Artificial Intelligence and Machine Learning: Emerging Trends and Innovations in Engineering, Management & Health Science

Book Overview

The growing integration of Artificial Intelligence (AI) and Machine Learning (ML) across multiple domains has transformed the global innovation landscape. This book aims to capture these transformative impacts across engineering, management, and health sciences, offering multidisciplinary insights into the theories, methods, and applications of AI and ML. The volume seeks contributions that explore conceptual frameworks, empirical research, applied case studies, and futuristic visions for AI-driven transformation.

THEMES AND SUB-THEMES

Theme 1: Theoretical Foundations and Emerging Paradigms of AI & ML

This theme explores the core algorithms, models, and computational approaches that form the backbone of AI and ML innovations. It welcomes research that strengthens understanding of the science behind intelligent systems.

Sub-themes:

• Evolution and conceptual frameworks of Artificial Intelligence and Machine Learning

• Deep learning architectures and neural computation models

• Reinforcement learning and adaptive algorithms

• Transfer learning, federated learning, and meta-learning approaches

• Explainable AI (XAI), interpretability, and trust in AI systems

• Cognitive computing and human–AI collaboration

• Ethics, transparency, and responsible AI frameworks

• AI policy design and global governance models

Theme 2: AI and Machine Learning Applications in Engineering

Engineering disciplines are increasingly driven by AI-based automation, optimization, and predictive analytics. This theme emphasizes practical innovations and technical applications across various engineering sectors.

Sub-themes:

• AI-assisted design optimization in mechanical, civil, and electrical engineering

• Intelligent control systems and process automation

• Predictive maintenance, fault diagnosis, and system reliability using ML

• Robotics, mechatronics, and autonomous systems powered by AI

• Smart materials and manufacturing: Industry 4.0 and beyond

• Internet of Things (IoT) and AI integration for smart infrastructure

• Energy management, renewable systems, and environmental modeling with AI

• Computational fluid dynamics, simulation, and digital twin technologies

• AI applications in transportation, logistics, and sustainable mobility

Theme 3: AI and Machine Learning in Management, Business, and Economics

The business world is undergoing rapid transformation due to data-driven decision-making and intelligent automation. This theme explores how AI and ML enhance competitiveness, strategic planning, and operational efficiency.

Sub-themes:

• Predictive analytics for business intelligence and strategy formulation

• AI in finance: algorithmic trading, credit scoring, and fraud detection

• Customer behavior modeling and AI-powered marketing insights

• Intelligent automation in supply chain and logistics management

• HR analytics and talent management through predictive modeling

• Risk analysis, forecasting, and performance optimization using ML

• AI ethics, governance, and policy development in corporate environments

• AI in entrepreneurship, innovation ecosystems, and sustainability management

• Digital transformation, leadership, and change management in the AI era

Theme 4: AI and Machine Learning in Health Sciences

AI and ML are redefining the boundaries of healthcare by improving diagnostics, treatment, and patient care. This theme invites research that showcases how intelligent algorithms are reshaping medical sciences.

Sub-themes:

• AI for medical image analysis, disease prediction, and early detection

• Machine learning models for epidemiology and precision medicine

• Bioinformatics, genomics, and AI-driven drug discovery

• Predictive healthcare analytics and clinical decision support systems

• AI in mental health, psychology, and neuroinformatics

• Robotics and automation in surgery, diagnostics, and rehabilitation

• AI in public health monitoring and telemedicine

• Big data integration, data governance, and ethical challenges in healthcare

• AI for hospital management systems and patient-centered digital solutions

Theme 5: Cross-Disciplinary Applications and Hybrid Innovations

This theme encourages contributions that highlight the synergy between AI, data science, and other disciplines. It focuses on hybrid models, interdisciplinary innovations, and real-world problem-solving.

Sub-themes:

• AI in education: learning analytics, personalized learning, and EdTech solutions

• AI for agriculture, food technology, and environmental sustainability

• Intelligent urban systems: smart cities, waste management, and green technologies

• AI in law, ethics, and policy formulation

• Social informatics: AI in social sciences and human behavior modeling

• Human–computer interaction, cognitive UX, and affective computing

• Quantum AI, edge AI, and next-generation computing paradigms

• AI in space research, defense technology, and disaster management

We welcome original, unpublished contributions from researchers, educators, policymakers, and practitioners across disciplines. The above-listed themes and sub-themes serve as an indicative overview of the focus areas for this volume. However, any scholarly work related to the title — “Artificial Intelligence and Machine Learning: Emerging Trends and Innovations in Engineering, Management & Health Science” — is warmly welcomed, even if it extends beyond the themes mentioned.

📌 Submission Deadline: November  10, 2025 | 📌 Publication Month: 30th November 2025.