Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
Bibliometric analysis is crucial for evaluating scientific publications, traditionally relying on metrics like citation count ...
Objectives In patients with chronic obstructive pulmonary disease (COPD), severe exacerbations (ECOPDs) impose significant morbidity and mortality. Current guidelines emphasise using ECOPD history to ...
Abstract: Accurate classification of fishes is thus very important in marine biology for species identification, biodiversity monitoring, and ecological studies. Most typical methods used are manual ...
Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...
Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...
Legacy metrics—uptime, latency, MTTR—no longer capture operational value in an AI-driven world. Mean time to prevention (MTTP ...
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This project focuses on building and evaluating machine learning (ML) classification models to predict whether a person has diabetes based on medical and demographic features. It was developed as an ...
Introduction: Weeds represent a critical component of agricultural biodiversity and contribute to a range of ecosystem services, yet they remain a major constraint on global crop production. Remote ...
Abstract: Accurate identification of femoral stem implants in hip arthroplasty is essential for effective revision surgery, minimizing operative complexity, patient morbidity, intraoperative blood ...
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