Handbook Of Research On Machine Learning Enabled Iot For Smart Applications Across Industries

Machine learning (ML) and the internet of things (IoT) are the top technologies used by businesses to increase efficiency, productivity, and competitiveness in this fast-paced digital era transformation. ML is the key tool for fast processing and decision making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. IoT technology has proven efficient in solving many real-world problems, and ML algorithms combined with IoT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. The Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries highlights the importance of ML for IoT’s success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Covering topics such as agriculture, pattern recognition, and smart applications, this premier reference source is an essential resource for engineers, scientists, educators, students, researchers, and academicians.

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.

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.

The book discusses the evolution of future generation technologies through Internet of Things (IoT) in the scope of Artificial Intelligence (AI). The main focus of this volume is to bring all the related technologies in a single platform, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the AI algorithms, machine learning algorithms, and learning analytics in IoT-enabled technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects.

The rapid growth of IoT and its applications in smart cities pose significant challenges for academic scholars. The increasing number of interconnected devices and the massive amounts of data they generate strain traditional networks, leading to inefficiencies and security vulnerabilities. Additionally, the centralized control plane in Software Defined Networks (SDN) presents a single point of failure, hindering network performance, while IoT devices themselves are susceptible to attacks, compromising user data and privacy. To address these pressing issues, Network-Enabled IoT Applications for Smart City Services offers a compelling solution. Edited by Dr. K. Hemant Kumar Reddy, Dr. Diptendu SinhaRoy, and Tapas Mishra, this book advocates leveraging SDN to handle high-frequency data streams effectively. It also proposes the integration of blockchain technology to enhance security and reliability in IoT applications, offering a roadmap for scholars to improve network efficiency, security, and reliability in IoT and smart city domains. With their extensive expertise, the authors provide academic scholars with a comprehensive and innovative resource that inspires further research and development in this evolving field, enabling them to make significant contributions to the advancement of IoT and smart city technologies.

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.

In an age defined by the transformative ascent of cloud computing and the Internet of Things (IoT), our technological landscape has undergone a revolutionary evolution, enhancing convenience and connectivity in unprecedented ways. This convergence, while redefining how we interact with data and devices, has also brought to the forefront a pressing concern – the susceptibility of these systems to security breaches. As cloud services integrate further into our daily lives and the IoT saturates every aspect of our routines, the looming potential for cyberattacks and data breaches necessitates immediate and robust solutions to fortify the protection of sensitive information, ensuring the privacy and integrity of individuals, organizations, and critical infrastructure. Emerging Technologies for Securing the Cloud and IoT emerges as a comprehensive and timely solution to address the multifaceted security challenges posed by these groundbreaking technologies. Edited by Amina Ahmed Nacer from the University of Lorraine, France, and Mohammed Riyadh Abdmeziem from Ecole Nationale Supérieur d’Informatique, Algeria, this book serves as an invaluable guide for both academic scholars and industry experts. Its content delves deeply into the intricate web of security concerns, elucidating the potential ramifications of unaddressed vulnerabilities within cloud and IoT systems. With a pragmatic focus on real-world applications, the book beckons authors to explore themes like security frameworks, integration of AI and machine learning, data safeguarding, threat modeling, and more. Authored by esteemed researchers, practitioners, and luminaries, each chapter bridges the divide between theory and implementation, aiming to be an authoritative reference empowering readers to adeptly navigate the complexities of securing cloud-based IoT systems. A crucial resource for scholars, students, professionals, and policymakers striving to comprehend, confront, and surmount contemporary and future security challenges, this book stands as the quintessential guide for ushering in an era of secure technological advancement.