A gradient boosting machine learning model for neo-epitopes classification - Barde, Romi; Islam, Mohammad; Al Seesi, Sahar (2025 Women in Bioinformatics Workshop (WIBI), 2025)
Keywords: Artificial intelligence; Boosting (machine learning); Computer science; Gradient boosting; Machine learning; Random forest; dept.csc

A pilot study on diabetes detection using handheld fundus camera and mobile app development - Al-Absi, Hamada R. H.; Muchori, Gilbert Njihia; Musleh, Saleh; Basit, Syed Abdullah; Islam, Mohammad Tariqul; Mou, Younss Ait; Alam, Tanvir (Discover Applied Sciences, 2025)
Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.
Keywords: Android Mobile application; Artificial intelligence; Computer graphics (images); Computer science; Computer vision; DMPred; Diabetes; Diabetes mellitus; Diabetic retinopathy; Fundus (uterus); Fundus camera; Handheld camera; Human–computer interaction; Medicine; Mobile apps; Mobile device; Ophthalmology; Ophthalmoscopy; Retinal; Retinal fundus image; World Wide Web; dept.csc; peer-reviewed