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Journal articles on the topic 'SVM-GA'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Purnamasari, Desi, Muhammad Adi Khairul Anshary, and Rianto Rianto. "Particle Swarm Optimization dan Genetic Algorithm untuk analisis sentimen pemekaran Papua di Twitter berbasis Support Vector Machine." AITI 20, no. 2 (2023): 177–90. http://dx.doi.org/10.24246/aiti.v20i2.177-190.

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Support Vector Machine (SVM) dapat digunakan untuk mengklasifikasikan analisis sentimen ke dalam sentimen positif atau negatif. Dalam penelitian ini data sentimen diambil dari Twitter dengan topik pemekaran Papua. Karena SVM memiliki kelemahan dalam pemilihan fitur pada saat pengklasifikasian maka diterapkan fitur optimasi algoritma SVM menggunakan feature selection. Dua metode feature selection yang digunakan adalah Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Tweet yang diambil sebanyak 839 data tweet, yang kemudian dibagi menjadi 640 data untuk proses training dan 199 data

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LIU, HAN-BING, and YU-BO JIAO. "APPLICATION OF GENETIC ALGORITHM-SUPPORT VECTOR MACHINE (GA-SVM) FOR DAMAGE IDENTIFICATION OF BRIDGE." International Journal of Computational Intelligence and Applications 10, no. 04 (2011): 383–97. http://dx.doi.org/10.1142/s1469026811003215.

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A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three susp

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Gu, Yuqi, Jianhua Wu, Yijun Guo, et al. "Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology." Foods 13, no. 20 (2024): 3331. http://dx.doi.org/10.3390/foods13203331.

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To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the charac

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Sajid, Maimoona Bint E., Sameeh Ullah, Nadeem Javaid, Ibrar Ullah, Ali Mustafa Qamar, and Fawad Zaman. "Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain." Wireless Communications and Mobile Computing 2022 (January 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/7386049.

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In this paper, a blockchain-based secure routing model is proposed for the Internet of Sensor Things (IoST). The blockchain is used to register the nodes and store the data packets’ transactions. Moreover, the Proof of Authority (PoA) consensus mechanism is used in the model to avoid the extra overhead incurred due to the use of Proof of Work (PoW) consensus mechanism. Furthermore, during routing of data packets, malicious nodes can exist in the IoST network, which eavesdrop the communication. Therefore, the Genetic Algorithm-based Support Vector Machine (GA-SVM) and Genetic Algorithm-based De

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Zhong, Lingfeng, Rui Liu, Xiaodong Miao, Yufeng Chen, Songhong Li, and Haocheng Ji. "Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine." Aerospace 10, no. 6 (2023): 558. http://dx.doi.org/10.3390/aerospace10060558.

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Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compress

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Li, X. Z., and J. M. Kong. "Application of GA-SVM method with parameter optimization for landslide development prediction." Natural Hazards and Earth System Sciences Discussions 1, no. 5 (2013): 5295–322. http://dx.doi.org/10.5194/nhessd-1-5295-2013.

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Abstract. Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering ar

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Wang, Yanjie, Zhengchao Xie, InChio Lou, Wai Kin Ung, and Kai Meng Mok. "Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs." Engineering Computations 34, no. 2 (2017): 664–79. http://dx.doi.org/10.1108/ec-11-2015-0356.

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Purpose The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model. Design/methodology/approach The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based

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Yang, Zhaosheng, Duo Mei, Qingfang Yang, Huxing Zhou, and Xiaowen Li. "Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/926251.

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To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was v

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Xiao, Li Zhi, Dong Ping Yang, De Xiang Sun, Xiao Kun Wang, and Zhi Liang Li. "The Optimization Algorithm of Aviation Equipment Maintenance Cost Forecast and its Applied Research." Advanced Materials Research 760-762 (September 2013): 1851–55. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1851.

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The maintenance cost forecast of aviation equipment is a multifactor influenced, non-linear and little samples problem. Aiming at the problem, genetic algorithm (GA) and support vector machine (SVM) were combined to build a GA-SVM forecast model for maintenance cost of aviation equipment. The model used GA to optimize the parameters of SVM, which can avoid the blindness choice of parameters and improve its forecast efficiency. Through the example analysis, the model has more accurate results and extensibility than PSO-SVM, SVM and multivariate linear regression in the forecast of maintenance c

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Nezaratian, Hosein, Javad Zahiri, Mohammad Fatehi Peykani, AmirHamzeh Haghiabi, and Abbas Parsaie. "A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams." Water Quality Research Journal 56, no. 3 (2021): 127–42. http://dx.doi.org/10.2166/wqrj.2021.003.

