Computational Optimization

Machine Learning and Fuzzy Systems

Narinder Kaur, Bobbinpreet Kaur, Yonis Gulzar, Dac-Nhuong Le

• Publisher: De Gruyter
• Year: 2026
• Topic: Mathematics
• eBook ISBN:
  9783112219447
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Mathematics

Data science, quantitative analysis, computer optimisation, and machine learning are all explored in this book. Insightful mathematical analysis of ML models is presented in this work. Various fields, including healthcare, banking, and e-commerce, are covered in this book through case studies. To choose features, hyperparameters, and models, the latter shows how optimisation can be implemented. Practical insights on the assessment and improvement of machine learning models in real-world situations, such as e-commerce, healthcare, banking, and agriculture, are included. Professionals in the fields of research, engineering, mathematics, and computer science are the target audience for this book. Through the integration of theoretical principles with practical applications, the usage of computational optimisation techniques in real-time settings is to be enhanced. 

Table of contents

• Title Page
• Copyright
• Contents
• Frontmatter
• Contents
• List of Authors
• Chapter 1 Bibliometric Review of Bibliometric Studies on Machine Learning Research Trends
• Chapter 2 Machine Learning and Computation-Enabled Smart Systems
• Chapter 3 Optimization for Machine Learning: Regression and Classification
• Chapter 4 Optimization for Machine Learning: Advancing Unsupervised Learning Techniques
• Chapter 5 Fuzzy-Based Mathematical Modeling
• Chapter 6 Transforming Smart Buildings with AI: Enhancing Energy Efficiency, Comfort, and Cost-Effectiveness
• Chapter 7 Optimization for Recommender Systems
• Chapter 8 Bridging Innovation and Vision: AI-Driven Method in Computer Vision and Image Processing
• Chapter 9 Hyper-parameter Optimization for Machine Learning
• Chapter 10 Advances in Artificial Neural Networks, Machine Learning, and Computational Intelligence: Emerging Trends and Future Perspectives
• Chapter 11 Challenges and Ethical Considerations in Computational Optimization: A Responsible AI Perspective
• Chapter 12 Future Directions and Emerging Trends in Computational Optimization Related to Data Science and Machine Learning
• Chapter 13 Case Studies and Real-World Applications: Machine Learning and Fuzzy Logic-Based Computational Optimization
• Index
• Index