arXiv:2606. 24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression.
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The paper presents a data‑driven study of male domestic violence (MDV) in Bangladesh, using exploratory data analysis to uncover patterns such as verbal abuse prevalence and the influence of financial dependency. It evaluates 10 traditional ML models, 3 deep learning models, and 2 ensemble models, ultimately proposing a stacking ensemble with ANN and CatBoost base classifiers and Logistic Regression meta‑model that achieves 95% accuracy and 99.29% AUC. Explainable AI techniques (SHAP, LIME) and statistical validation confirm the model’s superior performance and highlight key features driving predictions.
arXiv:2412. 06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role.
The paper presents a decision‑aware framework for predicting dementia‑related crash severity that emphasizes auditability and selective deferral. Using 4,781 Texas crash records, the authors evaluate several models—including structured, narrative, fusion, calibrated fusion, BERT‑family, and local large‑language‑model baselines—under a stratified 70/15/15 split. The leakage‑controlled Gemma model achieves the highest macro‑F1 of 0.545, while a calibrated fusion model reaches 0.522 macro‑F1 with an expected calibration error of 0.033; selective deferral further improves performance, raising macro‑F1 to 0.573 at 70% coverage and reducing severity cost to 0.577.
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
arXiv:2609.07766v1 Announce Type: cross Abstract: Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidenc...
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...