ISBN13: | 9781032417233 |
ISBN10: | 1032417234 |
Kötéstípus: | Keménykötés |
Terjedelem: | 332 oldal |
Méret: | 254x178 mm |
Súly: | 780 g |
Nyelv: | angol |
Illusztrációk: | 168 Illustrations, black & white; 168 Halftones, black & white; 50 Tables, black & white |
643 |
Villamosmérnöki tudományok, híradástechnika, műszeripar
A számítástudomány elmélete, a számítástechnika általában
Számítógép architektúrák, logikai tervezés
Szuperszámítógépek
Operációs rendszerek és grafikus felhasználói felületek
Szoftverfejlesztés
Mesterséges intelligencia
Környezetmérnöki tudományok
Villamosmérnöki tudományok, híradástechnika, műszeripar (karitatív célú kampány)
A számítástudomány elmélete, a számítástechnika általában (karitatív célú kampány)
Számítógép architektúrák, logikai tervezés (karitatív célú kampány)
Szuperszámítógépek (karitatív célú kampány)
Operációs rendszerek és grafikus felhasználói felületek (karitatív célú kampány)
Szoftverfejlesztés (karitatív célú kampány)
Mesterséges intelligencia (karitatív célú kampány)
Környezetmérnöki tudományok (karitatív célú kampány)
Applied Computer Vision and Soft Computing with Interpretable AI
GBP 89.99
Kattintson ide a feliratkozáshoz
A Prosperónál jelenleg nincsen raktáron.
This reference text presents the knowledge base of computer vision and soft computing techniques with their applications for sustainable developments.
This reference text presents the knowledge base of computer vision and soft computing techniques with their applications for sustainable developments.
Features:
- Covers a variety of deep learning architectures useful for computer vision tasks
- Demonstrates the use of different soft computing techniques and their applications for different computer vision tasks
- Highlights the unified strengths of hybrid techniques based on deep learning and soft computing taken together that give the interpretable, adaptive, and optimized solution to a given problem
- Addresses the different issues and further research opportunities in computer vision and soft computing
- Describes all the concepts with practical examples and case studies with appropriate performance measures that validate the applicability of the respective technique to a certain domain
- Considers recent real word problems and the prospective solutions to these problems
This book will be useful to researchers, students, faculty, and industry personnel who are eager to explore the power of deep learning and soft computing for different computer vision tasks.
1. Improved Healthcare System using Artificial Intelligence: Technology and Challenges. 2. A brain MRI segmentation method using feature weighting and a combination of efficient visual features. 3. Vision Based Skin Cancer Detection: Various Approaches with Comparative study. 4. MentoCare: An Improved Mental Healthcare System for Public. 5. Employee Health monitoring system using WBANs and Machine Learning. 6. Monitoring operational parameters in manufacturing industry using web analytical dashboards. 7. Concurrent Line Perpendicular Distance Functions for Contour Points Analysis. 8. A Resemblance of Convolutional Neural Network Architectures for Classifying Ferrograph Images. 9. Role of AI and IoT in Smart Agriculture towards Green Engineering. 10. Intuitionistic Fuzzy Hyper Graph with their Operations. 11. Heterogeneous Multiple-mini?Graphs Neural Network based Spammer Detection. 12. Spam email classification using Meta-Heuristic Algorithm. 13. A Blockchain Model for Land Registration Properties in Metro Cities. 14. A review on sentiment analysis applications and challenges. 15. Handling Skewed Datasets in Computing Environments: The Classifier Ensemble Approach. 16. Diagnosis of Dementia Using MRI: A Machine Learning Approach. 17. Optimized Tolerance Based Student?s Face Recognition and Identification Using Deep Learning. 18. Impact of Fake News on Society with Detection and Classification Techniques. 19. Neurological Disorder Detection Using Computer Vision and Machine Learning Technique. 20. Deep Learning for Tea Leaf Disease Classification: Challenges, Study Gaps, and Emerging Technologies.