15 results for “optimized classifier”

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Research ArticleOpen accessIn the abstract3D-QSAR and SVM Prediction of BRAF-V600E and HIV Integrase Inhibitors: A Comparative Study and Characterization of Performance with a New Expected Prediction Performance MetricLeonard Wesley, Saihitha Veerapaneni, Rachana Desai et al.The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction … …American Journal of Biochemistry and Biotechnology, Vol. 12, Issue 4 · 17 Oct 2016 · 7,802 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordA Metaheuristic-Optimized Feature Selection for Early-Stage Diabetes Prediction With SHAP-Guided Insight into Inﬂuential AttributesEsmay Azam Rochy, Jannatul Ferdaus, Uzzal Biswas et al.Diabetes is a metabolic disorder that causes elevated blood glucose. This long-term health condition can lead to cardiovascular diseases, stroke, kidney failure, visual impairment, neuropathy, and even death in critical cases. So, a Computer-Aided Diagnostic (CAD) system is necessary to diagnose diabetes automatically. A clinician can utilize a machine learning-based CAD system that automatically diagnoses many people. This paper will … …Journal of Computer Science, Vol. 22, Issue 2 · 24 Feb 2026 · 1,736 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordImproving Diabetes Risk Prediction Using Ensemble Boosting and SMOTE-Based Class BalancingKittipol Wisaeng, Pankom Sriboonlue, Benchalak MuangmeesriAccurate diabetes prediction is vital for early intervention, optimized resource allocation, and minimizing long-term complications. This study presents a comparative evaluation of traditional and advanced machine learning models for diabetes classification using a structured clinical dataset. Seven baseline algorithms were assessed against five advanced ensemble methods: CatBoost, LightGBM, XGBoost, Voting Ensemble, and Stacking Ensemble. To improve algorithm learning, the Synthetic … …Journal of Computer Science, Vol. 22, Issue 1 · 1 Feb 2026 · 1,976 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordOptimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector AlgorithmFizhan Kausar, Ramamurthy B.The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, … …Journal of Computer Science, Vol. 21, Issue 12 · 22 Jan 2026 · 1,563 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordA Computational Optimized Extended Model for Mineral Potential Mapping Based on WofE MethodZiaii Mansour, Pouyan Ali, Ziaei MahdiThe multivariate fuzzy-c-means classifier is used to model extended weight of evidence (WofE) considering predictor maps. Approaches to mineral potential mapping based on WofE modeling generally use binary maps, whereas, real-world geospatial data are mostly multi-class or fuzzy-class in nature. The consequent reclassification of fuzzy-class maps into binary maps is a simplification that might result in a loss of information. … …American Journal of Applied Sciences, Vol. 6, Issue 2 · 28 Feb 2009 · 7,561 accessesView PDF Cite