The book presents new approaches and methods for solving real-world problems. It highlights, in particular, innovative research in the fields of Cognitive Informatics, Cognitive Computing, Computational Intelligence, Advanced Computing, and Hybrid Intelligent Models and Applications. New algorithms and methods in a variety of fields are presented, together with solution-based approaches. The topics addressed include various theoretical aspects and applications of Computer Science, Artificial Intelligence, Cybernetics, Automation Control Theory, and Software Engineering. 

Chapter 1. Design of Power Efficient and High-Performance Architecture to Spectrum Sensing Applications Using Cyclostationary Feature Detection.- Chapter 2. Detection of Epileptic Seizure based on Relief Algorithm and Multi Support Vector Machine.- Chapter 3. Block Chain Based Shared Security Architecture.- Chapter 4. Assessment of Table Pruning and Semantic Interpretation for Sentiment Analysis using BRAE Algorithm.- Chapter 5. Feature Extraction and Classification between control and Parkinson’s using EMG signal.- Chapter 6. Utilization of Data Analytics Based Approaches for Hassle Free Prediction Parkinson Disease.- Chapter 7. A System for the Study of Emotions with EEG Signals using Machine Learning and Deep Learning.- Chapter 8. Treble band RF energy harvesting system for powering smart textiles.- Chapter 9. Performance analysis of data communication using hybrid NoC for Low Latency and high throughput on FPGA.- Chapter 10. Detection and Prediction of Schizophrenia using MagneticResonance Images and Deep Learning.- Chapter 11. Intrusion Detection Systems (IDS) – An Overview with a Generalized Framework.- Chapter 12. Hybrid machine learning model for context aware Social IoT using location based service under both static and mobility conditions.- Chapter 13. An Efficient Fuzzy Logic Control Based Soft Computing Technique for Grid Tied Photovoltaic System.- Chapter 14. Prediction of Academic Performance of Alcoholic Students using Data Mining Techniques.- Chapter 15. Big Data Analysis and Classification of Bio-medical Signal using Random Forest Algorithm.