Machine Learning in Multimedia (eBook, PDF)
Unlocking the Power of Visual and Auditory Intelligence
Redaktion: Kumar Swarnkar, Suman; Bhushan, Bharat; Somasekar, J.; Sharma, Annu
Alle Infos zum eBook verschenken
Sollten wir den Preis dieses Artikels vor dem Erscheinungsdatum senken, werden wir Ihnen den Artikel bei der Auslieferung automatisch zum günstigeren Preis berechnen.
Machine Learning in Multimedia (eBook, PDF)
Unlocking the Power of Visual and Auditory Intelligence
Redaktion: Kumar Swarnkar, Suman; Bhushan, Bharat; Somasekar, J.; Sharma, Annu
- 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 book explores the interdisciplinary nature of machine learning in multimedia, highlighting its intersections with fields such as computer vision, natural language processing, and audio signal processing. It uses case studies and examples to discuss the potential of machine learning in the realm of multimedia.
- Geräte: PC
- mit Kopierschutz
- eBook Hilfe
- Machine Learning in Multimedia (eBook, ePUB)77,95 €
- IoT and Analytics in Renewable Energy Systems (Volume 2) (eBook, PDF)52,95 €
- IoT and Analytics in Renewable Energy Systems (Volume 1) (eBook, PDF)52,95 €
- Cybersecurity Issues in Emerging Technologies (eBook, PDF)47,95 €
- Industrial Artificial Intelligence Technologies and Applications (eBook, PDF)0,99 €
- The Spirit of Recovery (eBook, PDF)52,95 €
- The Next Generation Innovation in IoT and Cloud Computing with Applications (eBook, PDF)52,95 €
-
-
-
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: 170
- Erscheinungstermin: 10. Dezember 2024
- Englisch
- ISBN-13: 9781040226421
- Artikelnr.: 72254211
- Verlag: Taylor & Francis
- Seitenzahl: 170
- Erscheinungstermin: 10. Dezember 2024
- Englisch
- ISBN-13: 9781040226421
- Artikelnr.: 72254211
Chronic Diseases 2. A Novel Approach to Multimedia Malware Detection using
Bi-LSTM and Attention Mechanisms 3. Exploring Machine Learning Applications
for Enhancing Security and Privacy in Multimedia IoT: A Comprehensive
Review 4. Advanced Machine Learning Strategies for Road Object Detection in
Multimedia Environments 5. A Multimedia-Driven Machine Learning Approach
for Mastitis Detection in Dairy Cattle 6. Music Genre Classification using
Long Short-Term Memory (LSTM) Networks: Analyzing Audio Spectrograms for
Enhanced Multimedia Understanding 7. Deep Learning-Based Image Recognition
for Autonomous Vehicles: Enhancing Safety and Efficiency 8. Identification
of Heart Disease Risk in Early Ages with Bagging Techniques 9. EEG-based
Emotion Recognition using SVM Classifier 10. Mortality Prediction of
Neonatal due to Jaundice Using Machine Learning 11. ML Techniques
Implementation for Heart Prediction in Healthcare 12. Analyzing the
Performance of ML Classification Algorithms for Stroke Prediction
Chronic Diseases 2. A Novel Approach to Multimedia Malware Detection using
Bi-LSTM and Attention Mechanisms 3. Exploring Machine Learning Applications
for Enhancing Security and Privacy in Multimedia IoT: A Comprehensive
Review 4. Advanced Machine Learning Strategies for Road Object Detection in
Multimedia Environments 5. A Multimedia-Driven Machine Learning Approach
for Mastitis Detection in Dairy Cattle 6. Music Genre Classification using
Long Short-Term Memory (LSTM) Networks: Analyzing Audio Spectrograms for
Enhanced Multimedia Understanding 7. Deep Learning-Based Image Recognition
for Autonomous Vehicles: Enhancing Safety and Efficiency 8. Identification
of Heart Disease Risk in Early Ages with Bagging Techniques 9. EEG-based
Emotion Recognition using SVM Classifier 10. Mortality Prediction of
Neonatal due to Jaundice Using Machine Learning 11. ML Techniques
Implementation for Heart Prediction in Healthcare 12. Analyzing the
Performance of ML Classification Algorithms for Stroke Prediction