Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…This helps eliminate redundant information and noise, extract key features, and increase robustness. Finally, the Support Vector Machine is utilized to detect different feature vector signals. …”
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Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study by Jie Zhang, Xinyi Feng, Wenhe Wang, Shudan Liu, Qin Zhang, Di Wu, Qin Liu

“…Compared to random forest (AUC: 0.87 (95%CI: 0.80, 0.93)), logistic regression (AUC: 0.80 (95%CI: 0.70, 0.89)), neural network (AUC: 0.80 (95%CI: 0.71, 0.89)), and support vector machine (AUC: 0.79 (95%CI: 0.79, 0.89)), XGBoost algorithm had the highest AUC values 0.87 (95%CI: 0.80, 0.93) in the test set, although the difference was not significant between models. …”
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Machine Learning-based Disease Classification in Tomato (Solanum lycopersicum) Plants by Md Towfiqur Rahman, Sudipto Dhar Dipto, Israt Jahan June, Abdul Momin, Muhammad Rashed Al Mamun

“…Various textural features were also extracted from segmented leaf images to create a training dataset. Machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and decision trees, were trained and evaluated using this dataset to classify images as healthy or diseased. …”
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Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection by Nazime Tokgöz, Ali Değirmenci, Ömer Karal

“…The classification methods employed include Decision Tree, Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbor, coupled with the Mutual Information (MI) feature selection algorithm. …”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. …”
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Machine learning‐guided plasticity model in refractory high‐entropy alloys by Shang Zhao, Jinshan Li, Weijie Liao, Ruihao Yuan

“…Through feature selection techniques, a critical subset of features is identified, enabling a support vector classification model to achieve 96% prediction accuracy. …”
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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification by Sylvio Barbon, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, Douglas Fernandes Barbin

“…The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. …”
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Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“…This research employs the UCI Mushroom Dataset to evaluate and compare the effectiveness of several machine learning models, including traditional algorithms like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes, as well as advanced ensemble techniques such as Stacking and Voting Classifier. …”
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Comprehensive duck DNA fingerprinting based on machine learning for breed identification by DengKe Yan, Feng Zhu, HaoLin Wang, ZhongTao Yin, ZhuoCheng Hou

“…Four characteristic molecular marker selection methods (Delta, Average Euclidean Distance (AED), Polymorphism Information Content (PIC), and Fixation Index (FST)) and four machine learning classification algorithms (Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB)) were tested. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…We note that coconut tree health issues have been addressed using advanced ML models for early detection and prediction in this paper. Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). …”
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Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning by Banu İsmail Mendi, Hatice Şanlı, Mert Akın Insel, Beliz Bayındır Aydemir, Mehmet Fatih Atak

“…For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews by I Gusti Ayu Nandia Lestari, Ni Made Rai Masita Dewi, Komang Gita Meiliana, I Komang Agus Ady Aryanto

“…Therefore, this study develops a sentiment analysis model to identify whether a review contains positive or negative sentiment using machine learning algorithms. The data used to build the model is obtained from user reviews of a film on the IMDb platform. …”
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The classification method of donkey breeds based on SNPs data and machine learning by Dekui Li, Dekui Li, Xiaolong Hu, Yongdong Peng

“…Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models were constructed and evaluated. …”
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TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…Advanced classifiers, including support vector machines and random forests, demonstrated that TKEO effectively improved model accuracy in the capture of fault-related signal dynamics. …”
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A diagnostic model for polycystic ovary syndrome based on machine learning by Cheng Tong, Yue Wu, Zhenchao Zhuang, Ying Yu

“…Ultimately, a total of 8 variables were screened and included in the subsequent model construction, namely LH, LH/FSH, E2, PRL, T, AMH, AD, and COR, with AMH having the highest diagnostic potential among all the variables included in the model. A total of five machine learning models were constructed, the logistic classification model has the best overall performance, and the support vector machine has the weakest overall performance. …”
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Predicting Students’ Performance Using a Hybrid Machine Learning Approach by Ropafadzo Duwati, Tawanda Mudawarima

“…The Linear Support Vector Classifier (SVC) captured linear patterns within the data, and Logistic Regression was employed as a meta-learner to make the final predictions. …”
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A survey: Breast Cancer Classification by Using Machine Learning Techniques by Ruaa Hassan Mohammed Ameen, Nasseer Moyasser Basheer, Ahmed Khazal Younis

“…The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. …”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…Nine supervised learning algorithms were evaluated and compared, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and several ensemble methods. …”
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