Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). …”
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Soot Mass Concentration Prediction at the GPF Inlet of GDI Engine Based on Machine Learning Methods by Zhiyuan Hu, Zeyu Liu, Jiayi Shen, Shimao Wang, Piqiang Tan

“…To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. …”
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Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization by Waqar Ashiq, Samra Kanwal, Adnan Rafique, Muhammad Waqas, Tahir Khurshaid, Elizabeth Caro Montero, Alicia Bustamante Alonso, Imran Ashraf

“…Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. …”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…These parameters feed into the digital twins, further refining the predictive and diagnostic capabilities of the models. The ML algorithms used include Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbours (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Neural Network (NN), AdaBoost (AB), Bagging (Ba), Extra Trees (ET), and XGBoost (XGB). …”
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Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules by Mohan Rao, Vahid Nassiri, Sanjay Srivastava, Amy Yang, Satjit Brar, Eric McDuffie, Clifford Sachs

“…We also calculated 55 physicochemical properties for these compounds. Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). …”
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CLASSIFICATION OF IRRIGATION MANAGEMENT PRACTICES IN MAIZE HYBRIDS USING MULTISPECTRAL SENSORS AND MACHINE LEARNING TECHNIQUES by João L. G de Oliveira, Dthenifer C. Santana, Izabela C de Oliveira, Ricardo Gava, Fábio H. R. Baio, Carlos A da Silva Junior, Larissa P. R. Teodoro, Paulo E. Teodoro, Job T de Oliveira

“…Data were analyzed using machine learning techniques, testing six algorithms: Logistic Regression (RL), REPTree (DT), J48 Decision Trees (J48), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). …”
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Diagnostic immune-related markers for diabetic kidney disease: a bioinformatics and machine learning approach by Ying Wang, Xinyuan Zhou, Yuxin Jiang, Ling Jiang, Li Gao, Xueqi Liu, Xiaoxia Wang, Chenyu Sun, Yonggui Wu

“…This study aimed to identify immune-related diagnostic biomarkers for DKD and explore their association with immune cell infiltration.Methods Three glomerular transcriptomic datasets (53 DKD, 36 controls) were analyzed via batch-corrected differential expression analysis to screen immune-related differentially expressed genes (DEGs). Machine learning algorithms (least absolute shrinkage and selection operator, support vector machine - recursive feature elimination) prioritized biomarkers, validated by RT-PCR in db/db mice. …”
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Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model by Bowen Zhang, Liang Chen, Tao Li

“…Four ML algorithms—random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)—were used alongside traditional logistic regression to predict CKD. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The performance of various ML models, including decision trees, support vector machines, and neural networks, is evaluated using metrics such as accuracy, AUC, recall, precision, F1, Kappa, and MCC. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

“…Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…The Random Forest (RF), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Extreme Gradient Boosting (XG-Boost) were identified as the mostly used ML algorithms. …”
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Advanced signal processing algorithm for fault classification and localization in VSC-HVDC based Offshore wind farm by Rehana Perveen

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A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning by Mostafa Zakeri, Amirhossein Atef, Mohammad Aziznia, Azadeh Jafari

“…Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. …”
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The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques by Rafał J. Doniec, Natalia Piaseczna, Konrad Duraj, Szymon Sieciński, Muhammad Tausif Irshad, Ilona Karpiel, Mirella Urzeniczok, Xinyu Huang, Artur Piet, Muhammad Adeel Nisar, Marcin Grzegorzek

“…Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebriation (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. …”
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Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children by Jintuo Zhou, Yongjin Xie, Ying Liu, Peiguang Niu, Huajiao Chen, Xiaoping Zeng, Jinhua Zhang

“…A stepwise logistic regression model was employed to select the features included in the final model. Six machine learning algorithms—logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN)—were employed to construct predictive models for DIC in critically ill children. …”
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Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients by Yingxi Chen, Chunyu Wang, Chunyu Wang, Xiaozhu Liu, Minjie Duan, Tianyu Xiang, Haodong Huang, Haodong Huang

“…The training set data were used to screen features using Logistic regression, Lasso regression, or recursive feature elimination (RFE). Five machine learning algorithms, including Logistic regression, Support Vector Machine (SVM), Random Forest (RF), eXtreme gradient boosting (XgBoost), and Light Gradient Boosting Machine (LightGBM), were selected for modeling. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…To mitigate potential class imbalance, the dataset was increased to 620 samples using the Synthetic Minority Over-sampling Technique (SMOTE). Machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boost Classifier (GBC), Multilayer Perceptron (MLP), and 1D Convolutional Neural Network (1DCNN) were developed and evaluated. …”
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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. …”
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A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region by Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche

“…Given the limited studies on ensemble and Machine Learning (ML) models for drought forecasting, this research compares five ML models [Robust Linear Regression, Bagged Trees, Boosted Trees, Support Vector Machine (SVM), and Matern Gaussian Process Regression (GPR)] to determine superior accuracy in the regional context. …”
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