapter. This book is recommended for anyone interested in federated learning‐based intelligent algorithms for smart communications.

In an era defined by the relentless march of technology, the seamless integration of Artificial Intelligence (AI) into our daily lives has ushered in a transformative landscape. At the forefront of this evolution are the Digital Natives of Generation AI, navigating the complexities of a digital world where algorithms are integral to their daily experiences. This juncture presents a dual influence, marked by the continuous progression of technological advancements and the dynamic ways the youngest members of our society engage with and adapt to the digital environment. As we stand at the crossroads of youth studies and AI, there arises a pressing need to comprehend the profound impact of this convergence on the future leaders of our world. Addressing this imperative, Exploring Youth Studies in the Age of AI emerges as a comprehensive solution to unravel the complexities and opportunities within this evolving landscape. This book, meticulously crafted for academics, researchers, educators, policymakers, and technology ethicists, serves as a guiding beacon in understanding how AI shapes the experiences of today's youth and, in turn, how youth culture influences the development and application of AI technologies. With a collection of enlightening chapters covering topics from "Data-Driven Pedagogies" to "Ethical AI: Guiding Principles for Youth-Centric Development," the book delves deep into the diverse dimensions of this intersection, providing actionable insights and fostering a nuanced understanding for those invested in the ethical, social, and educational implications of AI within the context of youth.

The longstanding practice of keeping academic disciplines separate has been a barrier to effectively addressing the complex challenges in our world. The boundaries separating fields like healthcare, social sciences, and technology have obscured the potential for interdisciplinary collaboration, preventing us from unlocking innovative solutions to the most pressing issues of our time. As a result, the critical problems we face, from healthcare inequities to technological advancements with ethical dilemmas, have remained largely unresolved. This fragmented approach to academic inquiry has left a void in our quest to tackle these challenges effectively. The solution is found within the pages of Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology. This groundbreaking compendium illuminates the transformative potential of interdisciplinary collaboration, offering direction and support in the form of knowledge for scholars, researchers, practitioners, and students committed to solving real-world problems. By harnessing the collective wisdom of diverse disciplines, the book demonstrates how convergence across healthcare, social sciences, organizational behavior, and technology can lead to groundbreaking insights and solutions. It showcases success stories and innovative strategies that drive positive change within our societies, offering a roadmap towards a brighter, more interconnected future.

The persistent challenge of inequitable access to quality services plagues diverse age groups, creating a glaring gap in our pursuit of inclusive well-being. Despite the revolutionary strides in digital health and artificial intelligence (AI), the promise of universal accessibility remains unfulfilled. The disparities demand a comprehensive understanding of obstacles hindering inclusivity, setting the stage for a transformative solution. Inclusivity and Accessibility in Digital Health is a groundbreaking exploration that is a beacon of change in the healthcare narrative. This book transcends conventional boundaries, offering innovative frameworks, case studies, and empirical research. It delves into the transformative potential of AI and digital health, presenting actionable insights to tailor healthcare services, manage diseases, and elevate overall well-being. Aligned with the United Nations Sustainable Development Goals, this book inspires researchers, healthcare professionals, policymakers, and tech enthusiasts to harness the power of technology for an inclusive healthcare revolution.