HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION by Syaiful Anam, M. Rafael Andika Putra, Zuraidah Fitriah, Indah Yanti, Noor Hidayat, Dwi Mifta Mahanani

“…There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). …”
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Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine by Perla Y. Sevilla-Camacho, José B. Robles-Ocampo, Juvenal Rodríguez-Resendíz, Sergio De la Cruz-Arreola, Marco A. Zuñiga-Reyes, Edwin N. Hernández-Estrada

“…This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. …”
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PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses by Xiaoyong Liu, Hui Fu

“…Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). …”
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Local Similarity-Based Fuzzy Multiple Kernel One-Class Support Vector Machine by Qiang He, Qingshuo Zhang, Hengyou Wang, Changlun Zhang

“…One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. …”
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Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques by Syamsul Risal, Fajar Apriyadi, A. Sumardin, Andini Dani Achmad, Annisa Nurul Puteri

“…This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. …”
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Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs) by Robert A. Sowah, Marcellinus Kuuboore, Abdul Ofoli, Samuel Kwofie, Louis Asiedu, Koudjo M. Koumadi, Kwaku O. Apeadu

“…This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. …”
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Anomaly detection using unsupervised machine learning algorithms: A simulation study by Edmund Fosu Agyemang

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Improving Airport Flight Prediction System Based on Optimized Regression Vector Machine Algorithm by Baraa Yousif Salman, Jaber Parchami

“…In this research, the optimized support vector regression (SVR) algorithm has been used to improve the accuracy of air delay prediction. …”
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Retracted: Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Applied Bionics and Biomechanics

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Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine by Yun Deng, Lifan Xiao, Yuanyuan Shi

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Unveiling Public Sentiment on Quarter Life Crisis: A Comparative Performance Evaluation of Support Vector Machine and Naïve Bayes Algorithms on Social Media X Data by Talitha Dwi Septyorini, Khothibul Umam, Maya Rini Handayani

“…This study seeks to classify public opinion related to the QLC into positive and negative sentiments by employing two computational classification models, namely Support Vector Machine (SVM) and Naïve Bayes (NB). …”
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Inverse System Decoupling Control of Composite Cage Rotor Bearingless Induction Motor Based on Support Vector Machine Optimized by Improved Simulated Annealing-Genetic Algorithm by Chengling Lu, Junhui Cheng, Qifeng Ding, Gang Zhang, Jie Fang, Lei Zhang, Chengtao Du, Yanxue Zhang

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Design and implementation algorithm of safe driver assistant system based on EOG by Zhao LYU, Xiao-pei WU, Chao ZHANG, Bing WEI

“…Furthermore, independent component analysis (ICA) algorithm was used to extract spatial feature parameters of activity-detected EOG signals, and combined with support vector machine (SVM) method to recognize the type of saccade signals, such as up-rolling, left-rolling and right-rolling. …”
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Particle Swarm Optimization Support Vector Machine-Based Grounding Fault Detection Method in Distribution Network by Zhongqin Xiong, Shichang Huang, Shen Ren, Yutong Lin, Zewen Li, Dongyu Li, Fangming Deng

“…In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-based grounding fault detection method is proposed for distribution networks. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Also, among the algorithms used in this research, the random forest algorithm based on ensemble learning has a higher advantage than the two-support vector machine and the artificial neural network algorithms.…”
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Machine learning algorithm to predict in-hospital mortality after aneurysmal subarachnoid hemorrhage by Juri V. Kivelev, Alexey L. Krivoshapkin, Albert A. Sufianov

“…The raw dataset contained 7290 features, from which 12 features were selected to train the following ML models: logistic regression, support vector machine, random forest, XGBoost, multilayer perceptron and long short-term memory network (LSTM) were tested. …”
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A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters by Murat Canayaz, Murat Demir, Zeynal Topalcengiz

“…Selected features after optimization were used in the K-nearest neighbor algorithm (kNN), support vector machine (SVM) and decision tree (DT) classification methods. …”
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Development of algorithms and software for classification of nucleotide sequences by V. R. Zakirava, D. A. Syrakvash, S. V. Hileuski, P. V. Nazarov, M. M. Yatskou

“…The most informative features of vectorization models were determined using feature selection and classification algorithms based on the random forests and support vector machine methods. …”
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Spatial prediction of landslide susceptibility using the data-mining algorithm (case study: Kamyaran county) by Osama Ashraf Mohammed, Hemn Kamal Hamaamin, Jwanro Azad, Sabri Rasooli, Houzhi Li

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Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine by Li Shu-rong, Ge Yu-lei

“…A new accurate method on predicting crude oil price is presented, which is based on ε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors. …”
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