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Abstract Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with

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Li, X. Z., and J. M. Kong. "Application of GA–SVM method with parameter optimization for landslide development prediction." Natural Hazards and Earth System Sciences 14, no. 3 (2014): 525–33. http://dx.doi.org/10.5194/nhess-14-525-2014.

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Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA–SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-

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Vahedi, Nafiseh, Majid Mohammadhosseini, and Mehdi Nekoei. "QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches." Current Analytical Chemistry 16, no. 8 (2020): 1088–105. http://dx.doi.org/10.2174/1573411016999200518083359.

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Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set a

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Ma, Dan, Hongyu Duan, Xin Cai, Zhenhua Li, Qiang Li, and Qi Zhang. "A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor." Water 10, no. 11 (2018): 1618. http://dx.doi.org/10.3390/w10111618.

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Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields

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Ding, Mei. "Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology." Computational Intelligence and Neuroscience 2022 (May 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/7153260.

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The application of Internet of Things technology provides conditions for the systematization of enterprise financial data and the intellectualization of financial management. In this study, support vector machine (SVM) algorithm and genetic algorithm (GA) are combined to obtain the innovation model of enterprise financial information intelligent processing based on GA-SVM optimization algorithm. Nine factors affecting enterprise financial information processing from 2010 to 2018 are selected as influencing factors, and according to the idea and method of data modeling, the simulation experimen

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Zhang, Jian Hua. "Optimization of Kernel Function Parameters SVM Based on the GA." Advanced Materials Research 433-440 (January 2012): 4124–28. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4124.

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That Support Vector Machines applies to image recognition have good results，But the kernel function C and parameters of the SVM which influence the result and performance has not been decided. Against this question, this paper bring forward a new algorithm that combines SVM with GA to classify and uses GA to select excellent kernel function, the results of experiment show Image Recognition based on SVM and GA are effective.

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Zhu, Wenjin, Zhiming Chao, and Guotao Ma. "A Support Vector Machine Model with Hyperparameters Optimised by Mind Evolutionary Algorithm for Assessing Permeability of Rock." Advances in Civil Engineering 2020 (September 8, 2020): 1–12. http://dx.doi.org/10.1155/2020/4718493.

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In this paper, a database developed from the existing literature about permeability of rock was established. Based on the constructed database, a Support Vector Machine (SVM) model with hyperparameters optimised by Mind Evolutionary Algorithm (MEA) was proposed to predict the permeability of rock. Meanwhile, the Genetic Algorithm- (GA-) and Particle Swarm Algorithm- (PSO-) SVM models were constructed to compare the improving effects of MEA on the foretelling accuracy of machine learning models with those of GA and PSO, respectively. The following conclusions were drawn. MEA can increase the pr

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Liu, Qin, Jian Min Xu, and Kai Lu. "Study on the Quantitative Method of Oversaturated Intersection." Applied Mechanics and Materials 587-589 (July 2014): 2100–2104. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2100.

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Oversaturation in the modern urban traffic often happens. In order to describe the degree of oversaturation, the indexes of intersection oversaturation degree are put forward include dissipation time, stranded queue, overflow queue and travel speed. On the basis of selected indexes, the genetic algorithm support vector machine (GA-SVM) model was proposed to quantify the degree of oversaturation. In this method the genetic algorithm is used to select the model parameters. The GA-SVM model built is used to quantify the degree of oversaturation. Combining with the volume of intersections in Guang

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Xue, Guanghui, Peng Hou, Sanxi Li, Xiaoling Qian, Sicong Han, and Song Gao. "Coal Gangue Recognition during Coal Preparation Using an Adaptive Boosting Algorithm." Minerals 13, no. 3 (2023): 329. http://dx.doi.org/10.3390/min13030329.

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: The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were

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Zhai, Linwei, Jian Qin, and Lean Yu. "Hybridizing support vector machines into genetic algorithm for key factor exploration in core competence evaluation of aviation manufacturing enterprises." Filomat 30, no. 15 (2016): 4191–98. http://dx.doi.org/10.2298/fil1615191z.

