Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…Fifteen combinations of linguistic features were trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms. …”
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Raman and FT-IR Spectroscopy Coupled with Machine Learning for the Discrimination of Different Vegetable Crop Seed Varieties by Stefan M. Kolašinac, Marko Mladenović, Ilinka Pećinar, Ivan Šoštarić, Viktor Nedović, Vladimir Miladinović, Zora P. Dajić Stevanović

“…Pre-processing was followed by Principal Component Analysis (PCA), and several classification methods were applied after that: the Support Vector Machines (SVM) algorithm, Partial Least Square Discriminant Analysis (PLS-DA), and Principal Component Analysis-Quadratic Discriminant Analysis (PCA-QDA). …”
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Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm by Omid Asadi, Mahsan Hajihosseini, Sima Shirzadi, Zahra Einalou, Mehrdad Dadgostar

“…This study aimed to address this challenge by employing the common spatial pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. …”
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Evaluation of Multi-Class Classification Performance Lung Cancer Through K-NN and SVM Approach by Muh. Indra Endriartono Saputra Troy, Sitti Rahmah Jabir, Siska Anraeni

“…In an effort to improve diagnosis and treatment, this study proposes an approach for multiclass performance evaluation using K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms based on 2024 data. in this study KNN is implemented conventionally while SVM applies 2 kernel processes, namely Linear and Polynominal. …”
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Enhancing the Accuracy of Land Use/Cover Map Using Some Spectral Indices in Sarab County–East Azerbaijan by A. Sarabchi, H. Rezaei, F. Shahbazi

“…Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.   …”
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SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches by Chuangchuang LI, Guangyue LU, Hanglong WANG

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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Strong inverse correlations between soil moisture and resistivity (r = −0.88) and weak positive correlations with temperature (r = 0.41) and suction (r = 0.34) were observed. Among the machine learning models evaluated, artificial neural networks and support vector machines demonstrated superior predictive performance, achieving a coefficient of determination (R²) above 0.77 and lower root mean square error (RMSE) values (less than 0.14). …”
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Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia by Fernanda Mulya Syah Putra, Sindhu Rakasiwi, Noval Ariyanto

“…This study aims to gain a better understanding of the components that influence user perception and satisfaction using textual, sentiment, and statistical analysis techniques. By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. …”
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Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm by Lilin Li, Qing He, Hua Wang, Wensheng Wang

“…This study proposes a novel hybrid model, DIWPSO-SVM, which integrates dynamic inertia weight particle swarm optimization (DIWPSO) with support vector machines (SVMs) to enhance the prediction accuracy of midspan deflection. …”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques by Mohammed Alqarni, Ali Alqarni

“…We employ three tree-based ensemble models: Kernel Ridge Regression (KRR), $$\:{\upnu\:}$$ -Support Vector Regression ( $$\:{\upnu\:}$$ -SVR), and Multi Linear Regression (MLR) for modeling the relationship between spatial coordinates and the concentration. …”
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Komparasi Algoritma Support Vector Machines dengan Algoritma Artificial Neural Network untuk Memprediksi Nilai Persetujuan Kredit Modal Kerja yang Diberikan Bank Umum by Abu Sopian, Agus Wiyatno, Albert Riyandi

“…Working capital credit approval provided by commercial bank need to predict because it has increased of credit provision provided by commercial bank that can be used as measurement of economic growth and country stability or as measurement of economic growth indicator from monetary sector by Bank of Indonesia. In this research will conducted working capital credit value approval prediction will be provided by commercial bank using support vector machine algorithm that is compared with artificial neutral network algorithm. …”
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Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning by Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

“…Three classification algorithms were applied: Decision Tree, Random Forest, and Support Vector Machine (SVM). …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…In the confusion matrix, the support vector machines with stepwise feature selection showed the best performance. …”
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An UWB LNA Design with PSO Using Support Vector Microstrip Line Model by Salih Demirel, Filiz Gunes, A. Kenan Keskin

“…A rigorous and novel design procedure is constituted for an ultra-wideband (UWB) low noise amplifier (LNA) by exploiting the 3D electromagnetic simulator based support vector regression machine (SVRM) microstrip line model. …”
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Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model by Kevin Gordillo-Orjuela, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marín

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Predicting biogas production in real scale anaerobic digester under dynamic conditions with machine learning approach by M. Erdem Isenkul, Sevgi Güneş-Durak, Yasemin Poyraz Kocak, İnci Pir, Mertol Tüfekci, Güler Türkoğlu Demirkol, Selçuk Sevgen, Aslı Seyhan Çığgın, Neşe Tüfekci

“…Among these, Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel has been used to predict biogas yield based on diverse operating parameters. …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…In this research, an innovative support vector regression approach is present that leverages machine learning algorithms to forecast the bearing response of CFREC joints after undergoing hydrothermal aging. …”
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