IEEE

Advancing Anomaly Detection: Non-Semantic Financial Data Encoding With Large Language Models

Abstract: Detecting anomalies in general ledger data is of utmost importance to ensure the trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms ...
IEEE

Disease Forecasting Using Climate Data and Machine Learning Techniques

Abstract: Seasons contribute significantly to the prevalence of most diseases, and climate change is bound to raise their prospects. Public health planning relies heavily on climate-sensitive ...
Frontiers

Explainable machine learning for stroke risk prediction: a comparative study with SHAP-based interpretation

Background: Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear ...
Frontiers

Machine learning-based prediction of occupational exposure risks among oral healthcare workers

Objective: This study aims to identify the key risk factors for occupational exposure among oral healthcare workers and develop a predictive model using machine learning algorithms to lay the ...