Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification by Wenjia Chen, Junwei Cheng, Song Yang, Li Sun

“…The mathematical model uses the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>l</mi></mrow><mrow><mn>2,1</mn></mrow></msub></mrow></semantics></math></inline-formula> norm and graph regularized term, which helps induce sparsity, improve robustness to outliers and noise, and enhance the explainability of the data re-expression. We employ the support vector machine or the K-nearest neighbor algorithms in the back-end layer to classify low-dimensional data. …”
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Work Place Safety: Machine Learning Techniques for Assessing Workplace Incident Severity by Munayr Hasan Khalleefah Hassan, Wagdi M. S. Khalifa

“…Analyzing a comprehensive dataset of occupational injury records from Occupational Safety and Health Administration (OSHA), this research explores the efficacy of seven distinct ML algorithms AdaBoostClassifier (ABC), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Extreme Learning Machine (ELM), Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), and Stacked Model (SM). …”
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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Previous studies have focused on traditional machine learning (ML) methods such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and ensemble learning methods for sleep disorders analysis. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

“…The best performing models were random forest (RF) and voom-based diagonal quadratic discriminant analysis (voomDQDA), both achieving 100% accuracy. Support vector machine (SVM) and voom based nearest shrunken centroids (voomNSC) showed excellent performance with 96.7% test accuracy, followed by voom-based diagonal linear discriminant analysis (voomDLDA) at 95.2%. …”
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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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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Four machine- learning algorithms, Random Forest, XGBoost, LightGBM, and Support Vector Machine, were evaluated.ResultsResults showed that the Random Forest model achieved the highest discriminant performance with an AUC of 0.87 and an accuracy of 0.79, and identified key predictors such as sleep disturbance, perceived stress, experiential avoidance, and self-criticism. …”
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A machine learning-based method for predicting the shear behaviors of rock joints by Liu He, Yu Tan, Timothy Copeland, Jiannan Chen, Qiang Tang

“…In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joints. …”
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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…We employ a comprehensive array of machine learning algorithms, including k-Nearest Neighbors, Random Forest, Support Vector Machines, and Feed-Forward Neural Networks, alongside a novel Convolutional Neural Network (CNN) approach. …”
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A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation by Or Inbar, Omri Inbar, Ronen Reuveny, Michael J. Segel, Hayit Greenspan, Mickey Scheinowitz

“…Of the 234 CPETs, 150 (50 in each group) tests were used for the support vector machine (SVM) learning stage, and the remaining 84 tests were used for the model validation. …”
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Assessment of flight fatigue using heart rate variability and machine learning approaches by Dalong Guo, Cong Wang, Yufei Qin, Lamei Shang, Aijing Gao, Baosen Tan, Yubin Zhou, Guangyun Wang

“…A subset of HRV features and the respiratory metric were used as input variables for four machine learning algorithms: decision tree, support vector machine, K-nearest neighbor, and light gradient-boosting machine (LightGBM). …”
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Cybersecurity of smart grids: Comparison of machine learning approaches training for anomaly detection by S. V. Kochergin, S. V. Artemova, A. A. Bakaev, E. S. Mityakov, Zh. G. Vegera, E. A. Maksimova

“…The relative effectiveness of such methods as multifractal analysis using wavelets, the Isolation Forest model, local outlier factor (LOF), k-means clustering, and one-class support vector machine (One-Class SVM), is analyzed.Results. …”
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Evaluating the Thermohydraulic Performance of Microchannel Gas Coolers: A Machine Learning Approach by Shehryar Ishaque, Naveed Ullah, Sanghun Choi, Man-Hoe Kim

“…Furthermore, advanced machine learning algorithms such as extreme gradient boosting (XGB), random forest (RF), support vector regression (SVR), k-nearest neighbors (KNNs), and artificial neural networks (ANNs) were employed to analyze their predictive capability. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…The performance of six classification machine learning algorithms is evaluated and compared—convolutional neural network (CNN), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve-Bayes, and decision tree. …”
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Artificial intelligence (machine learning) in the psychology of learning: Unveiling new insights and directions by Nora Darjazini, Mohammad Hossein Zarghami, Reza Ghorban Jahromi, Leila Shobeiry

“…Results: 260 features were extracted from the computer texts prepared from the speech and writing learners responses by natural language processing (NLP) algorithms. We used learning models of decision tree, nearest neighbor, support vector method, neural network and regularized linear method to predict reading comprehension using extracted linguistic features. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…After feature engineering, including feature selection and dimensionality reduction, the predictors are generated and input into a regressor. Five machine learning algorithms are employed as regressors one by one: linear regression (LR), ridge regression (RR), random forest (RF), support vector machine (SVM), and gradient boosting decision trees (GBDTs). …”
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Older people and stroke: a machine learning approach to personalize the rehabilitation of gait by Elvira Maranesi, Federico Barbarossa, Leonardo Biscetti, Marco Benadduci, Elisa Casoni, Ilaria Barboni, Fabrizia Lattanzio, Lorenzo Fantechi, Daniela Fornarelli, Enrico Paci, Sara Mecozzi, Manuela Sallei, Mirko Giannoni, Giuseppe Pelliccioni, Giovanni Renato Riccardi, Valentina Di Donna, Roberta Bevilacqua

“…Gait analysis was conducted using the GaitRite system, measuring 39 spatio-temporal parameters.ResultsStatistical tests revealed no significant differences, but Principal Component Analysis identified a dominant structure. Machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)—were employed for classification, with RF demonstrating superior performance in accuracy, precision, recall (all exceeding 85%), and F1 score compared to SVM and KNN. …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (HGF), matrix metalloproteinase 13 (MMP13), c-abl oncogene 1, non-receptor tyrosine kinase (ABL1), elastase neutrophil expressed (ELANE), fatty acid synthase (FASN), and long non-coding RNA (Linc00324). …”
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Interpretable machine learning models for prolonged Emergency Department wait time prediction by Hao Wang, Nethra Sambamoorthi, Devin Sandlin, Usha Sambamoorthi

“…We employed five ML algorithms - cross-validation logistic regression (CVLR), random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), and support vector machine (SVM) - for predicting patient prolonged wait times. …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…Diagnostic models were constructed using logistic regression (LR), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep learning (DL) algorithms. …”
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