Artificial Intelligence subject guide 

Artificial Intelligence for Autonomous Vehicles and Driver Assistance Systems provides a comprehensive overview of how artificial intelligence (AI) technologies are applied to modern transportation systems, particularly autonomous driving and advanced driver assistance systems (ADAS). The book explores key AI concepts such as machine learning, computer vision, sensor fusion, and decision-making algorithms, explaining how they enable vehicles to perceive their environment, interpret data, and operate safely. It also examines the design, development, and integration of intelligent systems within vehicles, alongside challenges related to safety, reliability, and real-world deployment. By combining theoretical foundations with practical applications, the text highlights the critical role of AI in advancing automation, improving road safety, and shaping the future of mobility.

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5G Narrowband Internet of Things: With Satellite Connectivity and Artificial Intelligence examines the integration of advanced communication technologies to enable efficient, large-scale connectivity for IoT systems. The book focuses on Narrowband IoT (NB-IoT) within 5G networks, explaining its architecture, design principles, and advantages for low-power, wide-area applications. It also explores the role of satellite communication in extending connectivity to remote and underserved regions, alongside the application of artificial intelligence to enhance data processing, network optimization, and decision-making. By combining these technologies, the text highlights how next-generation IoT systems can support diverse applications such as smart cities, agriculture, and industrial automation, offering a forward-looking perspective on scalable, intelligent, and globally connected digital ecosystems.

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Artificial Intelligence Empowered Smart Energy Systems explores how artificial intelligence (AI) technologies are transforming modern energy systems into more efficient, adaptive, and sustainable networks. The book examines the integration of AI with smart grids, renewable energy systems, and energy management platforms, highlighting how techniques such as machine learning, data analytics, and optimization algorithms can improve forecasting, demand response, and system reliability. It also addresses key applications including energy distribution, storage management, and real-time monitoring, alongside challenges related to data, security, and system integration. By combining technological insights with practical use cases, the text demonstrates how AI-driven solutions can enhance energy efficiency, support the integration of renewables, and accelerate the transition toward intelligent and low-carbon energy systems.

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Human-Centered Automation explores the design and implementation of automated systems that prioritize human needs, capabilities, and limitations. The book examines how humans and automation can effectively collaborate, focusing on improving usability, safety, and decision-making in complex systems. It discusses key principles such as user-centered design, cognitive ergonomics, and human–machine interaction, alongside challenges like trust, over-reliance, and system transparency. By integrating technical innovation with human factors, the text highlights strategies for creating automation that enhances human performance rather than replacing it, offering a balanced approach to developing more efficient, reliable, and user-focused technologies.

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Artificial Intelligence and Ethics: A Field Guide for Stakeholders examines the ethical challenges and societal implications arising from the rapid development and deployment of artificial intelligence technologies. The book explores key issues such as bias, privacy, accountability, transparency, and the impact of AI on employment and decision-making processes. It provides practical guidance for a wide range of stakeholders—including policymakers, developers, and organizations—on how to design, implement, and govern AI systems responsibly. By combining ethical theory with real-world case studies and frameworks, the text highlights the importance of aligning AI innovation with human values, promoting fairness, and ensuring that technological advancements contribute positively to society.

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Artificial Intelligence All-in-One For Dummies provides an accessible and comprehensive introduction to the core concepts, technologies, and applications of artificial intelligence. The book covers key topics such as machine learning, data science, natural language processing, robotics, and neural networks, explaining them in a clear and practical way for beginners. It also explores how AI is used across various industries, alongside guidance on tools, development approaches, and real-world implementation. By combining foundational knowledge with practical examples and step-by-step explanations, the text serves as a user-friendly guide for understanding and applying AI in both professional and everyday contexts.

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AI for Communication explores how artificial intelligence is transforming the way individuals and organizations communicate in digital environments. The book examines the use of AI tools such as natural language processing, chatbots, and automated content generation to enhance messaging, customer engagement, and information sharing. It also considers the impact of AI on media, marketing, and interpersonal communication, highlighting both opportunities and challenges, including issues of authenticity, bias, and ethical use. By combining theoretical insights with practical applications, the text provides a clear understanding of how AI-driven technologies are reshaping communication practices in an increasingly data-driven and connected world.

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Practical Deep Learning, 2nd Edition: A Python-Based Introduction provides a hands-on introduction to deep learning, focusing on practical implementation using Python. The book explains core concepts such as neural networks, data pre-processing, model training, and evaluation, while guiding readers through building real-world applications. It emphasizes accessible, step-by-step examples to bridge the gap between theory and practice, making complex topics easier to understand for beginners and practitioners. By combining foundational knowledge with applied techniques, the text equips readers with the skills needed to develop and deploy deep learning models across a range of tasks, including image recognition, natural language processing, and predictive analytics.

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Real-World Applications of Artificial Intelligence and Machine Learning in Power Systems: A Code Approach focuses on the practical use of AI and machine learning techniques to improve the performance, reliability, and efficiency of modern power systems. The book combines theoretical concepts with hands-on coding examples, demonstrating how algorithms can be applied to real-world challenges such as load forecasting, fault detection, energy management, and grid optimization. It explores data-driven approaches and computational methods that support smarter decision-making in power generation, transmission, and distribution. By bridging the gap between AI theory and engineering practice, the text provides a valuable resource for understanding how intelligent technologies can enhance the operation and sustainability of energy systems.

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Artificial Intelligence for Power Electronics explores the integration of AI techniques into power electronics systems to improve their performance, efficiency, and adaptability. The book examines how methods such as machine learning, neural networks, and intelligent control algorithms can be applied to the design, monitoring, and optimization of power converters and electronic devices. It covers applications including fault detection, predictive maintenance, energy management, and system control, highlighting how AI can enhance reliability and enable smarter, more responsive power systems. By combining theoretical foundations with practical use cases, the text demonstrates the growing importance of AI-driven solutions in modern power electronics and energy systems.

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Covers research in the field of artificial intelligence (AI). Publication of both experimental and theoretical research, as well as surveys and impact studies.

View Computational Intelligence (1998-present, one year delay)

Addresses concerns in applied research & applications of artificial intelligence.

View Applied Artificial Intelligence (1996-present)

Includes research reports and critical evaluations of applications, techniques and algorithms in artificial intelligence, cognitive science and related disciplines.

View the Artificial Intelligence Review (2008-present)

Focuses on the creation of intelligent information systems, including reasoning processes, and their application in database management processes.

View Journal of Intelligent Information Systems (2007-present, one year delay)

Aims to advance scientific research in artificial intelligence.

View Journal of Experimental & Theoretical Artificial Intelligence (1998-present, one year delay)

Artificial Intelligence for Engineering (Gold open access journal)

IET Intelligent Transport Systems (fully open access journal)

IET AI technical webinars

IET campaigns: AI

IET Digital Futures policy