Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot

Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT demonstrates how the computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based internet of things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.

"Deep Learning Techniques for Cloud-Based Industrial IoT aims to demonstrate how computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based Internet of Things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students"--

Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings. Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers’ points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.

Recently, the internet of things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. With the outbreak of the COVID-19 pandemic, artificial intelligence (AI) has gained significant attention by utilizing its machine learning algorithms for quality patient care. The integration of IoT with AI may open new possibilities for both technologies and can play a big part in smart healthcare by providing improved insight into healthcare data and allowing for more inexpensive personalized care. The Handbook of Research on Advancements in AI and IoT Convergence Technologies considers recent advancements in AI and IoT convergence technologies with a focus on state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those convergence technologies. It also provides insight into how to develop AI and IoT convergence techniques to meet industrial demands and covers the emerging research topics that are going to define the future of AI and IoT convergence technology development. Covering key topics such as diseases, smart healthcare, social distance monitoring, and security, this major reference work is ideal for industry professionals, nurses, healthcare workers, computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.

Cloud computing adoption has revolutionized how businesses and individuals harness the power of technology. The cloud's scalability, accessibility, and cost-efficiency have propelled it to the forefront of modern computing paradigms. However, as organizations increasingly rely on cloud services to store, process, and manage their data and applications, an intricate web of challenges has emerged, casting shadows over the very foundations of cloud computing. Improving Security, Privacy, and Trust in Cloud Computing unravels the complexities surrounding the cloud landscape, delving into the core concerns of security, privacy, and trust that have come to define its evolution. It aims to equip readers with the insights, knowledge, and practical strategies needed to navigate the intricate realm of cloud computing while safeguarding their most valuable assets. This book's exploration into security, privacy, and trust in cloud computing takes a holistic approach. Throughout the chapters of this book, readers will embark on a multidimensional expedition. This book will take them through real-world case studies of successful cloud security implementations and unfortunate breaches that underscore the urgency of robust defenses. From data encryption techniques to incident response protocols, this book offers practical insights and actionable strategies that can be implemented by IT professionals, security experts, and decision-makers alike.

In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).

In the dynamic field of modern business, where cloud computing has become the primary focus of operations, a pressing issue arises – the persistent concerns of security, privacy, and trust in cloud environments. Organizations find themselves at a crossroads, caught between the immense benefits of cloud adoption and the escalating challenges of safeguarding sensitive data and maintaining user trust. The need for a comprehensive and practical guide to navigate these intricate landscapes has never been more critical. Analyzing and Mitigating Security Risks in Cloud Computing is a groundbreaking guidebook tailored to address the very challenges that organizations face in securing their cloud infrastructures. With a focus on real-world examples, case studies, and industry best practices, the book equips its readers with actionable insights and tools to fortify their cloud security posture. From understanding the fundamentals of cloud computing to addressing emerging trends and implementing robust security strategies, the book serves as a holistic solution to bridge the knowledge gap and empower professionals at every level.