Multimodal Deep Learning for Poverty Prediction - Couverture souple

Brown, Christian

Couverture souple

ISBN 13 : 9798182713115

Editeur : Nala Kala, 2026

Synopsis

Multimodal Deep Learning for Poverty Prediction examines the application of deep learning and multimodal data analysis to the prediction and assessment of poverty-related conditions. The book explores how information from different data modalities can be integrated to identify patterns, relationships, and indicators associated with socioeconomic conditions. It introduces fundamental concepts in deep learning, multimodal machine learning, data fusion, predictive modeling, and socioeconomic data analytics. Attention is given to methods for combining heterogeneous information sources, extracting meaningful representations, developing predictive models, and evaluating their performance. The discussion considers important analytical factors such as data quality, feature representation, model learning, classification, prediction, and interpretation of results. By connecting multimodal artificial intelligence with poverty prediction, the book provides a technical perspective on how computational methods can support quantitative socioeconomic analysis. It is intended for students, researchers, data scientists, economists, social scientists, engineers, and professionals interested in artificial intelligence, machine learning, data analytics, development studies, and computational approaches to socioeconomic research. The book provides a foundation for understanding multimodal deep learning techniques and their role in data-driven poverty analysis.

Détails de l'édition

• Éditeur: Nala Kala
• Date d'édition: 2026
• Langue: anglais
• ISBN 13: 9798182713115
• Reliure: Paperback
• Nombre de pages: 150
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible