Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture by Md Shoaib Ahmed, Tanjim Taharat Aurpa, Md. Abul Kalam Azad

“…In the second portion, we extract the involved features to classify the diseases with the help of the Support Vector Machine (SVM) algorithm of machine learning with a kernel function. …”
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Optimization of Hydronic Heating System in a Commercial Building: Application of Predictive Control with Limited Data by Rana Loubani, Didier Defer, Ola Alhaj-Hasan, Julien Chamoin

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Fault classification of meta-action unit using CEEMDAN double-layer decomposition and COA-SVM by Anxiang Guo, Zhan Zhao, Jiarui Sun, Hongyu Ge

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Comparative Analysis of a Quantum SVM With an Optimized Kernel Versus Classical SVMs by Matheus Cammarosano Hidalgo

“…Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively. …”
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Gravity Predictions in Data-Missing Areas Using Machine Learning Methods by Yubin Liu, Yi Zhang, Qipei Pang, Sulan Liu, Shaobo Li, Xuguo Shi, Shaofeng Bian, Yunlong Wu

“…In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…After data preprocessing, two primary machine learning algorithms were employed: Support Vector Regression (SVR) and Random Forest (RF), both implemented using Scikit-learn. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…Methods This study was a secondary analysis of a recently published clinical trial that recruited 1,070 children aged 3- to 4-year-old with ECC. Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. …”
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Analysis of Community Sentiment Towards Free Nutrition Meal Programs on Twitter Using Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Ensemble Methods by Gresensia Rosadelima Ati, Putri Taqwa Prasetyaningrum

“…The analysis results show that the Support Vector Machine (SVM) algorithm, when combined with the TF-IDF weighting method, provides the highest accuracy of 95.05%. …”
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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

“…Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. …”
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Three Machine Learning Techniques for Melanoma Cancer Detection by Hadi Naghavipour, GholamReza Zandi, Abdulaziz Al-Nahari

“…Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). …”
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Energy storage efficiency modeling of high-entropy dielectric capacitors using extreme learning machine and swarm-based hybrid support vector regression computational methods by Yas Al-Hadeethi, Taoreed O. Owolabi, Mouftahou B. Latif, Bahaaudin M. Raffah, Ahmad H. Milyani, Saheed A. Tijani

“…This work employs single hidden layer extreme learning machine (ELM) algorithm and hybrid particle swarm optimization-based support vector regression (PS-SVR) for determining energy storage efficiency of high-entropy ceramics. …”
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A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…The results indicate that the proposed hybrid approach increases accuracy by 15% compared to models that use a single supervised learning algorithm, such as support vector regression (SVR), multi-layer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), and an increase in accuracy of 4% over other hybrid algorithms, such as convolutional neural networks and long short-term memory. …”
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Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning by Asraa Muayed Abdalah, Noor Redha Alkazaz

Subjects: “…Artificial Intelligence, Machine Learning Algorithms, Face Recognition, Age Prediction, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN)…”
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Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM by Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI

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Gearbox Fault Diagnosis Method Based on Improved Multi-scale Mean Permutation Entropy and Parameter Optimization SVM by Guo Panpan, Zhang Wenbin, Cui Ben, Xu Han

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Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM by Wang Bo, Nan Xinyuan

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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. …”
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Sentiment Analysis on Public Perception of the Nusantara Capital on Social Media X Using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) Methods by Dinda Haliza, Muhammad Ikhsan

“…This study aims to explore public sentiment regarding the development of IKN by applying artificial intelligence-based classification algorithms, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…Utilizing a Support Vector Machine (SVM) classification algorithm applied to time-series Landsat and high-resolution imagery (2003–2023), we quantify land cover changes, while Normalized Difference Vegetation Index (NDVI) trends serve as indicators of ecological recovery. …”
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