SUPPORT_VECTOR_MACHINES

作品数：214被引量：655H指数：12
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Evaluating machine learning methods for predicting groundwater fluctuations using GRACE satellite in arid and semi-arid regions
《Journal of Groundwater Science and Engineering》2025年第1期5-21,共17页Mobin Eftekhari Abbas Khashei-Siuki 
This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite ...
关键词：Decision Trees Support Vector Machines Random Forests GRACE Satellite Groundwater level 
Intrusion Detection System Based on an Intelligent Multilayer Model Using Machine Learning
《Journal of Artificial Intelligence and Technology》2024年第4期332-341,共10页Ouafae El Aeraj Cherkaoui Leghris 
With the rapid advent of information technology and social networking,the multiplication of connected devices further exposes users to the vulnerability of their personal data.This growing interconnectedness increases...
关键词：intrusion detection system support vector machines SNORT machine learning 
Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents被引量：1
《World Journal of Psychiatry》2024年第11期1696-1707,共12页Zhi-Hui Yu Ren-Qiang Yu Xing-Yu Wang Wen-Yu Ren Xiao-Qin Zhang Wei Wu Xiao Li Lin-Qi Dai Ya-Lan Lv 
BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers base...
关键词：Major depressive disorder ADOLESCENT Support vector machine Machine learning Resting-state functional magnetic resonance imaging NEUROIMAGING BIOMARKER 
Machine learning based models for predicting compressive strength of geopolymer concrete
《Frontiers of Structural and Civil Engineering》2024年第7期1028-1049,共22页Quang-Huy LE Duy-Hung NGUYEN Thanh SANG-TO Samir KHATIR Hoang LE-MINH Amir H.GANDOMI Thanh CUONG-LE 
Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding o...
关键词：geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines 
Differentially Private Support Vector Machines with Knowledge Aggregation
《Computers, Materials & Continua》2024年第3期3891-3907,共17页Teng Wang Yao Zhang Jiangguo Liang Shuai Wang Shuanggen Liu 
supported in part by National Natural Science Foundation of China(Nos.62102311,62202377,62272385);in part by Natural Science Basic Research Program of Shaanxi(Nos.2022JQ-600,2022JM-353,2023-JC-QN-0327);in part by Shaanxi Distinguished Youth Project(No.2022JC-47);in part by Scientific Research Program Funded by Shaanxi Provincial Education Department(No.22JK0560);in part by Distinguished Youth Talents of Shaanxi Universities,and in part by Youth Innovation Team of Shaanxi Universities.
With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most...
关键词：Differential privacy support vector machine knowledge aggregation data utility 
Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China被引量：1
《China Geology》2024年第1期104-115,共12页Ao Zhang Xin-wen Zhao Xing-yuezi Zhao Xiao-zhan Zheng Min Zeng Xuan Huang Pan Wu Tuo Jiang Shi-chang Wang Jun He Yi-yong Li 
supported by the projects of the China Geological Survey(DD20221729,DD20190291);Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...
关键词：Landslides susceptibility assessment Machine learning Logistic Regression Random Forest Support Vector Machines XGBoost Assessment model Geological disaster investigation and prevention engineering 
An improved method for sand wave morphology discrimination in rivers by combining a flow resistance law and support vector machines
《International Journal of Sediment Research》2024年第1期144-152,共9页Yuchuan Bai Yanjie Sun Xiaolong Song Haijue Xu 
the National Natural Science Foundation of China(Grant No.51979185);the Science and Technology Planning Program of Tianjin,China(Grant No.21JCQNJC00480).
A parameterized expression for sand wave morphology in rivers is established using a flow resistance law while accounting for sediment incipient velocity.A distinct relation is drawn between the proposed characteristi...
关键词：Sand wave morphology discrimination Flow resistance law Sediment incipient velocity Support vector machines Yellow River Estuary 
t-SNE:A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters被引量：1
《Artificial Intelligence in Agriculture》2023年第1期58-68,共11页Rui Silva Pedro Melo-Pinto 
supported by National Funds by FCT-Portuguese Foundation for Science and Technology,under the project UIDB/04033/2020;The authors also gratefully acknowledge the support from National funding by FCT,Portuguese Foundation for Science and Technology,through the individual research grant(SFRH/BD/137216/2018);from NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture:in this work we perform a study on models capable of predicting oenological parameters ...
关键词：Hyperspectral images Dimensionality reduction Regression T-SNE Support vector machines Wine grape berries 
Analysis of loss functions in support vector machines
《Frontiers of Mathematics in China》2023年第6期381-414,共34页Huajun WANG Naihua XIU 
Support vector machines(SVMs)are a kind of important machine learning methods generated by the cross interaction of statistical theory and optimization,and have been extensively applied into text categorization,diseas...
关键词：Support vector machines loss function SUBDIFFERENTIAL proximal operator Fenchel conjugate 
Application of four machine-learning methods to predict short-horizon wind energy被引量：1
《Global Energy Interconnection》2023年第6期726-737,共12页Doha Bouabdallaoui Touria Haidi Faissal Elmariami Mounir Derri El Mehdi Mellouli 
Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind e...
关键词：Wind Energy Prediction Support Vector Machines Decision Trees Adaptive Neuro-Fuzzy Inference Systems Artificial Neural Networks 
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