Deep Learning Approaches to Understanding Psychological Impacts on Vulnerable Populations

Abstract

This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological well-being in vulnerable groups. The findings of this study not only improve our understanding of psychological dynamics, but also serve as a foundation for creating targeted therapies that take into account the combined influence of climate and other stresses on the mental health of vulnerable groups. This research chapter is critical for understanding policies and actions that promote resilience and reduce negative psychological effects in the face of environmental and socioeconomic difficulties.