Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps by Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang, Yanan Sun

“…The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). …”
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Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI by Xueming Xia, Wei Du, Qiheng Gou

“…In the training dataset, the top-performing classifiers were the XGBoost, LightGBM, support vector machine (SVM) and random forest models, which achieved AUC of 0.963, 0.881, 0.876 and 0.855, respectively, with 5-fold cross-validation. …”
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AI-Based Breast Cancer Detection System: Deep Learning and Machine Learning Approaches for Ultrasound Image Analysis by Amro Moursi, Abdulrahman Aboumadi, Uvais Qidwai

“…By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. …”
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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

“…This study employs a dataset comprising gas sensor measurements that encompassing gasses, such as Liquid Petroleum Gas (LPG), Compressed Natural Gas (CNG), Methane, Propane, and others. Various machine learning classifiers, including K-Nearest Neighbors, Decision Tree, Support Vector Machines, XGBoost, and others, are compared with ExAIRFC-GSDC for gas detection. …”
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Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

“…The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. …”
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A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats by Dheyaaldin Alsalman

“…In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. …”
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Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients by Jae Hun Chung, Jae Hun Chung, Jae Hun Chung, Yushin Kim, Dongjun Lee, Dongwon Lim, Dongwon Lim, Dongwon Lim, Sun-Hwi Hwang, Sun-Hwi Hwang, Sun-Hwi Hwang, Si-Hak Lee, Si-Hak Lee, Si-Hak Lee, Woohwan Jung

“…One hundred eighty-nine features were extracted from each patient record, including demographic data, preoperative comorbidities, and blood test outcomes from the subsequent seven postoperative days (POD). Six ML algorithms were evaluated: Logistic Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), and Neural Network (NN). …”
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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

“…In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. …”
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Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique by Fatin Nabihah Jais, Mohd Zulfaezal Che Azemin, Mohd Radzi Hilmi, Mohd Izzuddin Mohd Tamrin, Khairidzan Mohd Kamal

“…The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). …”
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Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China by Yiyang Li, Gang Yao, Shuangyi Li, Xiuru Dong

“…It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting machine (GBM), and generalized linear model (GLM) were used to study the accurate prediction model of SOM in Tieling County, Tieling City, Liaoning Province, China. …”
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Evaluation of machine learning methods for forecasting turbidity in river networks using Sentinel-2 remote sensing data by Victor Oliveira Santos, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi

“…Spectral bands from Sentinel-2 were analyzed using machine learning algorithms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations across the Mississippi River, USA. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. …”
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Structural Damage Detection in the Wooden Bridge Using the Fourier Decomposition, Time Series Modeling and Machine Learning Methods by Younes Nouri, Farzad Shahabian, Hashem Shariatmadar, Alireza Entezami

“…To ascertain the presence of damage, supervised classification machine learning algorithms are employed. The algorithms are utilized consist of Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), support vector machines (SVM), ensemble learning, and decision tree. …”
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GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data by Muhammad Iqbal, Arshad Iqbal, Humaira Ayub, Maqbool Khan, Naveed Ahmad, Yasir Javed, Mohammed Ali Alshara

“…This research used Next Generation Sequencing (NGS) whole-exome data to create a categorization framework using machine learning methods. This study specifically investigated the effectiveness of decision tree, random forests, and support vector classification (SVC) algorithms in distinguishing different glaucoma genotypes. …”
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Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection by Tuğçe Öznacar, Tunç Güler

“…Methods: This study included both patients with and without ovarian cancer, creating a dataset containing 50 independent variables/features. Eight machine learning algorithms: Random Forest, XGBoost, CatBoost, Decision Tree, K-Nearest Neighbors, Naive Bayes, Gradient Boosting, and Support Vector Machine, were evaluated alongside four feature selection techniques: Boruta, PCA, RFE, and MI. …”
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Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting by Türker Tuğrul, Sertaç Oruç, Mehmet Ali Hınıs

“…Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. …”
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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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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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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Classification and Prediction of Liver Disease in Patients with Hepatitis C by Shalem Preetham Gandu, M. Roshni Thanka, E. Bijolin Edwin, Ebenezer Veemaraj, S. Stewart Kirubakaran

“…The classifiers used in model assessment included K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LGR), XGBoost (XGB), and Gaussian Naive Bayes (GNB). …”
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