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In the core competence comprehensive evaluation of aviation manufacturing enterprises, exploring the key factors affecting core competence is crucial to improve the competitiveness of the aviation manufacturing enterprises. In this paper, a novel hybrid approach integrating genetic algorithm (GA) and support vector machines (SVM) is proposed to conduct the key factor exploration tasks in the core competitiveness evaluation of aviation manufacturing enterprises. In the proposed hybrid GA-SVM approach, the GA is used for key factor exploration, while SVM is used to calculate the fitness function

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AlAyyash, Saad, A’kif Al-Fugara, Rania Shatnawi, Abdel Rahman Al-Shabeeb, Rida Al-Adamat, and Hani Al-Amoush. "Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping." Sustainability 15, no. 3 (2023): 2499. http://dx.doi.org/10.3390/su15032499.

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The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms res

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Kumar, Amarjeet, Vijay Kumar Singh, Bhagwat Saran, et al. "Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques." Sustainability 14, no. 4 (2022): 2287. http://dx.doi.org/10.3390/su14042287.

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Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (

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Li, Meiyan, and Yingjun Fu. "Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model." Sustainability 14, no. 24 (2022): 16376. http://dx.doi.org/10.3390/su142416376.

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Supply Chain Finance (SCF) is a new type of financing business carried out by commercial banks on the basis of supply chain management, which effectively promotes the healthy development of the supply chain. As the most typical mode of SCF, accounts receivable financing mode can use the part of accounts receivable occupying working capital for financing, which is widely used. In order to effectively manage the credit risk in the Supply Chain Finance and maintain the healthy operation of the supply chain, this paper proposes a supply chain financial credit risk prediction model based on PCA-GA-

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Shi, Xu Chao, and Ying Fei Gao. "Application of Genetic Arithmetic and Support Vector Machine in Prediction of Compression Index of Clay." Applied Mechanics and Materials 438-439 (October 2013): 1167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.438-439.1167.

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The compression index is an important soil property that is essential to many geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming. Support Vector Machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. Considering the fact that parameters in SVM model are difficult to be decided, a genetic SVM was presented in which the parameters in SVM method are optimized by Genetic Algorithm (GA). Taking plasticity index, water content, void ration and d

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Evitasari, Yuliana Dilla, Wawan Joko Pranoto, and Naufal Adzmi Verdikha. "Evaluasi Support Vector Machine Dengan Optimasi Metode Genetic Algorithm Pada Klasifikasi Banjir Kota Samarinda." Jurnal Sains Komputer dan Teknologi Informasi 6, no. 1 (2023): 49–53. http://dx.doi.org/10.33084/jsakti.v6i1.5462.

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Banjir merupakan bencana alam yang sering terjadi di Indonesia, terutama di kota Samarinda yang terletak di Kalimantan Timur. Penelitian ini bertujuan untuk meningkatkan akurasi dengan menerapkan metode seleksi fitur menggunakan Genetic Algorithm (GA). Melalui analisis data banjir kota Samarinda, ditemukan bahwa terdapat tiga atribut yang paling berpengaruh terhadap terjadinya banjir, yaitu kelembapan, lamanya penyinaran matahari, dan kecepatan angin. Selanjutnya, penelitian ini menggunakan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan data banjir. Dengan menerapkan seleksi f

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Cao, Ying, Kunlong Yin, Chao Zhou, and Bayes Ahmed. "Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis." Sensors 20, no. 3 (2020): 845. http://dx.doi.org/10.3390/s20030845.

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The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater l

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Huang, Wencheng, Hongyi Liu, Yue Zhang, et al. "Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM." Applied Soft Computing 109 (September 2021): 107541. http://dx.doi.org/10.1016/j.asoc.2021.107541.

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Wang, Meng, Junqi Yu, Meng Zhou, Wei Quan, and Renyin Cheng. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM." Sustainability 15, no. 24 (2023): 16833. http://dx.doi.org/10.3390/su152416833.

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Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the gen

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Sun, Wei, Guo Xiang Meng, Qian Ye, Jian Zheng Zhang, and Li Weng Zhang. "A New Time Series Regression Method Based on Support Vector Machine Plus and Genetic Algorithm." Advanced Materials Research 201-203 (February 2011): 2277–80. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2277.

