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Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals
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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy
Published 2025-03-01“…Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. …”
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Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning anal...
Published 2025-07-01“…This study sought to evaluate the region's surface water quality and sources of contamination using machine learning (ML) methods such as Logistic Regression (LOR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN). …”
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads
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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)
Published 2024-12-01“…To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. …”
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A Hybrid GRA-TOPSIS-RFR Optimization Approach for Minimizing Burrs in Micro-Milling of Ti-6Al-4V Alloys
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Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling
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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques
Published 2024-10-01Get full text
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Enhanced Viral Genome Classification Using Large Language Models
Published 2025-05-01“…Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph
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Comparison of machine learning models for coronavirus prediction
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Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households
Published 2025-08-01“…The classification model was developed using four machine learning algorithms: Random Forest, Gradient boosting, Decision tree, Ridge regression, Neural network, and AdaBoost. …”
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