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Full Description
This book is a multi-disciplinary reference on how domain-aware AI models can outperform generic approaches by addressing sector-specific complexities. It offers comparative frameworks, reproducible case studies, and real-world applications of emerging AI methods.
Collectively, the book emphasizes a unifying theme: the effective deployment of AI to strengthen decision-making, enhance system reliability, and mitigate risks in domains where precision, trust, and efficiency are critical.
This edited volume brings together twenty-one chapters of original research, each exploring how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are shaping innovation across critical domains. The book highlights the application of advanced architectures—including Convolutional Neural Networks (CNNs), Quaternion Neural Networks (QCNNs), Large Language Models (LLMs), and Gradient-Boosted Decision Trees (GBDTs)—to solve complex, domain-specific challenges.
In computer vision and infrastructure safety, chapters discuss the use of CNNs and QCNNs for automated road crack detection, offering scalable approaches to improving transportation safety while reducing dependence on manual inspections. In software engineering, contributions focus on leveraging ML, DL, and LLMs to enhance software quality assurance, minimize defects, and improve resilience in high-stakes industries. Additional chapters examine ML-driven methods, particularly GBDT, to uncover non-linear drivers of equity valuation across sectors, supporting more accurate forecasts and risk-sensitive decision-making.
Academics and researchers in computer science, AI, and data science, industry professionals in transportation, software engineering, finance, and policymakers seeking to apply AI systems effectively will find this book useful.
Contents
Part 1: Introduction 1. Introduction: From Generic AI to Domain-Aware Decision Systems 2. Comparative Analysis of CNN and Quaternion CNN for Image-Based Road Crack Detection in Computer Vision 3. Self-Structuring Neural Networks for Optimized Data Analytics Using EMANN-Like Algorithms 4. Scalable Data Science Workflow in the Cloud 5. Survey of Trustworthy AI Frameworks: Interpretability, Fairness, and Robustness Across Domains Part 2: Connecting Foundations to Applied Trustworthy AI 6. AI-based Sustainability Supply Chain Experimental Observations System: A Comprehensive Analysis 7. Transforming Supply Chain Management Through AI-Driven Predictive Analytics 8. Predictive Maintenance in Critical Infrastructure Using Graph Neural Networks 9. AI for Smart Transportation: Road Safety, Traffic Flow, and Accident Prevention Part 3: Decision Intelligence for Safer, Greener Infrastructure 10. Securing Critical E-commerce Infrastructure: Unsupervised Learning and LLM-Enhanced Anomaly Detection for Web-Based Attacks 11. Advancing Software Quality with Large Language Models, Deep Learning, and Cloud Infrastructure 12. Enhancing Software Reliability Prediction with Machine Learning: Addressing Data Noise and Model Consensus 13. AI for Cyber-Physical Security in Industrial IoT Systems 14. Trustworthy LLMs for Mission-Critical Applications: Benchmarks and Gaps Part 4: Building Reliable and Secure AI Systems 15. Bridging Theory and Practice: A LangChain-Based Virtual Assistant for Corporate AI Integration 16. Uncovering Non-Linear Drivers of Equity Valuation Across Sectors 17. AI-Driven Risk Modeling and Stress Testing in Financial Services 18. Decision Intelligence in Healthcare Finance and Insurance Systems Part 5: AI-Enhanced Decision-Making in Business and Finance 19. Unleashing Human Potential: Advancing Cognitive Capabilities Through AI-Designed Neural Interfaces 20. Ethical, Legal, and Societal Challenges of AI in High-Stakes Decision Systems 21. Cross-Sector Lessons and Roadmap: Towards Trustworthy and Domain-Aware AI.
