Evaluating Transfer Learning Models for Indian Rice Classification: The Impact of a Proposed Lightweight Deep Learning.- Defensive Classifier Methods: Feature Extraction and 
Behavior Analysis for Prediction.- Enhancing Core Stability in Federated Learning through Optimal Client Selection.- Machine Learning based Ensemble Learning approach 
for Malware Family Classification Using Visual File Representations.- Transformer Network Based Deep Learning Approach in Customised Dynamic Dietary Endorsements for Chronic Diseases.- Style Infusion: Leveraging Diffusion Process for Style Transfer.- Empowering Emotional Intelligence in Healthcare: ML-Based Facial and speech Emotion Recognition for 
Patient-Centered Systems.- Confirming the need for hyperparameter tuning of Kalman Filter in Weather Forecasting.- Hierarchy for selection of power efficient data-path multiplier hardware for high performance applications.- Performance Improvement in Aperture-Coupled Rectangular DRAs Using Reflector for 5.5 GHz Wireless Communication.

This book presents a diverse set of research contributions across critical domains like healthcare, agriculture, cybersecurity, communication systems, and quantum computing. Ranging from transfer learning frameworks for rice classification and federated learning improvements to facial emotion recognition and quantum-enabled tourism analytics, the chapters offer an in-depth journey into practical, scalable, and cutting-edge applications of AI. Readers also finds state-of-the-art studies on Kalman filters for weather forecasting, power-aware hardware architectures, and the revolutionary potential of explainable AI in medical imaging.
This book presents deep insights into how cognitive systems are addressing real world problems and redefining the future of smart technology.