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Journal articles on the topic 'SVM classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.

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As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting

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Subha, R., and M. Pushpa Rani. "SVM based Iris Classification." International Journal of Computer Sciences and Engineering 6, no. 2 (2018): 321–23. http://dx.doi.org/10.26438/ijcse/v6i2.321323.

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Vaidya, Jaideep, Hwanjo Yu, and Xiaoqian Jiang. "Privacy-preserving SVM classification." Knowledge and Information Systems 14, no. 2 (2007): 161–78. http://dx.doi.org/10.1007/s10115-007-0073-7.

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Gu, Suicheng, and Yuhong Guo. "Learning SVM Classifiers with Indefinite Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 942–48. http://dx.doi.org/10.1609/aaai.v26i1.8293.

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Recently, training support vector machines with indefinite kernels has attracted great attention in the machine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classifications and kernel principal component analysis. We first reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM classification to formulate a joint optimization model. The proposed model has the advantage of making consistent kernel transformations over training and test samples. It can be

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Deepthi, Medechal, Mosali Harini, Pandiri Sai Geethika, Vusirikala Kalyan, and K. Kishor. "Data Classification of Dark Web using SVM and S3VM." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 510–17. http://dx.doi.org/10.22214/ijraset.2023.55643.

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Abstract: There are many issues regarding the dark web structural Type. It also increases the number of cybercrimes like illegal trade, forums, Terrorist activity. By understanding online criminal’s actions are challenging because the data is available in a very great extent amount. In a recent day the Online crimes are increasing all over the world. The data related to different types of frauds and scams, such as phishing schemes, identity theft etc. The data and discussion related to the act of hacking (hacktivist) activities, this often involve political or social causes. In some parts of d

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MISS., NAMRATA N. RADE. "IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 43–47. https://doi.org/10.5281/zenodo.1459102.

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<strong>Nowadays,various approaches of texture classification have been developed which works on acquired image features and separate them into different classes by using a specific classifier . This paper gives a state - of - the - art texture classification technique called Speeded up Robust Features (SURF) with SVM (Support Vector Machine) classifier. In this concept,image data representation is accomplished by capturing feature s in the form of key - points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out. This me

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Bansal, Esha, and Anupam Bhatia. "Kernel’s Impact on SVM Classification." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (2017): 359–62. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0238.

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Al-Khalidy, Joanne H., and Raid R. Al-Ne’ma. "Breast Tumor Classification Using SVM." Tikrit Journal of Engineering Sciences 21, no. 1 (2013): 43–49. http://dx.doi.org/10.25130/tjes.21.1.06.

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Although there are several techniques that have been used to differentiate between benign andmalignant breast tumor lately, support vector machines (SVMs) have been distinguished as one ofthe common method of classification for many fields such as medical diagnostic, that it offersmany advantages with respect to previously proposed methods such as ANNs. One of them is thatSVM provide a higher accuracy, another advantage that SVM reduces the computational cost,and it is already showed good result in this work.In this paper, a Support Vector Machine for differentiation Breast tumor was presented

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Ivanova, Vanya, Tasho Tashev, and Ivo Draganov. "DDoS Attacks Classification using SVM." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (February 9, 2022): 1–11. http://dx.doi.org/10.37394/23209.2022.19.1.

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In this paper two types of classifiers of Distributed Denial of Service (DDoS) attacks, based on Support Vector Machines, are presented – a binary and a multiclass one. They use numerical samples, aggregated from packet switched network connections records, captured between attacking machines, most typically IoT bots and a victim machine. Ten of the most popular DDoS attacks are studied and represented as either 10- or 8-feature vectors. Detection rate and classification accuracy is being measured in both cases, along with lots of other parameters, such as Precision, Recall, F1-measure, traini

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Reynolds, Evan, Brian Callaghan, and Mousumi Banerjee. "SVM–CART for disease classification." Journal of Applied Statistics 46, no. 16 (2019): 2987–3007. http://dx.doi.org/10.1080/02664763.2019.1625876.

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Huang, Mei-Ling, Yung-Hsiang Hung, W. M. Lee, R. K. Li, and Bo-Ru Jiang. "SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/795624.

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Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing

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Priyanka, Dewangan, and Dedhe Vaibhav. "Soil Classification Using Image Processing and Modified SVM Classifier." International Journal of Trend in Scientific Research and Development 2, no. 6 (2019): 504–7. https://doi.org/10.31142/ijtsrd18489.

