Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine by Ibrahim Shomope MS, Kelly M. Percival BS, Nabil M. Abdel Jabbar PhD, Ghaleb A. Husseini PhD

“…In this regard, Random Forest (RF) and Support Vector Machine (SVM) are two ML algorithms that have been extensively applied in various biomedical and drug delivery contexts. …”
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OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS by SUN YaoQin

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Attitude-Predictive Control of Large-Diameter Shield Tunneling: PCA-SVR Machine Learning Algorithm Application in a Case Study of the Zhuhai Xingye Express Tunnel by Hui Li, Yijun Tan, Decheng Zeng, Dong Su, Shiye Qiao

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Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging by Jorge Alberto Leal, Luis Hernan Ochoa, Carmen Cecilia Contreras

“…In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. …”
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On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets by Rafael F. Pinheiro, Rui Fonseca-Pinto

“…Leveraging support vector machine (SVM) technology, a well-known machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. …”
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Application of Machine Learning Algorithms in Nitrous Oxide (N₂O) Emission Estimation in Data-Sparse Agricultural Landscapes by Uttam Ghimire, Waqar Ashiq, Asim Biswas, Wanhong Yang, Prasad Daggupati

“…To understand if machine learning algorithms could be employed in agricultural landscapes to estimate N₂O emissions, multiple linear regression (MLR), random forest regression (RFR), support vector regression (SVR) and artificial neural network (ANN) algorithms are tested on an agricultural site in Ontario, Canada. …”
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Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm by NIU Fangpeng, LI Xinguo, MAMATTURSUN•Eziz, ZHAO Hui

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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

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Implementasi Machine Learning untuk Pengenalan Penggunaan Makeup by Fetricia Wulandari, Rian Ferdian

“…It uses the Haar Cascade algorithm for face detection and a Support Vector Machine (SVM) classifier to categorize faces into three main shapes: round, oval, and square. …”
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Multi-Level Depression Severity Detection with Deep Transformers and Enhanced Machine Learning Techniques by Nisar Hussain, Amna Qasim, Gull Mehak, Muhammad Zain, Grigori Sidorov, Alexander Gelbukh, Olga Kolesnikova

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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

Subjects: “…soil water content prediction;support vector machine;salp swarm algorithm;opposition-based learning;chaotic optimization…”
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Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

“…The study employed four machine learning (ML) methods to assess depression: logistic regression (LR), support vector machine (SVM), XGBoost, and neural networks (NNs). …”
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Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches by Samuel Keown, Dylan O’Dwyer

“…To address this issue, robust hybrid machine learning (ML) approaches are employed, particularly emphasizing the Least Square Support Vector Regression (LSSVR) model. …”
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Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine by Jing Ming, Long Chen, Yan Cao, Chi Yu, Bi-Sheng Huang, Ke-Li Chen

“…In this study, the feasibility of using Raman spectroscopy combined with support vector machine (SVM) for rapid identification of nine easily confused mineral TCMs, i.e., borax, gypsum fibrosum, natrii sulfas exsiccatus, natrii sulfas, alumen, sal ammoniac, quartz, calcite, and yellow croaker otolith, was investigated. …”
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Remote sensing monitoring of wheat aphids by combining HJ satellite images with least squares twin support vector machine model by HU Gensheng, WU Wentian, LUO Juhua, HUANG Wenjiang, LIANG Dong, HUANG Linsheng

“…The monitoring model of wheat aphids in Tongzhou District and Shunyi District of Beijing was established by using the least squares twin support vector machine (LSTSVM) . The LSTSVM has a good processing ability for large scale unbalanced data and has stronger robustness than the traditional support vector machine (SVM) . …”
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Comparative Study of Machine Learning Techniques for Insurance Fraud Detection by Navin Duwadi, Anita Sharma

“…The most popular traditional machine learning algorithms used to identify insurance fraud in the auto industry were found to be support vector machine, logistic regression, and random forest.…”
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Optimization of artificial intelligence in localized big data real-time query processing task scheduling algorithm by Maojin Sun, Luyi Sun

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Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN) by Myo Myint Oo, Sinchai Kamolphiwong, Thossaporn Kamolphiwong, Sangsuree Vasupongayya

“…The objectives of this paper are to propose a detection method of DDoS attacks by using SDN based technique that will disturb the legitimate user's activities at the minimum and to propose Advanced Support Vector Machine (ASVM) technique as an enhancement of existing Support Vector Machine (SVM) algorithm to detect DDoS attacks. …”
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SENTIMENT ANALYSIS OF REVIEWS ON X APPS ON GOOGLE PLAY STORE USING SUPPORT VECTOR MACHINE AND N-GRAM FEATURE SELECTION by Fahri Aimar Kusumo, Dewi Retno Sari Saputro, Purnami Widyaningsih

“…Sentiment analysis requires classification algorithms, such as Support Vector Machine (SVM). SVM is a frequently used algorithm for text data classification because it can handle high-dimensional data. …”
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