Research on Classification of Rail Defects Based on Image Processing Algorithm by Mengying HUANG, Jiangping LUO, Wenxing WANG, Jingwei CAO

“…Secondly, the feature vectors of different kinds of defects were trained by support vector machine, and the optimal classification function was obtained. …”
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CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis by Ruixue Wang, Ning Zhao

“…Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. …”
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Comparing text classification algorithms with n-grams for mediation prediction by Retzi Y. Lewu, Kusrini Kusrini, Ainul Yaqin

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Chaos Time Series Prediction Based on Membrane Optimization Algorithms by Meng Li, Liangzhong Yi, Zheng Pei, Zhisheng Gao, Hong Peng

“…This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m) and least squares support vector machine (LS-SVM) (γ,σ) by using membrane computing optimization algorithm. …”
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(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. …”
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Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis by Fenilinas Adi Artanto

“…This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. …”
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fathima Fajila, Yuhanis Yusof

“…In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. …”
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Application of machine learning techniques for churn prediction in the telecom business by Raji Krishna, D. Jayanthi, D.S. Shylu Sam, K. Kavitha, Naveen Kumar Maurya, T. Benil

“…These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. …”
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An Innovative Approach for Fake News Detection using Machine Learning by Maya Hisham, Raza Hasan, Saqib Hussain

“…Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. …”
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An IoT and Machine Learning-based Neonatal Sleep Stage Classification by Awais Abbas, Hafiz Sheraz Sheikh, SaadUllah Farooq Abbasi

“…After feature extraction, support vector machine was used for sleep stage classification. …”
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Advancements in Image Classification: From Machine Learning to Deep Learning by Cheng Haoran

“…This paper systematically reviews the growth of image classification technology, beginning with the introduction of commonly used datasets such as CIFAR-10, ImageNet, and MNIST, and exploring their impact on algorithm development. Subsequently, the paper provides an in-depth analysis of image classification methods based on machine learning, including traditional algorithms such as Support Vector Machine (SVM), Random Forest, and Decision Tree. …”
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Machine learning-enabled prediction of bone metastasis in esophageal cancer by Liqiang Liu, Wanshi Duan, Tao She, Shouzheng Ma, Haihui Wang, Jiakuan Chen

“…National Institutes of Health from 2010 to 2020. Six machine learning models were constructed: Support Vector Machine, Logistic Regression, Extreme Gradient Boosting, Neural Network, Random Forest, and k-Nearest Neighbors. …”
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Explainable machine learning for predicting lung metastasis of colorectal cancer by Zhentian Guo, Zongming Zhang, Limin Liu, Yue Zhao, Zhuo Liu, Chong Zhang, Hui Qi, Jinqiu Feng, Peijie Yao

“…Our study has constructed seven ML algorithms based on the data mentioned above, including Random Forest (RF), Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gradient Boosting Machine. …”
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Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…This research examines classification algorithms like K-Nearest Neighbor (KNN), Decision Tree (DT), NaiveBayes (NB), Support Vector Machine (SVM), and Random Forest (RF) with wrapper feature selection techniques like SFFS, SBEFS, CBFS, VIF, and RANDIM for environmental and seed data. …”
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A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction by Bo Li, Yuanqiang Lian

“…Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. …”
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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
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Bioinformatics and machine learning-driven key genes screening for vortioxetine by Sabire Kılıçarslan, Meliha Merve Hız

“…After feature selection for the cleaned dataset, machine learning algorithms such as the K-nearest neighbors' algorithm, Naive Bayes, and Support Vector Machine (SVM) were used. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional neural network, ensemble learning, and adaptive boosting using a dataset gathered from a coke plant are developed to predict CRI. …”
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Application of machine learning in predicting adolescent Internet behavioral addiction by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…Six methods—multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting—were used to construct the model. …”
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Machine learning-based fatigue lifetime prediction of structural steels by Konstantinos Arvanitis, Pantelis Nikolakopoulos, Dimitrios Pavlou, Mina Farmanbar

“…Through preprocessing and feature selection, four techniques are explored: Polynomial Regression, Support Vector Regression (SVR), XGB Regression and Artificial Neural Network (ANN), aiming to identify the most effective algorithm. …”
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