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Support vector machine (SVM) is gaining popularity on time series analysis due to its advanced theory foundation. The introduction of the hidden information on the basis of SVM is called support vector machine plus (SVM+). However, the hidden information which provides something closely associated with the time series increases the difficulty of training SVM model. In this paper, a new time series regression method GA-RSVM+ is put forward, in which Genetic Algorithm (GA) is used to search the optimal combination of free parameters. The experimental result shows that GA-RSVM+ can accurately det

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Wang, Wei, Ran Liang, Yun Qi, Xinchao Cui, Jiao Liu, and Kailong Xue. "Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application." Fire 6, no. 10 (2023): 381. http://dx.doi.org/10.3390/fire6100381.

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The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine

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Ridwansyah, Ridwansyah, Ganda Wijaya, and Jajang Jaya Purnama. "HYBRID OPTIMIZATION METHOD BASED ON GENETIC ALGORITHM FOR GRADUATES STUDENTS." Jurnal Pilar Nusa Mandiri 16, no. 1 (2020): 53–58. http://dx.doi.org/10.33480/pilar.v16i1.1180.

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Graduation is a target that must be achieved by students, especially graduating on time will be very important. To determine students who graduate on time or cannot be determined before students reach the final semester and hold a trial, many students who fail to graduate on time cause delays and affect the quality assurance of a tertiary institution. The problem in this research is how to optimize student graduation in order to graduate on time. Therefore, to determine this decision, we conducted a graduation data trial using the SVM method with GA optimization. SVM with accurate learning ski

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Zhang, Yiyi, Jiaxi Li, Xianhao Fan, Jiefeng Liu, and Heng Zhang. "Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm." Polymers 12, no. 7 (2020): 1579. http://dx.doi.org/10.3390/polym12071579.

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The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer o

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Du, Xishihui, Kefa Zhou, Yao Cui, Jinlin Wang, and Shuguang Zhou. "Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 766. http://dx.doi.org/10.3390/ijgi10110766.

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Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-pros

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Yang, Yun Hui, and Yi Ping Ji. "Study on Classification Technology of Wool and Cashmere Based on GA-SVM." Advanced Materials Research 332-334 (September 2011): 1198–201. http://dx.doi.org/10.4028/www.scientific.net/amr.332-334.1198.

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Distinguishing of wool and cashmere is one of the toughest problems in fiber identification area. Support Vector Machine (SVM) was advanced here to classify fibers, and Genetic Algorithm (GA) was used to optimize multi-parameters of SVM simultaneously. Experimental results show that it plays full part of the GA, and accelerates the optimization search of SVM parameters. The model established is of practical significance in identification of wool and cashmere.

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Harafani, Hani. "Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm." SinkrOn 4, no. 2 (2020): 106. http://dx.doi.org/10.33395/sinkron.v4i2.10524.

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Liver disease is an important public health problem. Over the past few decades, machine learning has developed rapidly and it has been introduced for application in medical-related. In this study we propose Support Vector Machine optimization parameter with genetic algorithm to get a higher performance of Root Mean Square Error value of SVM in order to estimate the liver disorder. The experiment was carried out in three stages, the first step was to try the three SVM kernels with different combination of parameters manually, The second step was to try some combination of range parameters in th

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Zhu, Xiufang, Nan Li, and Yaozhong Pan. "Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification." Remote Sensing 11, no. 6 (2019): 734. http://dx.doi.org/10.3390/rs11060734.

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Group intelligence algorithms have been widely used in support vector machine (SVM) parameter optimization due to their obvious characteristics of strong parallel processing ability, fast optimization, and global optimization. However, few studies have made optimization performance comparisons of different group intelligence algorithms on SVMs, especially in terms of their application to hyperspectral remote sensing classification. In this paper, we compare the optimization performance of three different group intelligence algorithms that were run on a SVM in terms of five aspects by using thr

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Fang, Yihui, Xingwei Chen, and Nian-Sheng Cheng. "Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China." Water Supply 17, no. 1 (2016): 52–60. http://dx.doi.org/10.2166/ws.2016.097.

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Estuary salinity predictions can help to improve water safety in coastal areas. Coupled genetic algorithm-support vector machine (GA-SVM) models, which adopt a GA to optimize the SVM parameters, have been successfully applied in some research fields. In light of previous research findings, an application of a GA-SVM model for tidal estuary salinity prediction is proposed in this paper. The corresponding model is developed to predict the salinity of the Min River Estuary (MRE). By conducting an analysis of the time series of daily salinity and the results of simulation experiments, the high-tid

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Yulin, Gong Mingjia Hu Xiaojuan Chen and Yue Sun Changchun University of Science and Technology. "Research on Gesture Based on GA – SVM." Journal of Information and Communication Engineering(JICE) Volume 5, Issue 1 (2019): 268–74. https://doi.org/10.5281/zenodo.4273640.