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Recently the use of soil classification has gained more and more importance and recent direction in research works indicates that image classification of images for soil information is the preferred choice. Various methods for image classification have been developed based on different theories or models. In this study, three of these methods Maximum Likelihood classification MLC , Sub pixel classification SP and Support Vector machine SVM are used to classify a soil image into seven soil classes and the results compared. MLC and SVM are hard classification methods but SP is a soft classificat

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Chao, Chih-Feng, and Ming-Huwi Horng. "The Construction of Support Vector Machine Classifier Using the Firefly Algorithm." Computational Intelligence and Neuroscience 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/212719.

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The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In expe

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Sujitha, R., and B. Paramasivan. "Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification." Computers, Materials & Continua 70, no. 1 (2022): 1557–72. http://dx.doi.org/10.32604/cmc.2022.019323.

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Shanmugapriya, S., and P. Rutravigneshwaran. "Adaptive Rhea Optimization Enhanced CNN SVM Framework (ROC-SVM) for Precise MRI Based Brain Tumor Classification." Indian Journal Of Science And Technology 18, no. 24 (2025): 1939–52. https://doi.org/10.17485/ijst/v18i24.938.

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Background: Brain tumor classification in the background of MRI remains a critical challenge: it suffers from intensity profiles overlap, irregular tumor shapes, a large variability among patients, etc. Deep learning and machine learning approaches, are also prone to fails in trade off between classification accuracy, feature reliability and computational efficiency making it hard for their deployment in the clinical practice, particularly in real time or resource scarcity environments. Objectives: This work proposes a biologically inspired, cost effective and classification precise model to d

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Naofal Hakim, Mochamad Agusta, Adiwijaya Adiwijaya, and Widi Astuti. "Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3393. http://dx.doi.org/10.11591/ijece.v11i4.pp3393-3402.

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Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. There

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Mochamad, Agusta Naofal Hakim, Adiwijaya, and Astuti Widi. "Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3393–402. https://doi.org/10.11591/ijece.v11i4.pp3393-3402.

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Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. There

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Roxas, Edison A., Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Elmer P. Dadios, and Argel A. Bandala. "SVM Compound Kernel Functions for Vehicle Target Classification." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 654–59. http://dx.doi.org/10.20965/jaciii.2018.p0654.

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The focus of this paper is to explore the use of kernel combinations of the support vector machines (SVMs) for vehicle classification. Being the primary component of the SVM, the kernel functions are responsible for the pattern analysis of the vehicle dataset and to bridge its linear and non-linear features. However, the choice of the type of kernel functions has characteristics and limitations that are highly dependent on the parameters. Thus, in order to overcome these limitations, a method of compounding kernel function for vehicle classification is hereby introduced and discussed. The vehi

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Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coeff

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Rahayu, Sarwati, Andi Nugroho, Erwin Dwika Putra, Mariana Purba, Hadiguna Setiawan, and Sulis Sandiwarno. "Comparison of HSV, LAB and YCrCb Color Feature Extraction Results in the SVM Algorithm for Bird Species Classification." JSAI (Journal Scientific and Applied Informatics) 6, no. 3 (2023): 451–56. http://dx.doi.org/10.36085/jsai.v6i3.5920.

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The classification of bird species is a problem often faced by ornithologists, and has been considered scientific research since antiquity. This study aims to evaluate the results of color feature extraction including HSV, LAB and YCrCb against the results of the SVM classification. In addition, the results of this study are useful to determine the performance of color feature extraction that is suitable for bird species classification. The dataset used was 22,617 bird species images. Based on experimental results, the effect of HSV on the SVM classification caused a decrease in accuracy by -0

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Zhao, Yan Ling, Xiao Shi Zheng, Guang Qi Liu, and Na Li. "Image Texture Classification Based on LS-SVM." Applied Mechanics and Materials 182-183 (June 2012): 869–72. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.869.

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LS-SVM (Least Squares Support Vector Machine) is simple and has a good ability of non-linear regression. As inputs of LS-SVM, DC-Energy-Ratio and Deviation of image samples are extracted first. Output of LS-SVM is the current texture classification. The results show that LS-SVM classifies images accurately by training the proposed two features.

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S, Omprakash, and Ravichandran M. "Indian Journal of Science and Technology." Indian Journal of Science and Technology 13, no. 17 (2020): 1703–13. https://doi.org/10.17485/IJST/v13i17.20.

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Abstract <strong>Background:</strong> Medical data classification has become a hot research domain in data mining, but still it faces the increased classification accuracy issues. <strong>Methods/Statistical Analysis:</strong> Novel Hidden Markov Model based Support Vector Machine (HMM-SVM) is proposed to classify and predict Coronary Artery Disease (CAD). The features are extracted using HMM, and normalized using SVM. Feature Extraction assist the classification algorithm to get better results. HMM-SVM performs classification by extracting the features of Z-AlizadehSani dataset

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Raju, K. Hari Prasada, N. Sandhya, and Raghav Mehra. "Supervised SVM Classification of Rainfall Datasets." Indian Journal of Science and Technology 10, no. 15 (2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i15/106115.

