Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques by Dina Wulan Yekti rahayu, Khothibul Umam, Maya Rini Handayani

“…This study explores the performance of five sentiment classification algorithms—Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest—on an imbalanced sentiment dataset, with the SMOTE technique applied as a comparison. …”
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Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction by Yuchi Li, Zhigao Wang, Aiwei Yang, Xiaoqi Yu

“…For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. …”
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A classifier-assisted evolutionary algorithm with knowledge transfer for expensive multitasking problems by Min Hu, Zhigang Ren, Zhirui Cao, Yifeng Guo, Haitao Sun, Hongyao Zhou, Yu Guo

“…To be specific, a support vector classifier (SVC) is first developed and integrated into a classic evolutionary algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES). …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. …”
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Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms by Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani, Sultan Aljahdali

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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…This study adopts a novel approach by utilizing hybrid models, which offer greater accuracy than individual or ensemble ML models. Specifically, support vector regression (SVR) was combined with three optimization algorithms: firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO) to create hybrid models for estimating the SE of carbon fiber-reinforced mortars. …”
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Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms by Xiaohua Wan, Ruihuan Zhang, Yanan Wang, Wei Wei, Biao Song, Lin Zhang, Yanwei Hu

“…Using 39 optimal variables, a prediction model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and compared with four other algorithms: support vector machine (SVM), gradient boosting decision tree (GBDT), neural network (NN), and logistic regression (LR). …”
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Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms by Haobo Kong, Yong Li, Ya Shen, Jingjing Pan, Min Liang, Zhi Geng, Yanbei Zhang

“…Five machine learning algorithms, logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM), were utilized to construct the predictive models. …”
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Comparative analysis of machine learning algorithms for predicting depression among individuals with diabetes by Hind Bourkhime, Noura Qarmiche, Soumaya Benmaamar, Nada Lazar, Mohammed Omari, Mohamed Berraho, Nabil Tachfouti, Samira El Fakir, Hanan El Ouahabi, Nada Otmani

“…The algorithms evaluated include logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), Adaptive Boosting (AdaBoost), support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Methodology The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. …”
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

“…In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…Therefore, when processing Internet marketing node detection tasks, the K-means algorithm is used to regress the training set and calculate 5 weights. …”
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Prediction of contact resistance of electrical contact wear using different machine learning algorithms by Zhen-bing Cai, Chun-lin Li, Lei You, Xu-dong Chen, Li-ping He, Zhong-qing Cao, Zhi-nan Zhang

“…Random forest (RF), support vector regression (SVR) and BP neural network (BPNN) algorithms were used to establish RF, SVR and BPNN models, respectively, and the experimental data were trained and tested. …”
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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Additionally, machine learning algorithms such as artificial neural networks, support vector machines, regression trees, ensemble of regression trees, Gaussian process regression, efficient linear regression, and kernel methods are employed to predict power reduction due to soiling and soiling losses across various soiling percentages. …”
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Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis by Jingtai Ma, Jingtai Ma, Yiting Fang, Yiting Fang, Shiqi Li, Shiqi Li, Lilian Zeng, Siyi Chen, Zhifeng Li, Guiyuan Ji, Xingfen Yang, Wei Wu, Wei Wu

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A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm by Yingying Lian, Dongdong Qiao

“…In this paper, we propose multiple machine learning algorithms to estimate the capacity using the incremental capacity (IC) curve features, including the adaptive moment estimation (Adam) model, root mean square propagation (RMSprop) model, and support vector regression (SVR) model. …”
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Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture by Mustafa Mayyahi, Jordi Mongay Batalla, Constandinos X. Mavromoustakis

“…By leveraging the accuracy of the multiple signal classification (MUSIC) algorithm combined with predictive linear regression (LR) and support vector regression (SVR) models, our approach significantly enhances the MUSIC algorithm and accelerates the generation of beam weights for the beamforming system. …”
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Research on Tool Wear Monitoring Based on ET-GD and K-nearest Neighbor Algorithm by QIN Yiyuan, LIU Xianli, YUE Caixu, GUO Bin, DING Mingna

“…The optimized features are used to train logical regression extreme random tree support vector regression and K-nearest neighbor algorithm models and verified by ten fold cross validation method and test set. …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…These data were collected from the Urban Resilience Project of Rajdhani Unnayan Kartripakkha (RAJUK), which is the development authority of Dhaka. Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. …”
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Distributed denial-of-service (DDOS) attack detection using supervised machine learning algorithms by S. Abiramasundari, V. Ramaswamy

“…Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbours (KNN), Decision Tree (DT) supervised models, and Principle Component Analysis (PCA) feature selection method are used to differentiate between attack and regular traffic. …”
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