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Comparative architectural evaluation of CNN models with alternative classifiers for UAV-based detection of damaged green zones

Nodir Rakhimov, Jura Kuvandikov, Ma'ruf Tojiyev, Dilmurod Khasanov et al. • Frontiers in Artificial Intelligence (2026)

Unmanned aerial vehicle imagery provides a practical and high-resolution data source for monitoring the spatial condition of urban and peri-urban green zones. However, the reliable detection of damaged, stressed, or declining vegetation are...

DOAJ Open Access (DOAJ) DOI: 10.3389/frai.2026.1910842

Artificial intelligence and echocardiography

Maryam Alsharqi, William J. Woodward, J. A. Mumith, D. C. Markham et al. • Echo Research and Practice (2018)

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experie...

OPENALEX cc-by DOI: https://doi.org/10.1530/erp-18-0056 PMC: 6280250

An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients

Faezeh Aghamirzaei, Ahmad Ali Abin, Farzaneh Futuhi • Archives of Academic Emergency Medicine (2025)

Introduction: Acute Kidney Injury (AKI) is a severe complication of vancomycin treatment due to its nephrotoxic effects. However, research on predicting AKI in this high-risk group remains limited. This study presents a stacking ensemble ma...

DOAJ Open Access (DOAJ) DOI: 10.22037/aaemj.v13i1.2560

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

Esther Oyaga-Iriarte, Asier Insausti, Onintza Sayar, Azucena Aldaz • Journal of Pharmacological Sciences (2019)

Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this w...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jphs.2019.03.004