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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms
Published 2024-11-01“…The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. …”
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Machine Learning Algorithms Performance Evaluation for Intrusion Detection
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Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models
Published 2024-09-01“…It addresses the challenge of congestion management through machine learning (ML) models, aiming to enhance network performance and service quality. This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. …”
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Machine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications
Published 2025-01-01“…The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. Its second motivation is to determine the optimum set/number of meteorological data required for nuclear energy projects using the best-performing ML algorithm. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl
Published 2025-02-01Get full text
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Spatiotemporal heterogeneity of the ecological civilization in the Yangtze River economic belt
Published 2025-08-01Get full text
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022-12-01“…Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. …”
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Hybrid Weighted Random Forests Method for Prediction & Classification of Online Buying Customers
Published 2021-04-01“…This research article mainly proposed an extension of the Random Forest classifier named “Weighted Random Forests” (wRF), which incorporates tree-level weights to provide much more accurate trees throughout the calculation as well as an assessment of vector relevance. …”
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