A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm by S. Zheng, A. N. Jiang, X. R. Yang, G. C. Luo

“…This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). …”
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Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

“…Accurate early diagnosis is very important to provide information that can accelerate the treatment process and reduce complications in the mother and baby. One of the machine learning methods that can be used to predict GDM is the Support Vector Machine (SVM) algorithm and the Random Forest (RF) algorithm. …”
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A general cubic spline smooth semi-supervised support vector machine by ZHANG Xiao-dan, MA Jing-gai

“…A general cubic spline function with 2 times differentiable at zero point was deduced by a general three-moment method and was used to approach the non-smooth part in the semi-supervised support vector machine. A new smooth semi-supervised support vector with 1 time differentiable based on the general cubic spline function was constructed, and thus a lot of fast optimization algorithms could be applied to solve the smooth semi-supervised vector machine model. …”
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Support Vector Machine and Granular Computing Based Time Series Volatility Prediction by Yuan Yang, Xu Ma

“…Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.…”
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Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm by Rafiqul Islam, Md. Arif Hossain

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CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS by Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO

“…The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. …”
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Predicting failures in fiber optic information transmission systems with support of machine learning by Nafisa Juraeva, Dilmurod Davronbekov, Ulugbek Turdiev

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Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics by Quiin Latifah Almatin Lubis, Arif Akbarul Huda

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Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches by Honglei Yao, Andrew Topper

“…Optimizing energy efficiency and minimizing environmental impact in buildings depends critically on managing cooling requirements. The application of Support Vector Regression Model to the prediction of cooling load is explored in this study. …”
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Tennis Assistance Technology Based on Dynamic Time Warping Algorithm by Penggang Wang, Pengpeng Zhang, Guanxi Fan

“…This technology combines components such as normalization, support vector machine, joint detection, sparse matrix, and second-order stepping mode to improve algorithm performance and reduce computational complexity. …”
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Brain image clustering by wavelet energy and CBSSO optimization algorithm by Mohammad Sedaghat, Hasan Hosseinzadeh

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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…Our approach includes meticulous data preparation, which includes analyzing crucial operating elements such as voltage, current, and temperature. We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. …”
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An enhanced control solutions for efficient urban waste management using deep learning algorithms by Gabriel James, Anietie Ekong, Etimbuk Abraham, Enobong Oduobuk, Nseobong Michael, Victor Ufford, Oscar Ebong

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Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms by Ashutosh Shukla, Rupendra Kumar Pachauri, Athar Hussain, Ahmed Ali, Baseem Khan

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A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques by Peng Chen, Yonghong Xie, Pei Jin, Dezheng Zhang

“…In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. …”
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Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Lei Wang, Jinghang Li, Wenbo Zhang, Yu Li

“…In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). …”
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Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing by Zhu Liu, Xuesong Qiu, Yonggui Wang, Shuai Zhang, Zhi Li

“…Aiming at the hardware reusability, multi-service carrying capacity, and computing resource limitations of edge devices, a light-weight voltage sag monitoring and classification method based on improved firefly algorithm optimization, extended Kalman filter, and least-square support-vector machine is proposed. …”
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Sentiment Analysis of User Reviews on the Game GTA V Using Support Vector Machine by Adika Kaka Saputra, Maya Rini Handayani, Nur Cahyo Hendro Wibowo, Khothibul Umam

“…The TF-IDF method was used to convert text into numerical vectors, and sentiment classification was conducted using the Support Vector Machine (SVM) algorithm. …”
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SENTIMENT ANALYSIS OF OMNIBUS LAW USING SUPPORT VECTOR MACHINE (SVM) WITH LINEAR KERNEL by Ahmad Rohiqim Makhtum, Muhammad Muhajir

“…They were then assigned weights and classified using the Suport Vector Machine (SVM) method. The objective was to identify the public's sentiments towards the Omnibus law and evaluate the accuracy of the Support Vector Machine (SVM) method. …”
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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. …”
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