Distributed denial-of-service (DDOS) attack detection using supervised machine learning algorithms by S. Abiramasundari, V. Ramaswamy

“…Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbours (KNN), Decision Tree (DT) supervised models, and Principle Component Analysis (PCA) feature selection method are used to differentiate between attack and regular traffic. …”
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Pregnancy probability prediction models based on 5 machine learning algorithms and comparison of their performance by REN Chao, REN Chao, YANG Huan, ZHOU Niya, ZHOU Niya

“…In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions, feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set. Five algorithms, that is, Logistic Regression, Naive Bayes, Random Forest, Gradient Boosting Machine, and Support Vector Machine, were used to construct preconception outcome prediction models, and the parameters of each model were optimized using random search combined with grid search. …”
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Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health by Qiang Zhang, Diandong Lian, Yiqiao Zhang

“…ObjectiveAn analysis was conducted on the impact of the body on athletes’ emotions and motivation from the perspective of Public Health (PH).MethodsPSO-KNN (Particle Swarm Optimization-K-Nearest Neighbor) algorithm and PSO-SVM algorithm (Particle Swarm Optimization-Support Vector Machine) were obtained by combining Particle Swarm Optimization (PSO), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), and then the recognition rates of the two algorithms were compared.ResultsWhen comparing the PSO-KNN algorithm and PSO-SVM algorithm on baseline removed and baseline not removed, the average recognition rates of PSO-KNN algorithm and PSO-SVM algorithm under emotional state were 56.66 and 54.75%, respectively. …”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

“…In our experimental setup, 80% of the dataset was allocated for training and 20% for testing. The baseline Support Vector Machine (SVR) model achieved a mean squared error (MSE) of 0.00513, which was reduced to 0.00011 after applying binary NiOA (bNiOA) for feature selection. …”
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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

“…The proposed supervised machine learning algorithms comprise support vector machines, random forest, boosting, and long short-term memory (LSTM) recurrent neural network types. …”
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Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification by Tanuja Subba, Tejbanta Chingtham

“…This research presents an investigation into the classification of ECG signals using various Machine Learning (ML) methods. Specifically, the performance of Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms are examined. …”
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Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach by Muhammad Kunta Biddinika, Alya Masitha, Herman Herman, Vita Arfiana Nurul Fatimah

“…Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). …”
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Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms by Shanping Shi, Chao Huang, Xiaojian Tang, Hua Liu, Weiwei Feng, Chen Chen

“…By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), the key feature gene USP14 was identified. …”
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Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms by Shikha Sharda, Sumit Kumar, Raj Setia, Prince Dhiman, N. R. Patel, Brijendra Pateriya, Ali Salem, Ahmed Elbeltagi

“…The normalized difference vegetation index (NDVI) was computed during this period followed by implementation of random forest (RF), decision tree (DT), and support vector machine (SVM) algorithms to evaluate their performance for wheat classification. …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. …”
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Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample by Kupidura Przemysław, Kępa Agnieszka, Krawczyk Piotr

“…The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. …”
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Iris recognition based on generalized Gaussian distribution FDCT_Wrap and FSVM by Zhenhong HE

“…In order to improve the accuracy rate of iris recognition,an improved curvelet transform algorithm for iris recognition was proposed.Firstly,the iris image was decomposed with fast discrete curvelet transform by wrapping algorithm.Mean variance and energy of curvelet sub-band coefficients in different scales and different orientations were extracted.The weights of sub-bands were estimated by generalized Gaussian distribution.The feature vectors with stronger classification ability had large weight,which were calculated to constitute feature vectors of iris image.Finally,feature vectors were matched and recognized by classifier combined with fuzzy support vector machine and binary decision tree.The algorithm performances were tested with UBIRIS and CASIA iris database.Simulation results show that the proposed algorithm has higher recognition accuracy rate and efficiency.It is feasibility.…”
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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Therefore, this study investigates the effect of soiling (from 1% to 5%) on electrical parameters (open circuit voltage and short circuit current), photovoltaic panel characteristics (cell temperature and module efficiency), and environmental variables (wind speed and irradiance) in the United Arab Emirates based Noor Abu Dhabi Solar Project. Additionally, machine learning algorithms such as artificial neural networks, support vector machines, regression trees, ensemble of regression trees, Gaussian process regression, efficient linear regression, and kernel methods are employed to predict power reduction due to soiling and soiling losses across various soiling percentages. …”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. …”
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Automated Detection of Aberrant Episodes in Epileptic Conditions: Leveraging EEG and Machine Learning Algorithms by Uddipan Hazarika, Bidyut Bikash Borah, Soumik Roy, Manob Jyoti Saikia

“…Our approach creates and evaluates support vector machine, random forest classifier, and long short-term memory network machine learning models to classify seizures using EEG inputs. …”
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Predictive analytics in customer behavior: Anticipating trends and preferences by Hamed GhorbanTanhaei, Payam Boozary, Sogand Sheykhan, Maryam Rabiee, Farzam Rahmani, Iman Hosseini

“…In the current work, various machine learning algorithms such as Decision Tree (DT), Random Forest (RT), Logistic Regression (LR), Support Vector Machines (SVM), and gradient boosting are used to predict customer behavior. …”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. …”
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Prediction of Optimum Operating Parameters to Enhance the Performance of PEMFC Using Machine Learning Algorithms by Arunadevi M, Karthikeyan B, Anirudh Shrihari, Saravanan S, Sundararaju K, R Palanisamy, Mohamed Awad, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Hany S. Hussein, Mahmoud M. Hussein, Ahmed I. Omar

“…Different MLAs are modelled to explore the PEMFC performance and results proved that gradient boosting regression provides better predictions compared to other algorithms such as decision tree regressor, support vector machine regressor, and random forest regression.…”
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Diagnosing schizophrenia with routine blood tests: a comparative analysis of machine learning algorithms by Yavuz Selim Ogur, Ayse Erdogan Kaya, Nur Banu Ogur, Beyza Erdogan Akturk

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Voice pathology detection using machine learning algorithms based on different voice databases by Nurul Mu'azzah Abdul Latiff, Fahad Taha Al-Dhief, Nurul Fariesya Suhaila Md Sazihan, Marina Mat Baki, Nik Noordini Nik Abd. Malik, Musatafa Abbas Abbood Albadr, Ali Hashim Abbas

“…Several algorithms, including Online Sequential Extreme Learning Machine (OSELM), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB), were evaluated using two databases: the Saarbrucken Voice Database (SVD) and the Malaysian Voice Pathology Database (MVPD). …”
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