Child Abuse

Child abuse detection and prevention are active research areas focusing on improving the accuracy and efficiency of identifying at-risk children. Current research employs machine learning models, including convolutional neural networks (CNNs) for audio analysis and logistic regression models combined with clustering techniques for risk prediction using administrative data, as well as generative models for augmenting limited datasets of medical images. These efforts aim to enhance the effectiveness of child welfare systems by providing social workers with more accurate risk assessments and timely alerts, ultimately improving the safety and well-being of vulnerable children.