Behavioral Data
Behavioral data analysis focuses on extracting insights from observations of actions and interactions, aiming to understand human behavior in various contexts. Current research emphasizes the use of machine learning, particularly deep learning models like transformers and neural networks, to analyze diverse data types including video, sensor data, and clickstream information, often employing multimodal fusion techniques to improve prediction accuracy. This field is significant for its applications across diverse domains, from improving personalized marketing and educational tools to enhancing healthcare and security systems by enabling more accurate predictions and early detection of anomalies.
Papers
Livestock feeding behavior: A tutorial review on automated techniques for ruminant monitoring
José Chelotti, Luciano Martinez-Rau, Mariano Ferrero, Leandro Vignolo, Julio Galli, Alejandra Planisich, H. Leonardo Rufiner, Leonardo Giovanini
Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
David Hason Rudd, Huan Huo, Md Rafiqul Islam, Guandong Xu