Relevant bibliographies by topics / SVM-GA

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'SVM-GA'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "SVM-GA"

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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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Dissertations / Theses on the topic "SVM-GA"

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Mizaku, Alda. "Biomolecular feature selection of colorectal cancer microarray data using GA-SVM hybrid and noise perturbation to address overfitting." Diss., Online access via UMI:, 2009.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Bioengineering, Biomedical Engineering, 2009.<br>Includes bibliographical references.
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Chia, Yen Yee. "Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14394/.

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This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. ca
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Okuyucu, Cigdem. "Semantic Classification And Retrieval System For Environmental Sounds." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615114/index.pdf.

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The growth of multimedia content in recent years motivated the research on audio classification and content retrieval area. In this thesis, a general environmental audio classification and retrieval approach is proposed in which higher level semantic classes (outdoor, nature, meeting and violence) are obtained from lower level acoustic classes (emergency alarm, car horn, gun-shot, explosion, automobile, motorcycle, helicopter, wind, water, rain, applause, crowd and laughter). In order to classify an audio sample into acoustic classes, MPEG-7 audio features, Mel Frequency Cepstral Coefficients
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YU, Ting-Chun, and 游廷鈞. "Forecasting MLB Playoff Teams Using GA-SVM." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8sdafq.

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碩士<br>臺北市立大學<br>資訊科學系碩士在職專班<br>106<br>This research studies the Major League Baseball (MLB) Playoffs forecast. In general, the MLB postseason related records are extremely numerous and complex, to predict the outcome is very difficult, especially the invalid records will also affect the accuracy, computing time, and performance of the classifier. Therefore, we proposed the method named genetic algorithm- support vector machine (GA-SVM) as the prediction method. Generally, GA is the common filtering method, it can effectively filter to remove the invalid features, and the remaining of the valid
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dai, yu-hong, and 戴宇宏. "Incremental clustering with GA, SVM, and FCM methods." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/61605178574856574274.

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碩士<br>中華大學<br>資訊管理學系(所)<br>96<br>With explosion of information, it is very difficult to manage documents. How to efficiently find useful information in large information is very important. Clustering algorithm is a kind of technology to find characteristics of information and relationship to help manage documents. This study proposes a method--combination of SVM classification method and fuzzy clustering method based on genetic algorithm. SVM classification method based on genetic algorithm is used to classify incoming document to see if it belongs to the existing classes. Fuzzy clustering met
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Kao, Hsi-Chun, and 高璽鈞. "GA-SVM applied to the fall detection system." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/a6jc38.

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碩士<br>臺北市立大學<br>資訊科學系<br>105<br>With the advancement of modern medical techniques, the average life expectancy of humans has been increasing year by year and the older population has also risen. Therefore, the care for the elderly has gradually begun to pay attention. In addition to the health care, and how to avoid accident and injury of the elderly in the daily life is also extremely important. According to the accident statistics, the cause of the accidental included the traffic accidents, and the fall for the elderly is also the main cause for accidental death. Because the fall usually cau
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YANG, JOU-HSUAN, and 楊柔軒. "Feasibility of using GA-SVM On FOREX Forecasting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/m4v6em.

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碩士<br>國立彰化師範大學<br>企業管理學系<br>107<br>The amount of Taiwan’s export constitutes on average around more than 50% of GDP in recent years. Therefore, an accurate forecasting of foreign exchange rate (FOREX) is very critical to the sustainability of the firms in Taiwan. Although the FOREX forecasting is a very important issue in financial literature and numerous previous works has put endeavor into the search of best model for the FOREX forecasting. However, the results are still mixed. This might be attributed to the variations of modeling techniques. More recently, GA-SVM has evolved as a preferred
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Huang, Chun-Ying, and 黃俊穎. "An Application of GA-SVM in the FX Rate Forecast." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/00724081183966389360.

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碩士<br>國立彰化師範大學<br>企業管理學系<br>102<br>Because of advances in computer network communication technology, the electronic trading market has developed rapidly in recent years, and has created a low cost and convenient new era of financial transactions, and also has enabled the international capital to circulate rapidly. However, the difference between the exchange rate regimes by the countries of the world makes the volatility of the exchange rate is more aggravated and increase the risk of exchange rate. So how to draft reactive strategies through accurate exchange rate forecast to reduce the risk
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Li, Yen-Chin, and 李彥瑾. "On the Prediction of Company Failures Using GA Incorporation with SVM." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40546356777643553102.

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碩士<br>國立彰化師範大學<br>企業管理學系<br>98<br>Prediction of firm failure is a critical issue in the credit risk management literature. Numerous researchers have put endeavors into this area and have been generated fruitful results in the past decades. However, the decision regarding the most appropriate model and the most critical variables should be included in the model are still inconclusive. Recently, data mining techniques have evolved as a preferred approach for early warning models. The objective of this study is two-folds: first, to investigate the most influential factors which lead to financ
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Chen, Chen-Lung, and 陳震隆. "An Application of GA-SVM in the Prediction of Cotton Price." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/78297663648721813564.

