Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…The applied Machine Learning techniques include Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR). …”
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Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. …”
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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…Feature selection was performed using univariate logistic regression and LASSO regression. Five machine learning algorithms—logistic regression, decision tree, multilayer perceptron, random forest, and support vector machine—were trained and validated. …”
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A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders by Cosmina-Mihaela Rosca, Adrian Stancu

“…Surveys were also conducted to identify the diseases most frequently studied through ML algorithms. Thus, it was found that Alzheimer’s disease (37 articles for Support Vector Regression—SVR; 31 for Random Forest—RF), Parkinson’s disease (46 articles for SVM and 48 for RF), and multiple sclerosis (9 articles for SVM) are the most studied diseases in the field of Neuro-ML. …”
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Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study by Tan Y, Chen G, Wang S, Zhan X, Cheng R, Qiao L, Zhang Z, Liu Y

“…Models were constructed after screening variables using the multiple linear regression and feature importance methods. The model was constructed using five ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). …”
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A Hybrid Approach for Target Discrimination in Remote Sensing: Combining YOLO and CNN-Based Classifiers by Jamesson Lira Silva, Fabiano da Cruz Nogueira, Douglas Damião de Carvalho, Elcio Hideiti Shiguemori, Angelo Passaro

“…The attributes obtained from the CNNs were used as input for three classification algorithms: multilayer perceptron (MLP), logistic regression, and support vector machine (SVM), thereby completing the target discrimination process. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…This research sought to forecast concrete compressive strength through six machine learning (ML) algorithms namely Linear Regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boost (GB), Support Vector Machine (SVM), and Categorical Gradient Boost (CatBoost), and to examine the significance of the input factors on the concrete compressive strength. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K-nearest neighbors. …”
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A multi-model approach for estimation of ash yield in coal using Fourier transform infrared spectroscopy by Sameeksha Mishra, Anup K. Prasad, Arya Vinod, Anubhav Shukla, Shailayee Mukherjee, Bitan Purkait, Atul K. Varma, Bhabesh C. Sarkar

“…A novel approach that uses mid-infrared Fourier Transform Infrared spectroscopy (FTIR) (optical technique) data in the range of 1450–350 cm-1 to identify spectrally sensitive zones (fourteen selective absorption bands) and to predict the ash yield in coal samples is presented. Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. …”
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Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Md Saifuzzaman, Rami Albasha, Maxime Leduc

“…Three ML models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB)—were applied to Harmonized Landsat Sentinel (Landsat only, which is HLSL30) and Sentinel-2 datasets, accessed via the Google Earth Engine (GEE) Python API. …”
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Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. …”
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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R², and RMSE values of 5.5%, 0.971, and 1.263, respectively. …”
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Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…In parallel, the study applied machine learning (ML) models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to predict the mechanical properties of SMA based on input mix parameters. …”
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Integrative genomic analysis and diagnostic modeling of osteoporosis: unraveling the interplay of autophagy, osteogenesis, adipogenesis, and immune infiltration by Lin-Jing Han, Jian-Zong Zhu, Jian-Zong Zhu, Hong-Cai Liu, Xiao-Sheng Lin, Xiao-Sheng Lin, Shu-Zhong Yang

“…The diagnostic model, developed utilizing logistic regression, support vector machine (SVM), and the least absolute shrinkage and selection operator (LASSO), pinpointed nine pivotal genes—AKT1, NFKB1, TNF, CTNNB1, LMNA, BHLHE40, BMP4, WNT1, and COPS3—and confirmed their diagnostic efficacy through validation. …”
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Tanshinone Content Prediction and Geographical Origin Classification of <i>Salvia miltiorrhiza</i> by Combining Hyperspectral Imaging with Chemometrics by Yaoyao Dai, Binbin Yan, Feng Xiong, Ruibin Bai, Siman Wang, Lanping Guo, Jian Yang

“…Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were employed to discriminate 420 <i>Salvia miltiorrhiza</i> samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui Provinces. …”
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Speech emotion recognition using long-term average spectrum by Huerta-Hernández Luis David, Meléndez-Acosta Nayeli Joaquinita, Troncoso-Romero David Ernesto, Ramírez-Pacheco Julio César, León-Borges Jose Antonio

“…Our framework is evaluated through a comparative study, where classifiers such as artificial neural network, K-nearest neighbours, logistic regression, Bayesian algorithms, tree-based logistics, and support vector machine were used. …”
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Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques by Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin

“…Evaluated across six classifiers – Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, and BERT – the results demonstrate that the SEO2 framework consistently enhances classifier performance compared to single oversampling techniques. …”
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Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining by Xinfeng Zhao, Binghui Dong, Shengwen Dong, Wuyi Ming

“…Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…To address the high computational cost resulting from the complex mathematical expressions of traditional high-order yield criteria, this study proposes a data-driven modeling approach for high-order yield criteria aimed at improving computational efficiency in sheet metal forming simulations. Regression models for the yield stress and its first-order derivatives based on the Yld2000–2d yield criterion are developed using several machine learning algorithms, including Random Forest (RF), Multilayer Perceptron (MLP), Histogram-Based Gradient Boosting (HGB), and Support Vector Machine (SVM). …”
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