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Applied Computer Vision and Soft Computing with Interpretable AI

Applied Computer Vision and Soft Computing with Interpretable AI (Paperback, 1)

Oscar Castillo, Swati V. Shinde, Darshan V. Medhane (엮은이)
Chapman and Hall/CRC
135,070원

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Applied Computer Vision and Soft Computing with Interpretable AI
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책 정보

· 제목 : Applied Computer Vision and Soft Computing with Interpretable AI (Paperback, 1) 
· 분류 : 외국도서 > 기술공학 > 기술공학 > 공학일반
· ISBN : 9781032417264
· 쪽수 : 332쪽
· 출판일 : 2025-05-26

목차

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.

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