Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data by Sadullah Çelik, Bilge Doğanlı, Mahmut Ünsal Şaşmaz, Ulas Akkucuk

“…This study aims to compare the accuracy performances of different machine learning algorithms (Logistic Regression, Decision Tree, Support Vector Machines (SVMs), Random Forest, Artificial Neural Network, and XGBoost) using World Happiness Index data. …”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. …”
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Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

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Data Mining Approaches in Predicting Entrepreneurial Intentions Based on Internet Marketing Applications by Milan Krivokuća, Mihalj Bakator, Dragan Ćoćkalo, Marijana Vidas-Bubanja, Vesna Makitan, Luka Djordjević, Borivoj Novaković, Stefan Ugrinov

“…Furthermore, a supervised machine learning algorithm, support vector machine (SVM) was used. Finally, a feed-forward neural network (FNN) was applied. …”
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Dual-Language Sentiment Analysis: A Comprehensive Evaluating SVM, Logistic Regression, XGBoost, and Decision Tree Using TF-IDF On Arabic and English Dataset by Hawraa Ali Taher

“…This search helps the user to access the evaluation of other users through their tweets and comments on the social networking site for an opinion immediately and automatically, and then the process of uploading and evaluating the opinions using appropriate algorithms for this purpose as(Decision Tree classifier DTC , XGboost, Logistic Regression LR, Support Vector Machine SVM )with Term Frequency-Inverse Document Frequency TF_IDF …”
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Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

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Machine learning algorithms for prediction of cerebrospinal fluid leakage after posterior surgery for thoracic ossification of the ligamentum flavum by Ruizhou Guo, Ben Liu, Yunqi Wu, Yilu Zhang, Xiyang Wang, Dingyu Jiang, Zheng Liu

“…A baseline logistic-regression (LR) model and four ML algorithms—XGBoost, Random Forest, LightGBM and Support Vector Machine (SVM)—were tuned via Bayesian optimisation. …”
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A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features by Nurhan Gürsel Özmen, Neslihan Baki

“…The performance of the PD and control group was evaluated with Gradient Boosting (GB), Gaussian Naive Bayes (GNB), and K-nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) Algorithms. …”
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Machine learning approach for optimizing usability of healthcare websites by Amandeep Kaur, Jaswinder Singh, Satinder Kaur

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Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days by Yilun XU, Binqiao ZHANG, Jing HUANG, Xiao XIE, Ruoxin WANG, Danqing SHEN, Lina HE, Kaifan YANG

“…The least squares support vector machine (lSSVM) was optimized by IWPA, and an IWPA-LSSVM based photovoltaic power prediction model was established considering weather types and similar days. …”
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Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…The experiment was based on the collection and transcription of a database of thousand soybean seed analysis samples containing information on germination and tetrazolium tests (vigor and viability). The algorithms tested were REPTree, M5P, random forest, logistic regression, artificial neural networks and support vector machine, and the inputs tested were viability, vigor and vigor + viability (tetrazolium test) data. …”
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Medium and Long-Term Hydrogen Load Forecast for Unified Energy System by Shengjiang PENG, Chuanshuai SUN, Jianjun TUO, Tiejiang YUAN

“…Firstly, on the basis of the hydrogen load sample data from the industrial field, the characteristics of the load data are calculated and the support vector machine regression (SVR) algorithm is applied to set up the hydrogen load forecast model accordingly. …”
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Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique by Gui-Lin Hu, Chen-Xi Quan, Hao-Peng Dai, Ming-Hua Qiu

“…The 1H NMR from water-soluble content was shown to be more effective than that of oil fraction for qualitative of DCB blends, regardless of whether partial least squares discriminant analysis (PLS-DA) or machine learning (ML) algorithms were used. Support vector machine (SVM) was proved to be excellent for distinguishing DCB blends. …”
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Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morpholog... by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

“…However, to accurately predict the wear rate at high temperatures, six different machine learning regression algorithms were used, namely Support Vector Machine (SVM), Linear Regression (LR), Random Forest Regression (RFR), Gaussian Process Regression (GPR), XGBoost regression (XGB) and Decision Tree (DT). …”
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Quadratic Regression Models for Profile Picture NFT Valuation by Geun-Cheol Lee, Hoon-Young Koo, Heejung Lee

“…For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…A particle swarm optimization–support vector machine (PSO-SVM) model was then developed to predict the crushing force based on fertilizer shape features. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…The prediction models were stepwise, linear regression, nonlinear regression, k-nearest neighbor regression, random forest regression, and support vector machines. …”
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Machine Learning Techniques for Classification of Stress Levels in Traffic by Amanda Trojan Fenerich, Egídio José Romanelli, Rodrigo Eduardo Catai, Maria Teresinha Arns Steiner

“…The classification algorithms used were Support Vector Machine (SVM), Bayesian Networks (BN), and Logistic Regression (LR), comparatively. …”
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Supervised methods of machine learning for email classification: a literature survey by Muath AlShaikh, Yasser Alrajeh, Sultan Alamri, Suhib Melhem, Ahmed Abu-Khadrah

“…Supervised learning requires pre-training the model on labelled datasets, amalgamating classification, and regression learning. Notably, supervised methodologies such as support vector machines (SVMs), naive Bayes, decision trees, neural networks, random forests, and deep learning have been exploited for spam filtering. …”
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Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete by AGRAWAL Achal, CHANDAK Narayan

“…The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. …”
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