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modeling algorithm » scheduling algorithm (Expand Search)
cscap algorithm » cosine algorithm (Expand Search)
level modeling » event modeling (Expand Search)
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. …”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …”
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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers
Published 2024“…Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. …”
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Developing a UAE-Based Disputes Prediction Model using Machine Learning
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Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting
Published 2011Get full text
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A parallel ant colony optimization to globally optimize area in high-level synthesis. (c2011)
Published 2011Get full text
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A Framework for Predictive Modeling in Sustainable Projects
Published 2012Get full text
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Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms
Published 2023“…Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. …”
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Artificial intelligence models for predicting the mode of delivery in maternal care
Published 2025“…Five machine learning algorithms were evaluated: XGBoost, AdaBoost, random forest, decision tree, and multi-layer perceptron (MLP) classifier. …”
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Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network
Published 2024“…These algorithms include random forest (RF) classification and artificial neural networks (ANN). …”
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Integrated economic and environmental models for a multi stage cold supply chain under carbon tax regulation
Published 2017“…The structural properties for the optimal solution of the three models are identified and solution algorithms are also proposed. …”
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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study
Published 2022“…This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant param eters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. …”
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Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine
Published 2024“…In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. …”
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Prediction of EV Charging Behavior Using Machine Learning
Published 2021“…Therefore, in this paper we propose the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms including random forest, SVM, XGBoost and deep neural networks. …”
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