About the Book
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.
Table of Contents:
Chapter 1: An overview of past and present progressions in XAI
Chapter 2: Demystifying explainable artificial intelligence (EAI)
Chapter 3: Illustrating the significance of explainable artificial intelligence (XAI)
Chapter 4: Inclusion of XAI in artificial intelligence and deep learning technologies
Chapter 5: Explainable artificial intelligence: tools, platforms, and new taxonomies
Chapter 6: An overview of AI platforms, frameworks, libraries, and processes
Chapter 7: Quality framework for explainable artificial intelligence (XAI) and machine learning applications
Chapter 8: Methods for explainable artificial intelligence
Chapter 9: Knowledge representation and reasoning (KRR)
Chapter 10: Knowledge visualization: AI integration with 360-degree dashboards
Chapter 11: Empowering machine learning with knowledge graphs for the semantic era
Chapter 12: Enterprise knowledge graphs using ensemble learning and data management
Chapter 13: Illustrating graph neural networks (GNNs) and the distinct applications
Chapter 14: AI applications - computer vision and natural language processing
Chapter 15: Machine learning and computer vision - beyond modeling, training, and algorithms
Chapter 16: Assistive image caption and tweet development using deep learning
Chapter 17: Explainable renegotiation for SLA in cloud-based system
Chapter 18: Explainable AI for stock price prediction in stock market
Chapter 19: Advancements of XAI in healthcare sector
Chapter 20: Adequate lung cancer prognosis system using data mining algorithms
Chapter 21: Comparison of artificial intelligence models for prognosis of breast cancer
Chapter 22: AI-powered virtual therapist: for enhanced human-machine interaction
Chapter 23: Conclusion: an insight into the recent developments and future trends in XAI
About the Author :
Pethuru Raj is the 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 previously worked at IBM global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has authored and edited 34 books. He is a member of ACM. He holds a PhD degree in Formal Language Theory and Finite Automata from Anna University, Chennai, India.
Utku Köse is an associate professor at Suleyman Demirel University, Turkey, and a visiting researcher at University of North Dakota, USA. 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 more than 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. He received his PhD degree in Computer Engineering from Selcuk University, Turkey.
Usha Sakthivel is currently working as a professor, Dean, and Head of the Department of Computer Science and Engineering at Raja Rajeswari College of Engineering, Bangalore, India. She has 25 years of experience and graduated from Manonmanium Sundaranar University, in Computer Science and Engineering in 1998. She obtained her master's degree in computer science and engineering and PhD degree from Sathyabama University in the area of mobile ad hoc networks in 2013. She has 70 publications in international and national conferences and 45 publications in national and international journals in the area of mobile ad hoc networks, IoT, and wireless security. Most of the publications are having impact factor cited in SCI, Google Scholar, Scopus (h index and i10index), Microsoft, and others. She received funding from AICTE under NCP scheme, MODROBS, TGS, VTU TEQIP, and SERB (DST). She received the best teacher award from Lions Club in 2010 and 2012. She received the best paper award in many conferences. She received the women researcher award from Elsevier in 2020. She developed the Centre of Excellence Lab in IoT with industry collaboration and organized many international and national conferences including ICICN'16, ICRTCET'18, ICACT 2020, ICETEMT 2022, FDPs, and Technical Talks. She is associated with ISTE, CSI, IEEE, IAENG, IDES, and IACSIT. She has reviewed papers in Springer journals, IGI global, IJCs, and CIIT journals. She acted as a TPC member in MIRA'14, IoT BDS'17, and IoTBDS'18 in Portugal. She chaired sessions in FCS'14, ICISC'13, ICCCT'15, ICCCT'17, and IoTBDS'18. She is local chapter Active SPOC for NPTEL, the college website coordinator, and NBA co-coordinator at college level. She is the program coordinator for CoE on Women Empowerment in collaboration with Honeywell Corporation and Capgemini. She has published four patents including one granted patent. She has authored two books and edited one book.
Susila Nagarajan is currently working as a professor and Head of the Department of Information Technology at Sri Krishna College of Engineering and Technology, Coimbatore, India. She has 21 years of experience and graduated from Periyar Maniammai College of Technology for Women, Bharathidasan University in 2001 with a bachelor's degree in computer science. She completed her master's degree in computer science and engineering from Sathyabama Institute of Science and Technology in 2005 as a 9th Rank Holder.
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 in the fields of computing and control. He holds a PhD degree on the topic of "Online and Constructive Neural Learning methods" from Queen's University Belfast, UK.
