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Full Description
This book presents the fundamentals of swarm intelligence, from classic algorithms to emerging techniques. It presents comprehensive theoretical foundations and examples using the main Computational Intelligence methods in programming languages such as Python, Java and MATLAB®. Real-world applications are also presented in areas as diverse as Medicine, Biology and industrial applications.
The book is organized into two parts. The first part provides an introduction to swarming algorithms and hybrid techniques. In the second part, real world applications of swarm intelligence are presented to illustrate how swarm algorithms can be used in applications of optimization and pattern recognition, reviewing the principal methods and methodologies in swarm intelligence.
Contents
SECTION 1: FUNDAMENTALS AND ADVANCEMENTS ON SWARM INTELLIGENCE 1. Swarm Intelligence Based Algorithm for Feature Selection in High-Dimensional Datasets 2. Swarm Intelligence for Data Mining 3. Leveraging Center-Based Sampling Theory for Enhancing Particle Swarm Classification of Textual Data 4. Reinforcement Learning for Out-of-the-box Parameter Control for Evolutionary and Swarm-based Algorithm SECTION 2: APPLICATIONS 5. Recognition of Emotions in the Elderly Through Audio Signal Analysis 6. Recognition of Emotions in the Elderly through Facial Expressions: A Machine Learning-Based Approach 7. Identification of Emotion Parameters in Music to Modulate Human Affective States 8. Clinical Decision Support in the Care of Symptomatic Patients with COVID-19: An Approach Based on Machine Learning and Swarm Intelligence 9. The Sound of the Mind: Detection of Common Mental Disorders Using Vocal Acoustic Analysis and Machine Learning
