RMD Open

Distinct pattern of comorbidities hinders treatment target attainment in SLE through persistent disease activity and delayed glucocorticoid tapering: longitudinal data fro…

Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...
Scientific Research Publishing

Cybersecurity and Forensic Audio Analysis: Deepfake Detection Based on Mfcc, Audio-Text Disconsistency, and Prosodic Features ()

1 Department of Computer and Instructional Technologies Education, Gazi Faculty of Education, Gazi University, Ankara, Türkiye. 2 Department of Forensic Informatics, Institute of Informatics, Gazi ...
IEEE

An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository

Abstract: The number of exploits of Docker images involving the injection of adversarial behaviors into the image’s layers is increasing immensely. Docker images are a fundamental component of Docker.
Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...
IEEE

Artificial Intelligence-Based Random Forest Classifier for Student Management System

Abstract: In recent years, predicting academic performance of student has become vital to allow educational stakeholders for enabling proactive educational interventions and personalized learning.