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IWANN Special Collection 2019: Deep Learning Models in Healthcare and Biomedicine

PLOS Collections - Blogging about research and commentary published in PLOS Collections

Blogging about research and commentary published in PLOS Collections

Research Validates Deep Learning Models for Drowning Diagnosis

Team studies medical validity of deep learning models in diagnosing drowning

"The Role of Deep Learning Models in the Detection of Anti-Social Behav" by Marcella Papini, Umair Iqbal et al.

Increasing women’s active participation in economic, educational, and social spheres requires ensuring safe public transport environments. This study investigates the potential of machine learning-based models in addressing behaviours impacting the safety perception of women commuters. Specifically, we conduct a comprehensive review of the existing literature concerning the utilisation of deep learning models for identifying anti-social behaviours in public spaces. Employing a scoping review methodology, our study synthesises the current landscape, highlighting both the advantages and challenges associated with the automated detection of such behaviours. Additionally, we assess available video and audio datasets suitable for training detection algorithms in this context. The findings not only shed light on the feasibility of leveraging deep learning for recognising anti-social behaviours but also provide critical insights for researchers, developers, and transport operators. Our work