Applied Computer Vision and Soft Computing with Interpretable AI (eBook, PDF)
Redaktion: Shinde, Swati V.; Castillo, Oscar; Medhane, Darshan V.
Alle Infos zum eBook verschenken
Applied Computer Vision and Soft Computing with Interpretable AI (eBook, PDF)
Redaktion: Shinde, Swati V.; Castillo, Oscar; Medhane, Darshan V.
- Format: PDF
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Hier können Sie sich einloggen
Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei bücher.de, um das eBook-Abo tolino select nutzen zu können.
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…mehr
- Geräte: PC
- ohne Kopierschutz
- eBook Hilfe
- Größe: 42.53MB
- Applied Computer Vision and Soft Computing with Interpretable AI (eBook, ePUB)41,95 €
- David E. CloughIntroduction to Engineering and Scientific Computing with Python (eBook, PDF)93,95 €
- Ahmed BanafaIntroduction to Quantum Computing (eBook, PDF)25,95 €
- Artificial Intelligence for Cyber Defense and Smart Policing (eBook, PDF)93,95 €
- Shriram K. VasudevanMachine Learning with oneAPI (eBook, PDF)38,95 €
- Soft Computing in Materials Development and its Sustainability in the Manufacturing Sector (eBook, PDF)50,95 €
- Integration of Cloud Computing with Emerging Technologies (eBook, PDF)76,95 €
-
-
-
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.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 332
- Erscheinungstermin: 5. Oktober 2023
- Englisch
- ISBN-13: 9781000952490
- Artikelnr.: 68602744
- Verlag: Taylor & Francis
- Seitenzahl: 332
- Erscheinungstermin: 5. Oktober 2023
- Englisch
- ISBN-13: 9781000952490
- Artikelnr.: 68602744
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.
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.