Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. …”
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Sentiment Analysis and Classification of User Reviews of the 'Access by KAI' Application Using Machine Learning Methods to Improve Service Quality by Hildegardis Kristina saka, Putri Taqwa Prasetyaningrum

“…User reviews are collected and processed through preprocessing stages, balancing using the SMOTE method, and classified using three machine learning algorithms, namely Support Vector Machine (SVM), Decision Tree, and Logistic Regression. …”
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Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches by Chunhua Gao, Hui Wang

“…Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning-based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…Nine machine learning algorithms (Logistic Regression LR, Decision Tree DT, Gradient Boosting Machine GBM, K-Nearest Neighbors KNN, LASSO, Principal Component Analysis PCA, Random Forest RF, Support Vector Machine SVM, and XGBoost) were applied to training and testing datasets with 10-fold cross-validation to select three optimized algorithm models. …”
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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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Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification by Hai-Long Nguyen, Van Su Pham, Hai-Chau Le

“…Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.…”
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Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics by Li Y, Duan S, Ren S, Li D, Ma Y, Bu D, Liu Y, Li X, Cai X, Zhang L

“…Ultrasomics models were constructed based on the ultrasound image features of the training set using five different ML algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), decision tree (DT), and logistic regression (LR). …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability by Deyan Liu, Yuge Tian, Min Liu, Shangjian Yang

“…Seven machine learning algorithms, including logistic regression, decision tree, random forest, support vector machine, gradient boosting decision tree, k-nearest neighbors, and neural network, were used to develop predictive models. …”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…The performance of five machine learning algorithms (logistic regression, support vector machine, random forest, XGBoost and CatBoost) was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC). …”
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Detection of offensive content in the Kazakh language using machine learning and deep learning approaches by Milana Bolatbek, Moldir Sagynay, Shynar Mussiraliyeva, Zhastay Yeltay

“…The study employs a range of machine learning and deep learning techniques, such as logistic regression, support vector machines (SVM), and long short-term memory (LSTM) networks, to classify destructive content. …”
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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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A framework based on mechanistic modelling and machine learning for soil moisture estimation by Sabri Kanzari, Sana Ben Mariem, Samir Ghannem, Safouane Mouelhi, Hiba Ghazouani, Bechir Ben Nouna

“…These values were used as inputs to the Hydrus 1D model to generate soil water content profiles over a 27-year period. 109 profiles were calculated using machine learning algorithms (regression trees; random forest; support vector machine) to predict soil moisture content from daily values of rainfall and evaporation. …”
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A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Developme... by Xiaojie Jin, Yanru Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, Jintian Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu

“…MethodsThe application of 8 machine learning algorithms (gradient boosting machine [GBM], logistic regression, random forest, extreme gradient boosting [XGB], light gradient boosting machine [LGB], ridge regression, least absolute shrinkage and selection operator, and support vector machine) generated and validated (both internally and externally) a model for differentiating cold and hot syndromes in viral pneumonia, based on clinical data from 1484 patient samples collected at 2 medical centers between 2021 and 2022. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…This is the first study that addresses the challenge of accurately predicting oil production rates by utilizing various advanced machine learning methods including Random Forest, convolutional neural network, support vector machine, multilayer perceptron artificial neural network and ridge regression methods. …”
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Optimal Placement of Wind Power System Using Machine Learning by Abdul Karim, Muhammad Amir Raza, Darakhshan Ara, Muhammad Shahid, Shakir Ali Soomro

“…Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbines using the Pythons algorithms namely, Long Short-Term Memory (LSTM), Prophet (PT), Support Vector Regression (SVR), Linear Regression (LR), Seasonal Autoregressive Integrated Moving Average with External Factors (SARIMAX), and K-Nearest Neighbors (KNN). …”
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A data driven predictive viscosity model for the microemulsion phase by Akash Talapatra, Bahareh Nojabaei, Pooya Khodaparast

“…This study aims to compare the accuracy and correlation coefficients of these models, selecting the most precise model for predicting microemulsion phase viscosity under diverse reservoir conditions. Support Vector Regression (SVR) outperformed other models with an R2 of 0.978 and 0.963 and mean absolute errors of 0.059 and 0.072 for training and test datasets, respectively. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The six classical machine algorithms including Classified Regression Tree (CART), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Stepwise Regression (STR) and Gradient Boosting Machine (GBM) were used for training. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…We compared EMIT with Sentinel-2 (S2) multispectral data as model inputs, with and without GEDI data integration, using five Machine Learning (ML) algorithms: Partial Least Squares Regression (PLSR), Boosted Regression Trees (BRT), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR). …”
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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. …”
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