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Full Description
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.
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
