Prediction of high-risk pregnancy based on machine learning algorithms by Xinyu Pi, Junzhi Wang, Liangliang Chu, Guochun Zhang, Wenli Zhang

“…The study is based on the maternal health risk dataset (MHRD) from Bangladesh, covering multiple hospitals, community clinics, and maternal healthcare centers, and encompassing health data from 1014 pregnant women. Six machine learning algorithms—multilayer perceptron (MLP), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM)—are employed to construct predictive models. …”
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Comparative analysis of machine learning algorithms for money laundering detection by Sunday Adeola Ajagbe, Simphiwe Majola, Pragasen Mudali

“…This research examined contemporary machine learning (ML) algorithms, including XGBoost, K-Nearest Neighbors, Random Forest, Isolation Forest, and Support Vector Machines, to analyze transaction data for anomalies indicative of fraudulent behavior. …”
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Machine Learning Algorithms in Predicting Prices in Volatile Cryptocurrency Markets by Miguel Jiménez-Carrión, Gustavo A. Flores-Fernandez

“…In comparison, alternative models such as Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forests exhibited significantly higher error rates; for instance, XGBoost recorded an RMSE of $17,849.66 and a MAPE of 27.74%. …”
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Predicting Quail Egg Quality Using Machine Learning Algorithms by BI Yildiz, K Eskioğlu, D Özdemir, M Akşit

“…A dataset comprising 350 eggs from 18-week-old Japanese quails was analyzed using Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting. …”
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Application of machine learning algorithms in predicting pyrolytic analysis result by Thi Nhut Suong Le, A. V. Bondarev, L. I. Bondareva, A. S. Monakova, A. V. Barshin

“…To develop the prediction model, 5 different machine learning regression algorithms were applied and compared, including multiple linear regression, polynomial regression, support vector regression, decision tree, and random forest.Results. …”
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Advancing wildfire prediction in Nepal using machine learning algorithms by Saugat Sapkota, Khagendra Prasad Joshi, Sajesh Kuikel, Dipesh Kuinkel, Biplov Bhandari, Yanhong Wu, Haijian Bing, Suresh Marahatta, Deepak Aryal, S-Y Simon Wang, Binod Pokharel

“…To improve wildfire prediction and preparedness, this study evaluated four advanced machine learning algorithms—Random Forest, Radial Basis Function Neural Network, Artificial Neural Network, and Support Vector Machine—using comprehensive dataset (2001–2023) of meteorological, topographical, anthropogenic, locational, and vegetation variables. …”
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A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms by Mohammad Ghattas, Antonio M. Mora, Suhail Odeh

“…Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. …”
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A multi-strategy improved snake optimizer and its application to SVM parameter selection by Hong Lu, Hongxiang Zhan, Tinghua Wang

“…Support vector machine (SVM) is an effective classification tool and maturely used in various fields. …”
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Comparison of Rating-based and Inset Lexicon-based Labeling in Sentiment Analysis using SVM (Case Study: GoBiz Application Reviews on Google Play Store) by Hiliah Firda, Pacu Putra, Nabila Rizky Oktadini, Putri Eka Sevtiyuni, Allsela Meiriza

“…It compares two labeling methods—Rating-Based and Inset Lexicon—and evaluates them using the Support Vector Machine (SVM) algorithm. The analysis process includes data selection, text preprocessing, data transformation using TF-IDF, SVM implementation with 10-fold cross-validation, and result visualization through word clouds. …”
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Classification of Brain Tumors by Using a Hybrid CNN-SVM Model by Talitha Safa Nabila, Abu Salam

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Species-level classification of urban trees from WorldView-2 imagery in Debrecen, Hungary: An effective tool for planning a comprehensive green network to reduce dust pollution by Vanda Eva MOLNAR, Edina SIMON, Szilárd SZABÓ

“…Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were applied to different numbers of the MNF-transformed bands. …”
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Application of inertial navigation high precision positioning system based on SVM optimization by Ruiqun Han

“…The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. …”
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Forecast of Photovoltaic Power Based on IWPA-LSSVM Considering Weather Types and Similar Days by Yilun XU, Binqiao ZHANG, Jing HUANG, Xiao XIE, Ruoxin WANG, Danqing SHEN, Lina HE, Kaifan YANG

“…The least squares support vector machine (lSSVM) was optimized by IWPA, and an IWPA-LSSVM based photovoltaic power prediction model was established considering weather types and similar days. …”
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Drying of Nettle Using Concentrated Air Collector and Concentrated Photovoltaic Thermal Supported Drying System and Modeling with Machine Learning by Mehmet Onur Karaagac

“…The data obtained from the drying system were modelled using machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and gradient boosting decision trees (GBDT). …”
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Sentiment Analysis of TIMNAS Indonesia's Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM by Sewin Fathurrohman, Irfan Ricky Afandi, Firman Noor Hasan

“…The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. …”
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Passive indoor human daily behavior detection method based on channel state information by Xiaochao DANG, Yaning HUANG, Zhanjun HAO, Xiong SI

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Medium and Long-Term Hydrogen Load Forecast for Unified Energy System by Shengjiang PENG, Chuanshuai SUN, Jianjun TUO, Tiejiang YUAN

“…Firstly, on the basis of the hydrogen load sample data from the industrial field, the characteristics of the load data are calculated and the support vector machine regression (SVR) algorithm is applied to set up the hydrogen load forecast model accordingly. …”
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A real-time AI tool for hybrid learning recommendation in education: Preliminary results by Chaman Verma

“…This study created an innovative AI tool utilizing the Support Vector Machine (SVM) algorithm on primary samples of Hungarian informatics students to assess their suitability for adopting hybrid learning in their studies. …”
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Integrating Soil, Leaf, Fruitlet, and Fruit Nutrients, Along with Fruit Quality, to Predict Post-Storage Quality of Staccato Sweet Cherries by Mehdi Sharifi, William Wolk, Keyvan Asefpour Vakilian, Hao Xu, Stephanie Slamka, Karen Fong

“…This study aimed to forecast key quality attributes of Staccato sweet cherries after storage, simulating shipping conditions, by analyzing spring soil, leaf, fruitlet, and at-harvest data from thirty orchards in the Okanagan Valley, British Columbia, Canada, over two years. A support vector machine (SVM) was used to predict post-storage variables, with pre-harvest and at-harvest data selected by a genetic algorithm. …”
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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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