Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT. by Yasser Filahi, Omer Melih Gul, Ali Elghirani, Erkut Arican, Ismail Burak Parlak, Seifedine Kadry, Kostas Karpouzis

“…We applied several ML techniques, including logistic regression, Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), AdaBoosting, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). …”
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Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

“…Prediction models for 5-year OS in patients with HCC were established by logistic regression (LR), support vector machine (SVM), decision tree classification (DTC), random forests (RF), and extreme gradient Boosting (XGBoost), respectively. …”
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LncRNAs regulates cell death in osteosarcoma by Ping’an Zou, Zhiwei Tao, Zhengxu Yang, Tao Xiong, Zhi Deng, Qincan Chen, Li Niu

“…Three machine learning algorithms—Support Vector Machine, Random Forest, and Generalized Linear Model—were utilized to select feature genes. …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…Relevant indices of TACE refractoriness were selected. ML algorithms, including a support vector machine, random forest, logistic regression and adaptive boosting, were used to construct the radiomics, clinical prediction, and combined models. …”
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Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremit... by Jiawen Deng, BHSc, Myron Moskalyk, BHSc, MSc, Madhur Nayan, MD, PhD, Ahmed Aoude, MEng, MD, FRCSC, Michelle Ghert, MD, FRCSC, Sahir Bhatnagar, PhD, Anthony Bozzo, MSc, MD, FRCSC

“…Candidate features were selected based on availability and clinical relevance and then narrowed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Boruta algorithms. Six ML classification algorithms were tuned and calibrated: logistic regression, support vector machines, random forest, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and neural networks. …”
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AI-Based model for site-selecting earthquake emergency shelters by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…To address this, machine learning can enhance the speed and accuracy of shelter site selection; the results can also be generalized to other regions. Support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), gaussian processes classifier (GPC), and artificial neural network (ANN) methods are used to develop the model here. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…This study presents a robust machine learning framework to predict the CS of SFRC using a large-scale experimental dataset comprising 600 data points, encompassing key parameters such as fiber characteristics (type, content, length, diameter), water-to-cement (w/c) ratio, aggregate size, curing time, silica fume, and superplasticizer. Six advanced regression-based algorithms, including support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), extreme gradient boosting regression (XGBR), artificial neural networks (ANN), and K-nearest neighbors (KNN), were benchmarked through rigorous model validation processes including hold-out testing, K-fold cross-validation, sensitivity analysis, and external validation with unseen experimental data. …”
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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

“…Six machine learning algorithms—Decision Trees, k-nearest neighbors, Gaussian Support Vector Machine, Neural Network, Support Vector Machines kernel, and Logistic Regression Kernel—were evaluated for both binary and multi-class classification using feature sets of 4, 12, 23, 50, and 79 features. …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. …”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Five advanced machine learning (ML) algorithms, logistic regression, support vector machine, decision tree, random forest, and gradient boosting are employed to develop predictive models. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…The following machine learning models were used for the study: RandomForests (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and AdaBoost (AB), as well as the 10-time cross-validation technique. …”
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Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study by Yongsheng Zhang, Hongyu Zhang, Dawei Wang, Na Li, Haoyue Lv, Guang Zhang

“…Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, random forest, support vector machine, multilayer perceptron, extreme gradient boosting machine, light gradient boosting machine, and categorical boosting machine [CatBoost]), optimized through grid search and 5-fold cross-validation. …”
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Machine learning-driven discovery of anoikis-related biomarkers in Adult T-Cell Leukemia/Lymphoma subtypes by Mohadeseh Zarei Ghobadi, Elaheh Afsaneh

“…Subsequently, we employed decision trees, random forest, extreme gradient boosting, support vector machine, and logistic regression algorithms to identify classifier genes distinguishing each ATLL subtype from asymptomatic carriers. …”
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Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…A validated CFD model generated 935 numerical cases across diverse operational and design parameters, which were used to train and evaluate three machine learning algorithms: linear regression (LR), support vector regression (SVR), and artificial neural networks (ANN). …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. …”
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A Machine Learning Framework for Student Retention Policy Development: A Case Study by Sidika Hoca, Nazife Dimililer

“…For the case study, various machine learning algorithms—including Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Artificial Neural Network, Random Forest, Classification and Regression Trees, and Categorical Boosting—were trained for dropout prediction using data available at the end of the students’ second semester. …”
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Predicting adherence to gamified cognitive training using early phase game performance data: Towards a just-in-time adherence promotion strategy. by Yuanying Pang, Ankita Singh, Shayok Chakraborty, Neil Charness, Walter R Boot, Zhe He

“…<h4>Research design and method</h4>Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. …”
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Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data by Yuhua Zhong

“…To evaluate model performance, a diverse set of machine learning algorithms, including support vector machines, Naive Bayes, k-nearest neighbors, logistic regression, multi-layer perceptron with contrastive loss, and XGBoost are employed, with metrics such as Accuracy, F1 Score, Recall, Precision, and AUROC used for comparison. …”
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Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model by Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang

“…We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. …”
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