Article
Mathematics
Fengchun Liu, Sen Zhang, Weining Ma, Jingguo Qu
Summary: This paper proposes a support vector machine (SVM) attack detection model based on particle swarm optimization (PSO) to improve the attack detection capability of cyber physical systems (CPS). By using anomaly detection, data augmentation, and feature extraction methods, the model achieves accurate detection of different types of attack data.
Article
Computer Science, Hardware & Architecture
Ruizhong Du, Yun Li, Xiaoyan Liang, Junfeng Tian
Summary: This paper proposes a lightweight support vector machine intrusion detection model based on Cloud-Fog Collaboration(CFC-SVM), which addresses the issues of fog nodes being closer to user equipment, having heterogeneous nodes, limited storage capacity resources, and greater vulnerability to intrusion. The model utilizes Principal Component Analysis (PCA) to reduce dimensionality, eliminates attribute correlation, and reduces training time. Experimental results using the KDD CUP 99 dataset demonstrate that the proposed model outperforms other similar algorithms in terms of detection time, detection rate, and accuracy, effectively solving the problem of intrusion detection in the fog environment.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Deepa D. Shankar, Nesma Khalil, Adresya Suresh Azhakath
Summary: The fast evolution of Information and Digital technology has made the internet an effective medium for communication, but it has also led to data exploitation. To protect their data from misuse, users must utilize security frameworks like Information Hiding. This research focuses on steganalysis, a technique for detecting concealed information, and aims to address its general concept and associated breaches. The study uses blind statistical steganalysis with JPEG text embedded images, extracting features that indicate alterations during embedding. Different steganographic schemes and machine learning techniques, such as Support Vector Machine with Particle Swarm Optimization (SVM-PSO), are examined for comparative analysis.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Metallurgy & Metallurgical Engineering
Ji Ya-feng, Song Le-bao, Sun Jie, Peng Wen, Li Hua-ying, Ma Li-feng
Summary: The article introduces the application of an optimized model based on support vector machine to improve the quality of hot strip rolling products. The experimental results show that the PCA-CS-SVM model has the best prediction accuracy and fastest convergence speed, meeting the production requirements.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Engineering, Chemical
Mingjiang Shi, Peipei Tan, Liansheng Qin, Zhiqiang Huang
Summary: The valve is a critical control component in the oil and gas transportation system, and its service life can be affected by internal leakage caused by environmental and transmission medium factors. Traditional testing methods cannot accurately determine the valve's service life. Therefore, valve life prediction research is important for ensuring the safety of oil and gas transmission. This study proposes a valve service life prediction method based on the PCA-PSO-LSSVM algorithm. The method utilizes principal component analysis (PCA) to identify the main factors affecting the valve's service life, and applies the least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) to predict the valve's service life. The results demonstrate that the predicted valve service life using the PCA-PSO-LSSVM algorithm is closer to the actual value, with an average relative error (MRE) of 16.57% and a root mean square error (RMSE) of 1.2636. This improved accuracy in valve life prediction provides scientific and technical support for valve maintenance and replacement.
Article
Engineering, Marine
Zhiming Cheng, Houlin Liu, Runan Hua, Liang Dong, Qijiang Ma, Jiancheng Zhu
Summary: This paper proposes a fault identification method based on weighted kernel principal component analysis (WKPCA) and particle swarm optimization support vector machine (PSO-SVM), which effectively solves the problem of multi-fault classification of the centrifugal pump and provides reference for efficient maintenance of equipment.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
James A. Mckelvy, Irina Novikova, Eugeniy E. Mikhailov, Mario A. Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B. Matsko
Summary: In this study, an unsupervised machine learning algorithm and nonlinear dimensionality reduction technique were used to accurately determine the longitudinal angle of the local magnetic field through spectroscopic observations of EIT spectra. The algorithm represented each EIT spectrum measurement as a coordinate in a new reduced dimensional feature space, and a supervised support vector regression machine modeled the relationship between the KPCA projections and field direction. The results showed that the proposed method could predict the longitudinal angle of the local magnetic field with high accuracy and resolution.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Environmental Sciences
Erfan Ghasemi Tousi, Jennifer G. Duan, Patricia M. Gundy, Kelly R. Bright, Charles P. Gerba
Summary: This study assessed the impact of incorporating sediment information on improving machine learning models to quantify E. coli levels in irrigation water. The support vector machine model performed the best and including sediment features improved the performance of all models.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Agriculture, Multidisciplinary
Long Zhao, Xinbo Zhao, Hanmi Zhou, Xianlong Wang, Xuguang Xing
Summary: By establishing and optimizing machine learning models, using important factors extracted by path analysis as input, the accuracy of ETo prediction can be improved.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Computer Science, Information Systems
Zhaoyu Lei, Jianyi Guo, Feng Zheng, Jiayang Li, Lei Wang, Liangshou Hao, Youping Fan
Summary: This paper proposes a method for evaluating the state of thyristors based on kernel principal component analysis. A basic index system for evaluating the aging state of thyristor is established and effective evaluation indicators are obtained through dimensionality reduction and fuzzy evaluation. Experimental results show that the method provides more reasonable evaluation results.
Article
Computer Science, Information Systems
Baosheng Li, Chuandong Qin
Summary: This research proposes a new analytical framework to predict octane number, which can improve product quality through dimension reduction, model establishment, and parameter optimization. Key attributes affecting product quality are identified, enabling engineers to adjust operational variables to obtain high-quality products.
Article
Computer Science, Information Systems
Mohammed Amin Almaiah, Omar Almomani, Adeeb Alsaaidah, Shaha Al-Otaibi, Nabeel Bani-Hani, Ahmad K. Al Hwaitat, Ali Al-Zahrani, Abdalwali Lutfi, Ali Bani Awad, Theyazn H. H. Aldhyani
Summary: This paper presents a research model for an intrusion detection system based on Principal Component Analysis feature selection technique and different Support Vector Machine kernel classifiers. The impact of various kernel functions in SVM is investigated, and the performance of the investigation model is evaluated using multiple metrics. The results show that the Gaussian radial basis function kernel outperforms other kernels in terms of accuracy, sensitivity, and F-measure on both datasets.
Article
Computer Science, Hardware & Architecture
Ming Zhao, Weiyu Qiu, Tingxi Wen, Tingdi Liao, Jianlong Huang
Summary: The proposed method utilizes machine learning to extract texture information from TEC component images for classification features, using PCA for feature selection, and SVM for defect classification. Through testing, this method has been proven to be superior to manual detection, improving defect classification and overall product quality.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Physics, Applied
Jose Fabian Villa-Manriquez, Roberto Y. Sato-Berru, Jorge Castro-Ramos, Jose L. Flores-Guerrero
Summary: In this study, micro-Raman spectroscopy combined with support vector machine (SVM) was used to detect low concentrations of TMAO in synthetic urine. The study achieved high accuracy in classifying different concentrations of TMAO, indicating the potential of this method in detecting TMAO in urine. This research is of significance for understanding the association between TMAO and related diseases, as well as urine testing.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2022)
