Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images by Asraa M. Mohammad, Hussien Attia, Yossra H. Ali

“…This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). …”
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Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm by Di Leng, Ziliang Qiu

“…On this basis, the power system state estimation model established by particle swarm optimization support vector machines is used to judge the operational state of the power system. …”
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Dual-feature speech emotion recognition fusion algorithm based on wavelet scattering transform and MFCC by YING Na, WU Shunpeng, YANG Meng, ZOU Yujian

“…Then, the wavelet scattering features were expanded in the scale dimension and applied support vector machines to obtain posterior probabilities for emotion recognition. …”
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New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm by Chaofa Bian, Kefei Zhang, Yunzhao Wu, Suqin Wu, Yu Lu, Yabo Duan, Huajing Wu, Zhenxing Zhao, Wei Wu

“…Among the models tested, the SXL algorithm (stacking of support vector machine regression, extreme gradient boosting, and linear regression), which was selected from a stack of 2 or 3 out of six typical algorithms, achieved the highest inversion accuracy. …”
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Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine by ZHANG Meng, LI Guanghui

“…Then, based on full wavebands and characteristic wavebands, an extreme learning machine (ELM) model was built, as comparison with support vector machine (SVM) and K- mean algorithm. …”
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Electric Submersible Pump Fault Diagnosis Based on Laplacian Eigenmaps and Weighted Extreme Learning Machine by XU Zekun, FU Jun, GAO Xiaoyong, ZHANG Yu, LI Qiang, TAN Chaodong

“…The results show that the classification average accuracy, maximum accuracy, and G-mean of the algorithm proposed in this paper are improved by more than 10% on average compared with those of the support vector machine, decision tree, backpropagation (BP) algorithm, extreme learning machine, and weighted extreme learning machine, thus confirming the effectiveness of the proposed method.…”
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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

“…As understanding reasoning behind their predictions is still a great challenge for the healthcare professionals and raised a great concern about their trustworthiness, interpretability and transparency in clinical settings. Thus, an advanced algorithm of explainable artificial intelligence (XAI) has been synergized with hybrid model comprising of DenseNet201 network for extracting the most important features based on the input Magnetic resonance imaging (MRI) data following supervised algorithm, support vector machine (SVM) to distinguish distinct types of brain scans. …”
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Facial recognition and analysis: A machine learning-based pathway to corporate mental health management by Zicheng Zhang, Tianshu Zhang, Jie Yang

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Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety by Abhishek Kumar Pathak, Ankit Kumar Singh, Pankaj Kumar, Vimal Bhatia, Vimal Bhatia, Vimal Bhatia, Ondrej Krejcar, Ondrej Krejcar

“…The proposed method outperforms the traditional approaches such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Haar Cascade Classifiers, and other DL architectures like Xception and VGG16, in terms of accuracy and efficiency. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…Methods This retrospective study included 110 children with CSD. We developed four machine learning models (logistic regression, support vector machine, decision tree, and extreme gradient boosting [XGBoost]), and compared their predictive performances. …”
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Development of a machine learning-based surrogate model for friction prediction in textured journal bearings by Yujun Wang, Georg Jacobs, Shuo Zhang, Benjamin Klinghart, Florian König

“…Furthermore, three ML methods are trained and compared to select the most suitable prediction method: artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GPR). …”
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Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors by Fernando Rodriguez, Philipp Krauss, Jonas Kluckert, Franziska Ryser, Lennart Stieglitz, Christian Baumann, Roger Gassert, Lukas Imbach, Oliver Bichsel

“…Based on features extracted from the sensor data, a Support Vector Machine was trained to predict tremor severity. …”
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Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach by Mariam Kili Bechir, Ferhat Atasoy

“…We evaluated seven distinct algorithms: Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbors (k-NN), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), Random Forests (RF), and a basic "nonlinear mapping technique". …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…For performance verification, four alternative predictive models were established, including LDA–ANN, support vector machines (SVM), Particle Swarm Optimization (PSO), and a GA-tuned BA–ANN. …”
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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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View-invariant object representation in anterior and posterior inferotemporal cortex: A machine learning approach by Jun-ya Okamura, Daisuke Fukano, Keisuke Murakami, Gang Wang

“…A classifier was trained by support vector machine (SVM) to create a hyperplane that separated one object from the other three objects at the same viewing angles, and then tested by response vectors to the object images at different viewing angles. …”
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Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

“…Abstract Machine learning has become an essential component across various domains, including the education sector. …”
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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. …”
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Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods by Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof S. Pieczarka, Tomasz Wojciechowski

“…These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. …”
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