| Titre : |
Math optimization for artificial intelligence : heuristic and metaheuristic methods for robotics and machine learning |
| Type de document : |
document électronique |
| Auteurs : |
Umesh Kumar Lilhore, Éditeur scientifique ; Vishal Dutt, Éditeur scientifique ; T. Ananth Kumar, Éditeur scientifique ; Martin Margala, Éditeur scientifique ; Kaamran Raahemifar, Éditeur scientifique |
| Editeur : |
Berlin : De Gruyter |
| Année de publication : |
2025 |
| Collection : |
Mathematical methods in the digital age num. Vol. 2 |
| Importance : |
1 fichier PDF (13.3 Mo) |
| ISBN/ISSN/EAN : |
978-3-11-143618-0 |
| Note générale : |
Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Mathematical optimization
Artificial intelligence -- Mathematics |
| Index. décimale : |
519.6 : 004.8 Mathématique numérique. Analyse numérique. Programmation. (informatique). Science des ordinateurs : Intelligence artificielle. |
| Résumé : |
The book presents powerful optimization approaches for integrating AI into daily life.This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology.This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead. |
| Note de contenu : |
Summary :
1. The role of mathematical optimization in advanced ai applications
2. An overview of mathematical optimization in artificial intelligence
3. Robust optimization methods for ensuring ai system
4. Swarm intelligence and optimization in AI
5. Privacy and security for 6g's iot-connected future in the age of quantum computing
6. Optimization in natural language processing models for enhanced performance and efficiency
7. Unveiling the intriguing applications of mathematical optimization in artificial intelligence
8. Unleashing the power of evolutionary algorithms: advanced optimization techniques in artificial intelligence
9. Introduction to mathematical optimization techniques in AI
10. Hybrid mathematical optimization techniques in AI
11. Mathematical optimization for enhanced ai-enabled geospatial intelligence
12. Deep learning-based ultrasound analysis using explainable artificial intelligence (xai) methods for breast cancer
13. Explainable artificial intelligence techniques in deep learning-based liver tumor analysis
14. A novel african wild dog optimization (awdo) algorithm for applications of artificial intelligence
15. Artificial intelligence-based control strategies for covid-19 that target different age groups
16. Model optimization in deep learning: theory and application
17. Quantitative analysis for lms using mathematical modeling by artificial
18. Optimizing neural network training by addressing key challenges and advanced techniques
19. Principles and applications of bayesian optimization in AI |
| En ligne : |
https://research.ebsco.com/linkprocessor/plink?id=9819afd6-b2da-3d2e-ad7b-824fd9 [...] |
Math optimization for artificial intelligence : heuristic and metaheuristic methods for robotics and machine learning [document électronique] / Umesh Kumar Lilhore, Éditeur scientifique ; Vishal Dutt, Éditeur scientifique ; T. Ananth Kumar, Éditeur scientifique ; Martin Margala, Éditeur scientifique ; Kaamran Raahemifar, Éditeur scientifique . - Berlin : De Gruyter, 2025 . - 1 fichier PDF (13.3 Mo). - ( Mathematical methods in the digital age; Vol. 2) . ISBN : 978-3-11-143618-0
Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index Langues : Anglais ( eng)
| Mots-clés : |
Mathematical optimization
Artificial intelligence -- Mathematics |
| Index. décimale : |
519.6 : 004.8 Mathématique numérique. Analyse numérique. Programmation. (informatique). Science des ordinateurs : Intelligence artificielle. |
| Résumé : |
The book presents powerful optimization approaches for integrating AI into daily life.This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology.This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead. |
| Note de contenu : |
Summary :
1. The role of mathematical optimization in advanced ai applications
2. An overview of mathematical optimization in artificial intelligence
3. Robust optimization methods for ensuring ai system
4. Swarm intelligence and optimization in AI
5. Privacy and security for 6g's iot-connected future in the age of quantum computing
6. Optimization in natural language processing models for enhanced performance and efficiency
7. Unveiling the intriguing applications of mathematical optimization in artificial intelligence
8. Unleashing the power of evolutionary algorithms: advanced optimization techniques in artificial intelligence
9. Introduction to mathematical optimization techniques in AI
10. Hybrid mathematical optimization techniques in AI
11. Mathematical optimization for enhanced ai-enabled geospatial intelligence
12. Deep learning-based ultrasound analysis using explainable artificial intelligence (xai) methods for breast cancer
13. Explainable artificial intelligence techniques in deep learning-based liver tumor analysis
14. A novel african wild dog optimization (awdo) algorithm for applications of artificial intelligence
15. Artificial intelligence-based control strategies for covid-19 that target different age groups
16. Model optimization in deep learning: theory and application
17. Quantitative analysis for lms using mathematical modeling by artificial
18. Optimizing neural network training by addressing key challenges and advanced techniques
19. Principles and applications of bayesian optimization in AI |
| En ligne : |
https://research.ebsco.com/linkprocessor/plink?id=9819afd6-b2da-3d2e-ad7b-824fd9 [...] |
|  |
519 Analyse combinatoire. Calcul des probabilités, etc.
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