Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. …”
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Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive ge... by Wounsuk Rhee, Sam Yeol Chang, Bong-Soon Chang, Hyoungmin Kim

“…Logistic regression, support vector machine (SVM), random forest, XGBoost, and LightGBM were trained using five-fold cross-validation. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…Principal component analysis (PCA) was used for dimensionality reduction and to comprehensively evaluate the models’ predictive capabilities, we used several ML algorithms, including decision trees, k-nearest neighbors (KNN), logistic regression, naive Bayes, random forests, neural networks, XGBoost, and support vector machines (SVM) to predict ARDS risk. …”
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Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis by Cong Hu, Heli Li, Jinru Cui, Yunjie Li, Feiyan Zhang, Hao Li, Xiaoping Luo, Yan Hao

“…Through integrative analysis combining differential gene expression profiling with three machine learning algorithms - Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and RandomForest combined with eXtreme Gradient Boosting (XGBoost) - we identified four hub genes, with JUN emerging as a core regulator. …”
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Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American populat... by Qian Li, Yuan Tian, Wei Peng, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Mengchao Sheng, Yongyou Wu

“…Objective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC).Design Retrospective cohort study.Setting Second Affiliated Hospital of Soochow University.Participants A total of 500 inpatients from the Second Affiliated Hospital of Soochow University, collected retrospectively between 1 April 2018 and 31 March 2023, were used as the training set, while 824 Asian patients from the Surveillance, Epidemiology and End Results database comprised the external validation set.Main outcome measures Prediction models were developed using multiple ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, naive Bayes, decision tree (DT), gradient boosting DT, random forest and artificial neural network (ANN). …”
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Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors by Ya Li Bachelor, Min Zhang Bachelor, Yong Hu MM, Bo Du Bachelor, Youlong Mo Bachelor, Tianchu He MM, Mingdan Zhao Bachelor, Benlan Li Bachelor, Ji Xia Bachelor, Zhongjun Huang Bachelor, Fangyang Lu MD, Zhen Huang Bachelor, Bing Lu MD, Jie Peng MD

“…Third, radiological models were built using six machine learning algorithms: logistic regression (LR), discriminant analysis (DA), neural network (NN), random forest (RF), support vector machine (SVM) and K-Nearest Neighbour (KNN). …”
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Modeling the Relationship between Financial Stability and Banking Risks: Artificial Intelligence Approach by Hakeem Faraj Gumar, Parviz Piri, Mehdi Heydari

“…These methods include artificial neural network, deep neural network, convolutional neural network, recurrent neural network, self-organizing neural network, gradient boosting, random forest, decision tree, spatial clustering, k-means algorithm, k-nearest neighbor, support vector regression and support vector machine. …”
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Evaluation method of e-government audit information based on big data analysis by Jingui He, Hansi Ya

“…Results demonstrate that the proposed parallel PSO-RF algorithm outperforms conventional RF and Support Vector Machine (SVM) approaches across multiple indicators, with a maximum prediction deviation of only 0.28 % compared to actual audit issue probabilities. …”
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Driving Pattern Analysis, Gear Shift Classification, and Fuel Efficiency in Light-Duty Vehicles: A Machine Learning Approach Using GPS and OBD II PID Signals by Juan José Molina-Campoverde, Juan Zurita-Jara, Paúl Molina-Campoverde

“…Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. …”
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Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets by Sheng Liu, Conghao Liu, Xunan An, Xin Liu, Liang Hao

“…Validation trials demonstrated that the proposed model achieved a mean absolute percentage error of 20.09% compared with 33.18% of a support vector machine regression (SVMR) model. The root-mean-square error of the proposed model was 33.94, whereas that of the SVMR model was 68.16. …”
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Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retr... by Hongyi Li, Cancan Chang, Bo Zhou, Yu Lan, Peizhuo Zang, Shannan Chen, Shouliang Qi, Ronghui Ju, Yang Duan

“…Therefore, we employed a support vector machine (SVM) algorithm to analyze asymmetrically prominent cortical veins (APCVs) and deep medullary veins (DMVs) to establish a radiomic model for predicting the prognosis of AIS by combining clinical indicators. …”
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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Subsequently, machine learning models (Random Tree - RT, Random Forest - RF, Linear Regression - LR, K-Nearest Neighbors - KNN, Support Vector Regression - SVR, and Multilayer Perceptron - MLP) were trained, optimized, and evaluated using k-fold cross-validation, followed by statistical analysis. …”
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Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station by Luis Hernán Ochoa Gutiérrez, Luis F Niño, Carlos A. Vargas

“…We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS's National Seismological Network at a three-component station located near Bogota, Colombia. …”
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Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating Hig... by Xie Y, Yang M, Huang J, Jiang Z

“…The least absolute shrinkage and selection operator, as well as the support vector machine with random forest algorithm, were utilized for machine learning. …”
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Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection by Murat Ekinci, Furkancan Demircan, Zafer Cömert, Eyup Gedikli

“…Furthermore, the Support Vector Machine (SVM) model achieved an accuracy of 92% using a feature map comprising features selected by a range of optimization algorithms. …”
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Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive e... by Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie

“…Six ML algorithms were implemented: decision tree (DT), support vector machine (SVM), generalized linear model (GLM), gradient boosting machine (GBM), random forest (RF), and XGBoost. …”
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Cancer staging diagnosis based on transcriptomics and variational autoencoder by LI Jiarui, QIAN Li, SHEN Junjie

“…Subsequently, the performance efficiency of our IFRSVAE model was evaluated in conjunction with Random Forest (RF), Support Vector Machine(SVM), and eXtreme Gradient Boosting (XGboost), and it was also compared with other methods. …”
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Contribution of hydrogeological, well logs and machine learning in predicting the aquifer hydraulic properties in arid regions: a case study of Nubian Sandstone aquifer, Farafra Oa... by Ahmed Nosair, Muhammad Nabih, Ahmed Bakry

“…Consequently, this research aims to use conventional well log and hydrogeological data to predict T, K, and PHIE using machine learning (ML) algorithms, including random forest (RF), gradient boosting (GB), linear regression (LR), and support vector machines (SVM). …”
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Outliers and anomalies in training and testing datasets for AI-powered morphometry—evidence from CT scans of the spleen by Yuriy Vasilev, Yuriy Vasilev, Anastasia Pamova, Tatiana Bobrovskaya, Anton Vladzimirskyy, Anton Vladzimirskyy, Olga Omelyanskaya, Elena Astapenko, Artem Kruchinkin, Novik Vladimir, Kirill Arzamasov, Kirill Arzamasov

“…Using visual methods (1.5 interquartile range; heat map; boxplot; histogram; scatter plot), machine learning algorithms (Isolation forest; Density-Based Spatial Clustering of Applications with Noise; K-nearest neighbors algorithm; Local outlier factor; One-class support vector machines; EllipticEnvelope; Autoencoders), and mathematical statistics (z-score, Grubb’s test; Rosner’s test).ResultsWe identified measurement errors, input errors, abnormal size values and non-standard shapes of the organ (sickle-shaped, round, triangular, additional lobules). …”
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