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Abstract: Surface electromyography (sEMG) is a kind of weak electrical signal generated by muscle activity, which contains information of gesture and is widely used in prosthetic control, rehabilitation and medical treatment. The difference between different motion patterns can be reflected by the different sEMG characteristics, so the recognition of human motion can be studied. Four time-domain features including absolute mean value, waveform length, zero-crossing number and root mean square value were extracted from the double Myo arm-ring data set in Ninapro benchmark database. Classificati

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Awalin, Qonita Ilmi, Ika Hesti Agustin, Alfian Futuhul Hadi, Dafik Dafik, and R. Sunder. "Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms." CAUCHY: Jurnal Matematika Murni dan Aplikasi 9, no. 2 (2024): 320–28. http://dx.doi.org/10.18860/ca.v9i2.29320.

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To categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). CKD is a severe illness in which the kidneys fail to adequately filter blood and perform their normal functions. This study utilized secondary data consisting of patient conditions and health information. Based on references from CKD-related journals, 15 independent variables and one dependent variable were selected from an initial set of 54 variables. To addres

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He, Chao, Junwen Peng, Wenhui Jiang, Chaofan Wang, Junting Li, and Zefu Tan. "A hybrid time series analysis-genetic algorithm-support vector machine model for enhanced landslide prediction." International Journal of Industrial Engineering Computations 16, no. 3 (2025): 785–98. https://doi.org/10.5267/j.ijiec.2025.3.005.

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Landslide prediction is a critical task for ensuring public safety and preventing economic loss in regions prone to such natural disasters. Traditional models for landslide prediction often lack accuracy and precision because of the intricate interactions between various factors that lead to landslide events. To tackle this issue, we introduce an innovative hybrid approach for landslide prediction that combines Time Series Analysis (TSA), Genetic Algorithm (GA), and Support Vector Machine (SVM). TSA decomposes landslide displacement data into trend, seasonal, and residual components, improving

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Xiao, Fang. "Forest Fire Disaster Area Prediction Based on Genetic Algorithm and Support Vector Machine." Advanced Materials Research 446-449 (January 2012): 3037–41. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.3037.

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Forest fire disaster area prediction based on genetic algorithm and support vector machine is presented in the paper.Genetic algorithm is used to select appropriate parameters of support vector machine. Genetic algorithm can obtain the optimal solution by a series of iterative computations.The forest fire disaster area data in Jiangxi Province from 1970 to 1997 are used as our research data. The comparison of the forest fire disaster area forecasting results between the proposed GA-SVM model and the SVM model is given,which indicates that the proposed GA-SVM model has more excellent forest fir

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Xu, Chun Bi, Jing Cheng Liu, and Jun Li. "Forecast Model for Gas Well Productivity Based on GA and SVM." Applied Mechanics and Materials 71-78 (July 2011): 4958–62. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.4958.

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The accurate prediction of gas well productivity is an important task in gas reservoir engineering research. According to the global optimization ability of the genetic algorithm (GA) and the superior regression performance of the support vector machine (SVM), this paper proposed a method based on GA and SVM to improve the prediction accuracy. As the proposed model can reduce the dimensionality of data space and preserve features of gas well productivity, compared with BP neural network model, the proposed GA-SVM model for gas well productivity in practical engineering has higher accuracy and

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Yang, Shouguo, Ruming Huang, Haoxing Liu, and Jalin Li. "Prediction of gas emission in mining face based on GA-PSO-SVM." E3S Web of Conferences 385 (2023): 01012. http://dx.doi.org/10.1051/e3sconf/202338501012.

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In order to prevent the gas from exceeding the limit and accurately and effectively predict the gas emission, this paper puts forward a prediction method of gas emission in mining face based on GA-PSO-SVM. The historical data of a coal mine is analyzed by comprehensively considering five factors that affect the gas emission from the working face. By predicting the gas emission from the test set, the values of MSE, MAE and RMSEP of GA-PSO-SVM model in the return gas concentration prediction are 0.029942, 0.001323 and 0.036378, respectively, and the three indexes are superior to the other three

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Lu, Juan, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, and Bing Huang. "An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes." Complexity 2019 (June 13, 2019): 1–13. http://dx.doi.org/10.1155/2019/3094670.

