Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…Several investigations are carried out in linear and non-linear regression models, including Poisson Regressor, Gradient Boosting Regressor, Bayesian Ridge, k-Nearest Neighbours, Lasso, Random Forest, Elastic-Net, and Support Vector Regression, using datasets of welding parameters. …”
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Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology by Zhengyan Xia, Chu Zhang, Haiyong Weng, Pengcheng Nie, Yong He

“…And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. …”
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Hybrid ANFIS systems: Evaluation of bearing capacity of driven piles by Yan Peng, Haiquan Gao

“…To increase the optimal networks’ modeling efficacy, optimization methods were deployed to determine the essential parameters of the simulations. Also, other algorithms were developed for comparison purposes, such as single ANFIS, support vector regression (SVR) M5P, multi-adaptive regression spline (MARS), random forests (RF), and random trees (RT). …”
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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%.…”
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Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. …”
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Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With... by Jun Wang, Jiajun Zhu, Hui Li, Shili Wu, Siyang Li, Zhuoya Yao, Tongjian Zhu, Bi Tang, Shengxing Tang, Jinjun Liu

“…A dual-phase feature selection process, combining least absolute shrinkage and selection operator logistic regression and the Boruta algorithm, was used to identify relevant variables from the multimodal dataset. …”
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Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study by Mingfei Jiang, Xiaoran Li, YongLu

“…After identifying these predictors, we trained and optimized 7 models to predict the risk of social isolation among older adults: Lightgbm, logistic regression, decision tree, support vector machine, random forest, gradient boosting decision tree (Gbdt), and Xgboost. …”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. …”
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A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP by SHENG Wu, WANG Lingzi

“…Case analysis results showed that: ① the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the XGBoost model were 0.93, 0.007, and 0.008, respectively, indicating the highest goodness of fit and the lowest errors compared with random forest (RF), support vector regression (SVR), and gradient boosting decision tree (GBDT). ② The mean relative error of the XGBoost model was 4.478%, demonstrating higher accuracy and better generalization performance compared with the other models. ③ Based on the mean absolute SHAP values of input features, the gas concentration at T1 on the working face had the greatest influence on the gas concentration in the upper corner, followed by the gas concentration in the upper corner extraction pipeline, with the gas content and roof pressure of the mining coal seam following closely. …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

“…The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. …”
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Accelerated discovery of high-density pyrazole-based energetic materials using machine learning and density functional theory by Muhammad Tukur Ibrahim, Muktar Musa Ibrahim, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba

“…The performance of four machine learning algorithms including: multilinear regression, artificial neural network, support vector machines, and random forest algorithms were evaluated. …”
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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms including boosted regression trees (BRT), random forest (RF), and support vector machines (SVM) have been applied in Fars Province, Iran. …”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
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Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach by Mariam Kili Bechir, Ferhat Atasoy

“…We evaluated seven distinct algorithms: Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbors (k-NN), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), Random Forests (RF), and a basic "nonlinear mapping technique". …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…This study aims to compare the performance of several advanced ML models—namely, Artificial Neural Networks (ANNs), Bayesian Algorithm (BA), Least Squares Boosting (Lsboost), Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for shale volume estimation using well log data. …”
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Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool by Jianying Liu, Wei Jiang, Yahong Yu, Jiali Gong, Guie Chen, Yuxing Yang, Chao Wang, Dalong Sun, Xuefeng Lu

“…Clinical data from 471 elderly patients collected between February and December 2023 were utilized for developing and internally validating the model, while 221 patients’ data from March to June 2024 were used for external validation. The Boruta algorithm was applied for feature selection. Models including logistic regression, light gradient boosting machines, support vector machines (SVM), decision trees, random forests, and extreme gradient boosting were evaluated using metrics such as AUC, accuracy, sensitivity, and specificity. …”
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Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum... by Faten Dhawi, Abdul Ghafoor, Norah Almousa, Sakinah Ali, Sara Alqanbar

“…This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. …”
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Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS–SVR) models were established from the perspectives of individual processes and the entire process. …”
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A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly by Barbara Frezza, Mario Cesare Nurchis, Gabriella Teresa Capolupo, Filippo Carannante, Marco De Prizio, Fabio Rondelli, Danilo Alunni Fegatelli, Alessio Gili, Luca Lepre, Gianluca Costa

“…In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated. …”
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