Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

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Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region by Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh, Abiodun Esau Daomi

“…This study employs four ML algorithms—differential evolution (DE), naïve Bayes (NB), random forest (RF), and support vector machines (SVMs)—to develop flood susceptibility models using 16 predictor variables. …”
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Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu, Yong He

“…The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). …”
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Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel by WU Zhong-guang, WU Shun-chuan

“…Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. …”
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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. …”
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The Impact of Image Spatial Resolution and Machine Learning Algorithm on Urban Vegetation Classification: Focus on Data Loss and Misclassification by Alexander Takele Muleta, Julius Bamah, Shirley Bushner, Oz Kira

“…The classification was performed with support vector machine (SVM), random forest (RF), decision tree (DT), and Gaussian Naïve Bayes (GNB) algorithms to examine data loss across resolutions and algorithms. …”
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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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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.) by Jinpeng Wei, Zhangyan Dai, Qi Zhang, Le Yang, Zhaoqi Zeng, Yuliang Zhou, Jun Liu, Bingxian Chen

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Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity by Jin-Xia Yang, Jin-Xia Yang, Yue Liu, Yue Liu, Rong Huang, Hai-ying Wu, Ya-yun Wang, Su-ying Cao, Guo-ying Wang, Jian-Min Zhang, Zi-Sheng Ai, Hui-min Zhou

“…Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.ResultsForty-nine children were diagnosed with T2DM during the follow-up period. …”
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Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction by Jing-xian Wang, Chang-ping Li, Zhuang Cui, Yan Liang, Yu-hang Wang, Yu Zhou, Yin Liu, Jing Gao, Jing Gao, Jing Gao, Jing Gao

“…Lasso-Logistic, XGBoost, Random Forest, K-Nearest Neighbor, and Support Vector Machine models were constructed. The prediction performance of the models was compared through AUC, Accuracy, Precision, F1 score, and Brier score. …”
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Fault diagnosis technology for three-level inverter based on ICEEMDAN-FE and SVM by CAO Ruijun, GUO Qiyi

“…In order to improve the accuracy to diagnose complex open-circuit faults for three-level inverters, a new fault diagnosis method of three-level inverters was proposed, combining improved complete ensemble empirical mode decomposition with adaptive noise-fuzzy entropy (ICEEMDAN-FE) and support vector machine (SVM). First, the detection signal is supplied at three-phase load voltage, which was converted into α-β phase voltage by Concordia to reduce the dimension of the eigenvector. …”
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Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms by Marwa Matboli, Marwa Matboli, Hiba S. Al-Amodi, Abdelrahman Khaled, Radwa Khaled, Marwa Ali, Hala F. M. Kamel, Hala F. M. Kamel, Manal S. Abd EL Hamid, Hind A. ELsawi, Eman K. Habib, Eman K. Habib, Ibrahim Youssef

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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Models included Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and XGBoost, assessed via accuracy, precision, recall, F1 score, and AUC. …”
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Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity by Ramon M. Salazar, PhD, Saurabh S. Nair, MS, Alexandra O. Leone, MBS, Ting Xu, PhD, Raymond P. Mumme, BS, Jack D. Duryea, BA, Brian De, MD, Kelsey L. Corrigan, MD, Michael K. Rooney, MD, Matthew S. Ning, MD, Prajnan Das, MD, Emma B. Holliday, MD, Zhongxing Liao, MD, Laurence E. Court, PhD, Joshua S. Niedzielski, PhD

“…Each data set was modeled using 11 commonly employed machine learning algorithms (elastic net, least absolute shrinkage and selection operator [LASSO], random forest, random forest regression, support vector machine, extreme gradient boosting, light gradient boosting machine, k-nearest neighbors, neural network, Bayesian-LASSO, and Bayesian neural network) by randomly dividing the data set into a training and test set. …”
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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.Results: The area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. …”
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Flood-drought shifts monitoring on arid Xinjiang, China using a novel machine learning based algorithm by Sulei Naibi, Anming Bao, Ye Yuan, Jiayu Bao, Rafiq Hamdi, Tao Yu, Xiaoran Huang, Ting Wang, Tao Li, Jingyu Jin, Gang Long, Piet Termonia

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Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

“…We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). …”
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Machine Learning Predictive Models for Survival in Patients with Brain Stroke by Solmaz Norouzi, Samira Ahmadi, Shayeste Alinia, Farshid Farzipoor, Azadeh Shahsavari, Ebrahim Hajizadeh, Mohammad Asghari Jafarabadi

“…Mortality outcomes were modeled using various statistical techniques, including the Cox model, decision trees, random survival forests (RSF), support vector machines (SVM), gradient boosting, and mboost. …”
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Predicting Financial Market Volatility with Modern Model and Traditional Model by R. G. Aldeki

“…The major topic investigates how classical methods (ARCH and GARCH) and well-known machine learning algorithms, support vector regression, and hybrid methods. …”
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Automated Classification of Snow Crab Shell Condition Using Analysis of Field Images by Ibrahim Kadri, Tobie Surette, Sid Ahmed Selouani, Mohsen Ghribi, Ryan LeBlanc

“…The images were gathered from both fishery and scientific survey operations in the southern Gulf of Saint Lawrence in 2023 and 2024. Both the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms were able to reproduce reliable field identifications of five shell conditions (New-soft, New-hard, Intermediate, Old, and Very-old) to an accuracy of 87.6% and 91.7%, respectively. …”
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