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Expected by: 01 October 2023
Explainable Artificial Intelligence (XAI)
Concepts, enabling tools, technologies and applications
Edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam
The world is keen to leverage multi-faceted AI techniques and tools to deploy and deliver the next generation of business and IT applications. Resource-intensive gadgets, machines, instruments, appliances, and equipment spread across a variety of environments are empowered with AI competencies. Connected products are collectively or individually enabled to be intelligent in their operations, offering and output.
AI is being touted as the next-generation technology to visualize and realize a bevy of intelligent systems, networks and environments. However, there are challenges associated with the huge adoption of AI methods. As we give full control to AI systems, we need to know how these AI models reach their decisions. Trust and transparency of AI systems are being seen as a critical challenge. Building knowledge graphs and linking them with AI systems are being recommended as a viable solution for overcoming this trust issue and the way forward to fulfil the ideals of explainable AI.
The authors focus on explainable AI concepts, tools, frameworks and techniques. To make the working of AI more transparent, they introduce knowledge graphs (KG) to support the need for trust and transparency into the functioning of AI systems. They show how these technologies can be used towards explaining data fabric solutions and how intelligent applications can be used to greater effect in finance and healthcare.
Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications is aimed primarily at industry and academic researchers, scientists, engineers, lecturers and advanced students in the fields of IT and computer science, soft computing, AI/ML/DL, data science, semantic web, knowledge engineering and IoT. It will also prove a useful resource for software, product and project managers and developers in these fields.
About the Editors
Pethuru Raj is chief architect and vice-president in the Site Reliability Engineering (SRE) division of Reliance Jio Platforms Ltd. Bangalore, India. He focuses on emerging technologies including internet of things (IoT), artificial intelligence (AI), big and fast data analytics, blockchain, digital twins, cloud-native computing, edge and fog clouds, reliability engineering, microservices architecture (MSA), and event-driven architecture (EDA). He has authored and edited 34 books. He is a member of ACM.
Utku Köse is an associate professor at Suleyman Demirel University, Turkey. His research interests include artificial intelligence, machine ethics, optimization, chaos theory, distance education and e-learning, computer education and computer science and biomedical applications. He has over 200 scientific publications including articles, proceedings and reports. He has authored and edited several books. He is a member of ACM, an IEEE senior member, and member of IEEE Systems Man and Cybernetics Society, and IEEE Young Professionals.
Usha Sakthivel is a professor and head of the Department of Computer Science and Engineering at Rajarajeswari College of Engineering, Bangalore, India. Her areas of interest include computer networks, wireless networks, cloud computing, artificial Intelligence and internet of things. She has published 4 patents, over 45 papers and several books. She has organized workshops and conferences in association with IEEE, AICTE SERB, VTU-and TEQIP. She is a member of IEEE, ISTE, IAENG and IACSIT.
Susila Nagarajan is a professor and head of the Department of Information Technology at Sri Krishna College of Engineering and Technology, Coimbatore, India. Her areas of interest include cloud computing, blockchain technology and internet of things. She has published 5 patents and over 30 papers in referred journals. She has authored several books, and organized workshops and conferences in association with IEEE, AICTE and ICMR. She is a member of IEEE, IET, ISTE, IAENG and IFERP.
Vijanth S. Asirvadam is an associate professor in the Department of Electrical and Electronics Engineering at the Universiti Teknologi PETRONAS (UTP), Malaysia. He was previously a system engineer in the industry. His research interests include computing techniques in signal, image and video processing, linear and nonlinear system identification, unconstraint optimization and model validation. He is a member of IEEE and IET. He has published over 200 articles in proceedings and journals.
Publication Year: 2023
Pages: 554
ISBN-13: 978-1-83953-695-3
Format: HBK
Editors: Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam