This book comprehensively discusses the role of cloud computing in artificial intelligence‐based data‐driven systems and hybrid cloud computing for large data‐driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‐based frameworks and advanced computing techniques to deal with online/virtual systems. This book: • Covers the aspects of security, authentication, and prediction for data‐driven systems in heterogeneous environments. • Provides data‐driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‐making for real‐time problems. • Showcases deep learning‐based computer vision algorithms for enhanced pattern detection in different domains based on data‐centric approaches. • Examines the role of the Internet of Things and machine learning algorithms for data‐driven systems. • Highlights the applications of data‐driven systems and cloud computing in enhancing network performance. This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing. This book: Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems. This book: Presents data-driven intelligent heuristics for improving and advancing environmental sustainability in both eco-cities and smart cities. Discusses various efficient control systems for distributed and hybrid renewable energy sources and enhance the scope of smart energy management systems for developing even intelligent systems. Showcases how distributed energy systems improve the data-driven robots in the Internet of Medical Things. Highlights practical approaches to optimize power generation, reduce costs through efficient energy, and reduce greenhouse gas emissions to the possible minimum. Covers bio-inspired swarm models, smart data-driven sensing to combat environmental issues, and futuristic data-driven enabled schemes in blockchain-fog-cloud assisted medical eny ecosystem. The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

In today's dynamic workplace environment, ensuring the safety and well-being of employees has never been more critical. This book explores cutting-edge technologies intersecting with workplace safety to deliver effective and practical results. Artificial Intelligence in Workplace Health and Safety: Data-Driven Technologies, Tools and Techniques offers a comprehensive roadmap for professionals, researchers, and practitioners in work health and safety (WHS), revolutionizing traditional approaches through the integration of data-driven methodologies and artificial intelligence. Covering the foundations and practical applications of data-driven WHS and historical perspectives to current regulatory frameworks, it investigates the key concepts of data collection, management, and integration. Through real-world case studies and examples, readers can discover how AI technologies such as machine learning, computer vision, and natural language processing are reshaping WHS practices, mitigating risks, and optimizing safety measures. The reader will learn applications of AI and data-driven methodologies in their workplace settings to improve safety. With its practical insights, real-world examples, and progressive approach, this title ensures that readers are not just prepared for the future of WHS but empowered to shape it for better. This text is written for professionals and practitioners seeking to enhance workplace safety through innovative technologies. This extends to safety professionals, HR personnel and engineers across different sectors.

This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help. This book: Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector. Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management. Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care. Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing. This book: Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond. Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data. Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks. Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security. Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence. It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed. Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms. Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments. All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.