Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models. Then, it evaluates and selects from three time-series decomposition methods. …”
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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. …”
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Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study by Heonyi Lee, Yi-Jun Kim, Jin-Hong Kim, Soo-Kyung Kim, Tae-Dong Jeong

“…We evaluated the performance of 4 ML models: gradient boosting machine, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB). …”
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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

“…This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. …”
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Production and optimization of affordable artificial geopolymer aggregates containing crumb rubber, plastic waste, and granulated cork based on machine learning algorithms by Mohamed Abdellatief, Abedulgader Baktheer, Mohamed Shahin, Aref A. Abadel, Ashraf M. Heniegal

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Chamber shape optimization for ultra-high-pressure water-jet nozzle based on computational fluid dynamics method and a data-driven surrogate model by Wen-Tao Zhao, Zheng-Shou Chen, Yuan-Jie Chen, Jiang-Long Li

“…Then, to reduce CFD computational costs and accelerate calculation speed, a data-driven surrogate model based on IWOA-support vector machine (SVM) was proposed. Herein, the parameter gamma and the penalty coefficient in SVM model, are trained using IWOA. …”
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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…The supervised machine learning algorithms used include K-Nearest Neighbor (KNN), decision tree classifier (CART), Naïve Bayes (NB) and Support vector machine (SVM). …”
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A Prediction Model of Stable Warfarin Doses in Patients After Mechanical Heart Valve Replacement Based on a Machine Learning Algorithm by Bowen Guo, Cong Chen, Junhang Jia, Jubing Zheng, Yue Song, Taoshuai Liu, Kui Zhang, Yang Li, Ran Dong

“…Results: A total of 413 patients were included in this study for model training and validation, and 13 important features were selected for model development. The support vector machine radial basis function (SVM Radial) algorithm showed the best performance of all models, with the highest R2 value of 0.98 and the lowest MAE of 0.14 mg/day (95% confidence interval (CI): 0.11–0.17). …”
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Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes by Xunjie Yang, Weng Lan, Chunyi Lin, Chunyu Zhu, Zicong Ye, Zhishi Chen, Guian Zheng

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Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“…The proposed system is built on a multitude of machine-learning algorithms such as Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) to render accurate estimations of patient waiting times. …”
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Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms by Marcelo Augusto Garcia-Junior, Bruno Silva Andrade, Ana Paula Lima, Iara Pereira Soares, Ana Flávia Oliveira Notário, Sttephany Silva Bernardino, Marco Fidel Guevara-Vega, Ghabriel Honório-Silva, Rodrigo Alejandro Abarza Munoz, Ana Carolina Gomes Jardim, Mário Machado Martins, Luiz Ricardo Goulart, Thulio Marquez Cunha, Murillo Guimarães Carneiro, Robinson Sabino-Silva

“…Cyclic voltammetry (CV) using a [Fe(CN)₆]^3−/4 solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. …”
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A New Topology of Three-Phase Four-Wire UPQC with a Simplified Control Algorithm by Yash Pal, A. Swarup, B. Singh

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Detection of Irrigated and Non-Irrigated Soybeans Using Hyperspectral Data in Machine-Learning Models by Izabela Cristina de Oliveira, Ricardo Gava, Dthenifer Cordeiro Santana, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Mayara Favero Cotrim, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro

“…The models tested were artificial neural networks (ANN), decision trees (DT), linear regression (LR), M5P algorithm, random forest (RF), and support vector machine (SVM). …”
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Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review by Zhenli Chen, Jie Hao, Haixia Sun, Min Li, Yuan Zhang, Qing Qian

“…Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. …”
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An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network by Fatemeh Safara, Amin Salih Mohammed, Moayad Yousif Potrus, Saqib Ali, Quan Thanh Tho, Alireza Souri, Fereshteh Janenia, Mehdi Hosseinzadeh

“…Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined.…”
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Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

“…The authors only employ the support vector machine to determine whether a sample is malware or not. …”
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Machine learning identifies 6-gene signature in peripheral blood for pancreatic cancer diagnosis by Francisco Carrillo-Perez, Octavio Caba, Cristina Jiménez-Luna, Francisco Ortuño, Daniel Castillo-Secilla, Luis Javier Herrera, Jose Prados, Ignacio Rojas

“…Feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by support vector machine (SVM) classification, identified a 15-gene signature derived from the exLR data. …”
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Estimated ultimate recovery prediction of shale gas wells based on stacked integrated learning algorithm by Min Pang, Zheyuan Zhang, Zhaoming Zhou, Wendi Zhou, Qiong Li

“…The method employs Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) as base learners, with Logistic Regression (LR) as the meta-learner. …”
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Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification by Sherzod Abdumalikov, Jingeun Kim, Yourim Yoon

“…Two different EEG datasets, EEG Emotion and DEAP Dataset, containing 2548 and 160 features, respectively, were evaluated using random forest (RF), logistic regression, XGBoost, and support vector machine (SVM). For both datasets, the experimented three feature selection methods consistently improved the accuracy of the models. …”
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Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning by Ellen Vanden Broecke, Laurens Van Mulders, Ellen De Paepe, Dominique Paepe, Sylvie Daminet, Lynn Vanhaecke

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