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It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε-insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (A

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Bhushan, Shashi, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, and Ahed Abugabah. "A Novel Approach to Face Pattern Analysis." Electronics 11, no. 3 (2022): 444. http://dx.doi.org/10.3390/electronics11030444.

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Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combinatio

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Yin, Zhenliang, Xiaohu Wen, Qi Feng, Zhibin He, Songbing Zou, and Linshan Yang. "Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area." Hydrology Research 48, no. 5 (2016): 1177–91. http://dx.doi.org/10.2166/nh.2016.205.

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Accurate estimation of evapotranspiration is vitally important for management of water resources and environmental protection. This study investigated the accuracy of integrating genetic algorithm and support vector machine (GA-SVM) models using climatic variables for simulating daily reference evapotranspiration (ET0). The developed GA-SVM models were tested using the ET0 calculated by Penman–Monteith FAO-56 (PMF-56) equation in a semi-arid environment of Qilian Mountain, northwest China. Eight models were developed using different combinations of daily climatic data including maximum air tem

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Ramdhani, Yudi, Dhia Fauziah Apra, and Doni Purnama Alamsyah. "Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 192. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp192-199.

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Grapes are one of the fruit plants that grow that propagate in certain fields. Grapes can be processed into juice, wine, raisins, and so on. Raisins are dried grapes. Raisins have a distinctive taste and aroma. Raisins are a concentrated and nutritious source of carbohydrates, containing antioxidants, potassium, fiber and iron. To increase the accuracy value, the optimize selection genetic algorithm (GA) is used. This research was conducted modeling using the support vector machine (SVM) and SVM algorithms based on optimize selection GA by using the raisin (raisin varieties) dataset obtained f

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Yudi, Ramdhani, Fauziah Apra Dhia, and Purnama Alamsyah Doni. "Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin." Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin 30, no. 1 (2023): 192–99. https://doi.org/10.11591/ijeecs.v30.i1.pp192-199.

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Grapes are one of the fruit plants that grow that propagate in certain fields. Grapes can be processed into juice, wine, raisins, and so on. Raisins are dried grapes. Raisins have a distinctive taste and aroma. Raisins are a concentrated and nutritious source of carbohydrates, containing antioxidants, potassium, fiber and iron. To increase the accuracy value, the optimize selection genetic algorithm (GA) is used. This research was conducted modeling using the support vector machine (SVM) and SVM algorithms based on optimize selection GA by using the raisin (raisin varieties) dataset obtained f

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Ahmed, Kanwal, Muhammad Imran Nadeem, Dun Li, et al. "Exploiting Stacked Autoencoders for Improved Sentiment Analysis." Applied Sciences 12, no. 23 (2022): 12380. http://dx.doi.org/10.3390/app122312380.

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Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a gen

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Awalullaili, Fithroh Oktavi, Dwi Ispriyanti, and Tatik Widiharih. "KLASIFIKASI PENYAKIT HIPERTENSI MENGGUNAKAN METODE SVM GRID SEARCH DAN SVM GENETIC ALGORITHM (GA)." Jurnal Gaussian 11, no. 4 (2022): 488–98. http://dx.doi.org/10.14710/j.gauss.11.4.488-498.

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Hypertension is an abnormally high pressure that occurs inside the arteries. Hypertension increased by 8.3% from 2013 based on health research in 2018. Some of the factors that cause hypertension include gender, age, salt consumption, cigarette consumption, cholesterol levels and a family history of hypertension. The data in this study are data on normal and hypertensive patients at the Padangsari Health Center for the period of July – December 2021. This study will classify blood pressure with the aim of obtaining the results of the accuracy of the classification of the methods used. The meth

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Wang, Chi Jo, Juing Shian Chiou, and Yu Chia Hu. "Application of Hybrid Algorithm to Real-Time Face Recognition." Applied Mechanics and Materials 278-280 (January 2013): 1309–13. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1309.

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This paper proposed the principal component analysis (PCA) and support vector machine-genetic algorithm (SVM-GA) to the real-time face recognition. The integrated scheme aims to apply the SVM-GA method to improve the validity of PCA based real-time recognition systems. Experimental results show that the proposed method simplifies features effectively and obtains a higher classification accuracy.

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