Next-Generation AI: Towards Explainable and Optimized Intelligent Systems
Description
This book addresses all three challenges in an integrated and principled way-offering researchers, engineers, and practitioners a coherent, application-driven resource that bridges theory and deployment. As AI systems move from research benchmarks into clinical workflows, industrial deployments, and everyday decision support, three imperatives have become central to responsible AI development: making systems genuinely explainable, privacy-compliant, and computationally efficient enough for real-world use.
This book brings together fifteen original contributions from research teams spanning universities, engineering schools, and industrial R&D centres. Its defining feature is the explicit connection it draws between formal explainability theory and practical AI systems: rather than treating interpretability as a post-hoc concern, this book argues-and demonstrates-that genuine transparency must be built into system architecture from the outset, with neuro-symbolic integration positioned as its deepest available foundation.
Organised into four thematically coherent parts, this book covers explainability from symbolic foundations through to applied clinical and agricultural systems; privacy-preserving learning via federated architectures, blockchain-secured aggregation, and differential privacy; model optimisation and efficient deployment through quantisation, hyperparameter search, and knowledge distillation; and generative AI with vision-language models, large language model-driven annotation, and multimodal plant disease detection.
Application domains include dermatology, breast cancer diagnosis, dental imaging, diabetes prediction, smart agriculture, and natural language processing for medical text.
The intended readership includes graduate students and researchers in artificial intelligence, machine learning, and biomedical engineering, as well as applied scientists and engineers seeking rigorous, deployable solutions to explainability, privacy, and efficiency challenges.
This book is equally valuable as a reference for practitioners navigating regulatory requirements such as the EU AI Act and GDPR, where the distinction between apparent and verifiable transparency has direct legal and ethical significance.
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Content
Neuro Symbolic Artificial Intelligence: Reasoning, Generalisation, and Open Challenges.- Explainable Deep Learning-Based Multi-Scale Caries Detection A Comparative Analysis of YOLOv8 and YOLO11 Architectures.- Optimized and Explainable Machine Learning for Early Diabetes Detection and Complication Prediction.- Optimized and Explainable AI Framework for Adaptive Solar Pumping Systems in Smart Agriculture.- AI Driven Skin Lesion Analysis: Datasets, Deep Learning Architectures, and Explainable Artificial Intelligence.- Privacy Preserving Explainable AI Balancing Automated Optimization, Interpretability, and Data Protection in Healthcare.- Preserving Privacy in Breast Cancer Diagnosis Harnessing Federated Learning and Blockchain for Secure and Accurate Results.- Enhancing the Classification Performance of Fine-Tuned Deep Learning Models The Role of Hyperparameter Optimization in Skin Cancer Detection.- Model Compression and Quantization for Healthcare AI Enabling Efficient Medical Diagnosis.- A Real Time Model for Skin Cancer Detection under Severe Class Imbalance.- Deep Learning Approaches for Breast Cancer Classification Across Medical Imaging Modalities.- A New GenAI Based Strategy for Label Enrichment in Medical Named Entity Recognition on the DEFT 2020 Dataset.- Vision Language Models Architectures, Benchmarks, and Open Challenges.- Hybrid GenAI driven Tool for Automatic Image Annotation AutoNota.- Optimizing Multimodal Pipelines with Video Language Models Robustness and Efficiency in Plant Disease Detection.