Predictive analytics in customer behavior: Anticipating trends and preferences by Hamed GhorbanTanhaei, Payam Boozary, Sogand Sheykhan, Maryam Rabiee, Farzam Rahmani, Iman Hosseini

“…In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. …”
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Migraine triggers, phases, and classification using machine learning models by Anusha Reddy, Ajit Reddy

“…In many cases, patients with migraine are often misdiagnosed as regular headaches.MethodsIn this article, we present a study on migraine, covering known triggers, different phases, classification of migraine into different types based on clinical studies, and the use of various machine learning algorithms such as logistic regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) to learn and classify different migraine types. …”
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Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data by Robert Gutierrez, Tianshi Fang, Robert Mainwaring, Tom Reddyhoff

“…Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…After data preprocessing, two primary machine learning algorithms were employed: Support Vector Regression (SVR) and Random Forest (RF), both implemented using Scikit-learn. …”
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Early Prediction Detection of Retail and Corporate Credit Risks Using Machine Learning Algorithms by Mohamed A. Hamada, Karim Farag, Adejor E. Abiche

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Predicting the availability of power line communication nodes using semi-supervised learning algorithms by Kareem Moussa, Khaled Mostafa Elsayed, M. Saeed Darweesh, Abdelmoniem Elbaz, Ahmed Soltan

“…Self-training classifier has been used to allow Light Gradient Boosting Machine (LGBM) and Support Vector Machine (linear and non-linear kernel) to behave in a self-training manner as well as the training of label propagation and label spreading algorithms. …”
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Integrated MCDM framework for sustainable pharmacy supplier selection using pioneering criteria with fuzzy TOPSIS SVR and GRA by R. Raveena, S. Umamaheswari

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Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…With the following supervised machine learning algorithms: Random Forest, Artificial Neural Network (ANN) and Support Vector Regression (SVR); the study modeled RFC. …”
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An exploration of machine learning approaches for early Autism Spectrum Disorder detection by Nawshin Haque, Tania Islam, Md Erfan

“…Logistic Regression, Random Forest, Support Vector Classifier, and Decision Tree attained 100% accuracy and mIoU with the real-world dataset. …”
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Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning by Dthenifer Cordeiro Santana, Rafael Felipe Ratke, Fabio Luiz Zanatta, Cid Naudi Silva Campos, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Natielly Pereira da Silva, Gabriela Souza Oliveira, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Matildes Blanco, Paulo Eduardo Teodoro

“…Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. …”
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Efficient Short-Term Wind Power Prediction Using a Novel Hybrid Machine Learning Model: LOFVT-OVMD-INGO-LSSVR by Zhouning Wei, Duo Zhao

“…The improved NGO algorithm was used to optimize the least squares support vector regression (LSSVR) prediction model to improve the computational speed and prediction results. …”
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Environmental Factors in Structural Health Monitoring—Analysis and Removal of Effects from Resonance Frequencies by Rims Janeliukstis, Lasma Ratnika, Liga Gaile, Sandris Rucevskis

“…It is based on the identification of resonant frequencies from operational modal analysis, removing the effect of environmental factors on the resonant frequencies through support vector regression with optimized hyperparameters and, finally, classifying the global structural state as either healthy or damaged, utilizing the Mahalanobis distance. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
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Methods of Machine-Aided Training in Small Business: Content and Management by S. A. Tishchenko, M. A. Shakhmuradian

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Application of machine learning algorithms in predicting pyrolytic analysis result by Thi Nhut Suong Le, A. V. Bondarev, L. I. Bondareva, A. S. Monakova, A. V. Barshin

“…To develop the prediction model, 5 different machine learning regression algorithms were applied and compared, including multiple linear regression, polynomial regression, support vector regression, decision tree, and random forest.Results. …”
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. …”
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Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine by M. Etebarian, k. movagharnejad

“…Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm by Ling Li, Tao Yue, Hui Wu, Yanping Zhao, Qinmei Liu, Hairong Zhang, Wei Xu

“…This article presents a novel classification system with a proposed feature selection method for assessing neonatal brain injury, in which the feature selection method is combined using elastic net (EN) regression and an improved crow search algorithm (ICSA), named EN-ICSA. …”
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