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碩士<br>國立彰化師範大學<br>企業管理學系國際企業經營管理<br>101<br>Cotton is an very important materials of textile industry in Taiwan, but it is purely rely heavily from import. The highly volatility of cotton price may results in big losses and further jeopardize the price competiveness of Taiwanese firms. Thus, accurately prediction in the cotton price is an critical issue to the sustainable growth of Taiwanese textile industry. More recently, data mining techniques has become a preferred prediction approach to both academia and industry. The objective of this study is to investigate whether an adoption of a d
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Book chapters on the topic "SVM-GA"

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Land, Walker H., and J. David Schaffer. "Hybrid GA-SVM-Oracle Paradigm." In The Art and Science of Machine Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18496-4_5.

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Banpavichit, Sasin, Waree Kongprawechnon, and Kanokwate Tungpimolrut. "Cardiac Auscultation with Hybrid GA/SVM." In The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37371-8_22.

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Raja Sekar, M., and N. Sandhya. "Study of Galaxy Evolution Through GA-SVM." In Smart Intelligent Computing and Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1921-1_60.

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Xu, Zheng, Qinjun Zhao, Yang Zhang, Yuhua Zhang, and Tao Shen. "Apple Grading Method Based on GA-SVM." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8462-6_9.

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Yang, Yuli, Zhi Li, and Yanfeng Wang. "Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3415-7_29.

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Jaffar, M. Arfan, Ayyaz Hussain, Fauzia Jabeen, M. Nazir, and Anwar M. Mirza. "GA-SVM Based Lungs Nodule Detection and Classification." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10546-3_17.

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Verma, Vishal, Sandeep Kumar Verma, Satish Kumar, Alka Agrawal, and Raees Ahmad Khan. "Diabetes Classification and Prediction Through Integrated SVM-GA." In Recent Advances in Computational Intelligence and Cyber Security. CRC Press, 2024. http://dx.doi.org/10.1201/9781003518587-8.

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Sung, Ki-seok, and Sungzoon Cho. "GA SVM Wrapper Ensemble for Keystroke Dynamics Authentication." In Advances in Biometrics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11608288_87.

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He, Jian-Biao, Hua-Min Zhang, Jun Liang, Ou Jin, and Xi Li. "Paper Currency Denomination Recognition Based on GA and SVM." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47791-5_41.

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Tang, Hong, Huaqiang Zhang, and Xiujuan MA. "Offshore Wind Speed Load Predicting Based on GA-SVM." In Lecture Notes in Electrical Engineering. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4847-0_55.

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Conference papers on the topic "SVM-GA"

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Jiang, Wanghua, and Xueliang Fan. "Intelligent hydrogen flame recognition method: Comparisons among SVM, GA-SVM and PSO-SVM." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934252.

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Li, Hongguo, Jingyao Wang, and Wenzhi Hou. "GA-SVM Optimization Algorithm for Tracking Moving Objects." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699075.

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Lai, Li, Chengyao Li, and Yuhao Zhang. "Research on Rainfall Prediction Based on GA-SVM Model." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10698810.

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Singla, Priyanka, and Himmat Rathore. "Innovative Message Routing for Next Generation Transportation System Using GA-Based SVM." In 2024 34th International Telecommunication Networks and Applications Conference (ITNAC). IEEE, 2024. https://doi.org/10.1109/itnac62915.2024.10815246.

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Ariawan, Sandy, Arvind Kumar, Chinthala Kumara Swamy, V. Divya, V. Manikandan, and S. Rukmani Devi. "Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA Model with Feature Analysis." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690879.

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Yousef, Maria, Ala M. Abu-Samaha, and Sana Khawaj. "GA-SVM Hybrid Approach for Optimizing Feature Selection in Chronic Kidney Disease Diagnosis." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013723.

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Chen, Wei, and Hui-mei Yuan. "An improved GA-SVM algorithm." In 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2014. http://dx.doi.org/10.1109/iciea.2014.6931525.

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Kour, Jaspreet, M. Hanmandlu, and A. Q. Ansari. "Online signature verification using GA-SVM." In 2011 IEEE International Conference on Image Information Processing (ICIIP). IEEE, 2011. http://dx.doi.org/10.1109/iciip.2011.6108923.

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Wang, Weiwei. "Parametric Model Based on GA and SVM." In Third International Conference on Natural Computation (ICNC 2007) Vol V. IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.541.

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Zadeh, A. E., M. Dehghan, and S. A. Seyedin. "Modulation identification using GA-SVM and WPD." In the 3rd international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1292331.1292366.

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