Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…This study developed an advanced bankruptcy prediction model using Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms based on datasets from the UCI machine learning repository. …”
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Adaptive convolutional neural network-based principal component analysis algorithm for the detection of manufacturing data by Tsun-Kuo Lin

“…The mentioned algorithm adaptively selects a suitable classification scheme (a CNN-based scheme or PCA-based support vector machine scheme) on the basis of various types of inputs to detect manufacturing data. …”
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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. …”
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A machine learning approach to predict phyllosphere resistome abundance across urbanization gradients by Rui-Ao Ma, Yi-Hui Ding, Shifa Zhong, Ting-Ting Jing, Xuechu Chen, Si-Yu Zhang

“…Among the five tested algorithms tested in the machine learning models (ridge regression, K-nearest neighbor, support vector machine, and neural network), the random forest algorithm achieved the highest accuracy with the lowest root mean square error (27.24 vs. 40.79–46.79 for the other models). …”
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Exploring the application of machine learning and SHAP explanations to predict health facility deliveries in Somalia by Jamilu Sani, Salad Halane, Mohamed Mustaf Ahmed, Abdiwali Mohamed Ahmed, Jamal Hassan Mohamoud

“…Methods This study analyzed data from the 2020 Somalia Demographic and Health Survey (SDHS) involving 8,951 women aged 15–49 years. Seven ML algorithms, Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbors, were evaluated for their ability to predict health facility deliveries. …”
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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

“…Experiments were performed to compare the effectiveness of Minimum Redundancy Maximum Relevance for feature selection with Principal Component Analysis for feature reduction. 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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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Feature selection was performed in two steps: first using univariate logistic regression, followed by multivariate logistic regression with a stringent significance threshold (p < 0.05). We utilized six machine learning algorithms—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—to construct predictive models. …”
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Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection by Alexey Moskovchenko, Michal Svantner

“…Other combinations, such as Gaussian support vector machine model with raw data and K-nearest neighbor with thermographic signal reconstruction derivative data, also exhibited promising performances.…”
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Machine learning applications for chloride ingress prediction in concrete: insights from recent literature by Quynh-Chau Truong, Anh-Thu Nguyen Vu

“…This review explores recent ML advancements in assessing corrosion in RC structures. Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

“…To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. …”
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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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Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…The models included Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Support Vector Classifier (SVC). We evaluated these models i.e., based on the evaluation and the key performance metrics. …”
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MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE ANALYSIS FOR APPLE LEAF DISEASE DETECTION by Anupam Bonkra, Sunil Pathak, Amandeep Kaur

“…This work employs five classification algorithms Inception V3, Decision Tree, Support Vector Machine (SVM), and Random Forest to create a model for detecting diseases on apple leaves. …”
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Classifying software security requirements into confidentiality, integrity, and availability using machine learning approaches by Taghreed Bagies

“…For both techniques, we developed five models by using five well-known machine learning algorithms: (1) support vector machine (SVM), (2) K-nearest neighbors (KNN), (3) Random Forest (RF), (4) gradient boosting (GB), and (5) Bernoulli Naive Bayes (BNB). …”
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Exploring a cost-effective way for nutrient management with machine learning for container plants by Ping Yu, Kuan Qin

“…By training the data using K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes algorithms, this system could reach up to 0.7–1.0 accuracy in guiding fertilization schedules during the plant vegetative growth stage. …”
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Signal-piloted processing and machine learning based efficient power quality disturbances recognition. by Saeed Mian Qaisar

“…The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. …”
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Predicting mother and newborn skin-to-skin contact using a machine learning approach by Sanaz Safarzadeh, Nastaran Safavi Ardabili, Mohammadsadegh Vahidi Farashah, Nasibeh Roozbeh, Fatemeh Darsareh

“…A predictive model was built using nine statistical learning models (linear regression, logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). …”
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Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana

“…We utilized a variety of data sources, such as demographic, environmental, and remotely sensing datasets, applying machine learning algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. …”
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Machine Learning Approaches for Data-Driven Self-Diagnosis and Fault Detection in Spacecraft Systems by Enrico Crotti, Andrea Colagrossi

“…Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. …”
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