Multimodal Emotion Recognition Using by Vala Jaykumar (10 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Taschenbuch. Condition: Neu. MULTIMODAL EMOTION RECOGNITION USING DEEP LEARNING NETWORKS | Advanced Deep Learning and Fusion Techniques for Multimodal Emotion Recognition | Jaykumar Vala (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786209086779 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Condition: New.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 136 pp. Englisch.

Paperback. Condition: new. Paperback. This book, "Multimodal Emotion Recognition Using Deep Learning Networks" focuses on improving emotion recognition by combining multiple data sources (modalities) like facial expressions, EEG, and Physiological signals. Deep learning models are used to extract features from each modality, and fusion techniques (such as late fusion approach) integrate these features to make more accurate emotion predictions. The study shows that multimodal fusion significantly boosts performance over single-modality systems, highlighting the importance of combining complementary emotional cues using advanced neural network architectures. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book, 'Multimodal Emotion Recognition Using Deep Learning Networks' focuses on improving emotion recognition by combining multiple data sources (modalities) like facial expressions, EEG, and Physiological signals. Deep learning models are used to extract features from each modality, and fusion techniques (such as late fusion approach) integrate these features to make more accurate emotion predictions. The study shows that multimodal fusion significantly boosts performance over single-modality systems, highlighting the importance of combining complementary emotional cues using advanced neural network architectures.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.