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Anshori, Mochammad, Wayan Firdaus Mahmudy, and Ahmad Afif Supianto. "Classification Tuberculosis DNA using LDA-SVM." Journal of Information Technology and Computer Science 4, no. 3 (2019): 233. http://dx.doi.org/10.25126/jitecs.201943113.

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Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA.

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Kulkarni, Atul, and Debajyoti Mukhopadhyay. "SVM Classifier Based Melanoma Image Classification." Research Journal of Pharmacy and Technology 10, no. 12 (2017): 4391. http://dx.doi.org/10.5958/0974-360x.2017.00808.3.

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Ali, Shawkat, and Kate A. Smith. "Kernal Width Selection for SVM Classification." International Journal of Data Warehousing and Mining 1, no. 4 (2005): 78–97. http://dx.doi.org/10.4018/jdwm.2005100104.

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Vaidehi K. and Manivannan R. "Automated Math Symbol Classification Using SVM." International Journal of e-Collaboration 18, no. 2 (2022): 1–14. http://dx.doi.org/10.4018/ijec.304037.

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Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features ext

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Maheshwari, Akhil, Dolly Sharma, Ritik Agarwal, Shivam Singh, and Loveleen Kumar. "Gender Classification using SVM With Flask." International Journal of Electrical, Electronics and Computers 6, no. 3 (2021): 43–46. http://dx.doi.org/10.22161/eec.63.6.

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Reddy, Gaddam Akhil, and Dr B. Indira Reddy. "Classification of Spam Text using SVM." Journal of University of Shanghai for Science and Technology 23, no. 08 (2021): 616–24. http://dx.doi.org/10.51201/jusst/21/08437.

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The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Ma

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Jing Peng, D. R. Heisterkamp, and H. K. Dai. "LDA/SVM driven nearest neighbor classification." IEEE Transactions on Neural Networks 14, no. 4 (2003): 940–42. http://dx.doi.org/10.1109/tnn.2003.813835.

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K, GOKILA, JANANIE K R, MADHU BALA C, and GOMATHY NAYAGAM M. "Content Based Video Classification Using SVM." Special Issue 5, Special Issue 1 (2019): 468–77. http://dx.doi.org/10.23883/ijrter.conf.20190322.060.iwtaz.

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Kim, Daehak, KwangSik Oh, and Jooyong Shim. "On Line LS-SVM for Classification." Communications for Statistical Applications and Methods 10, no. 2 (2003): 595–601. http://dx.doi.org/10.5351/ckss.2003.10.2.595.

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Huh, Myung-Hoe, and Hee-Man Park. "Visualizing SVM Classification in Reduced Dimensions." Communications for Statistical Applications and Methods 16, no. 5 (2009): 881–89. http://dx.doi.org/10.5351/ckss.2009.16.5.881.

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Ghosh, Sayantani, and Samir Kumar Bandyopadhyay. "SVM Classifier for Human Gender Classification." International Journal of Applied Research on Information Technology and Computing 7, no. 2 (2016): 100. http://dx.doi.org/10.5958/0975-8089.2016.00010.5.

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Wang, Qi, Yingjie Tian, and Dalian Liu. "Adaptive FH-SVM for Imbalanced Classification." IEEE Access 7 (2019): 130410–22. http://dx.doi.org/10.1109/access.2019.2940983.

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Zhang, Xiaopeng, and Yuan Chai. "Transformer Fault Diagnosis Based on BOA Optimized SVM." Journal of Big Data and Computing 2, no. 4 (2024): 97–100. https://doi.org/10.62517/jbdc.202401415.

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To improve the classification accuracy of transformers in different operating states, a BOA-SVM classification method for transformers in different operating states is proposed. Due to the excellent global search capability of the BOA method, in order to address the issue of the significant impact of key parameters on transformer operation status classification results in the SVM method, BOA is used to determine the key parameters of the SVM method, and the BOA-SVM model is applied to transformer operation status classification. In the end, the BOA-SVM fault diagnosis model has higher classifi

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Karungaru, Stephen, Lyu Dongyang, and Kenji Terada. "Vehicle Detection and Type Classification Based on CNN-SVM." International Journal of Machine Learning and Computing 11, no. 4 (2021): 304–10. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1052.

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In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves

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Yoo, Seungsoo, Jaeduk Yoo, Soeun Heo, and Sun-Yong Kim. "ResNet/SVM-Based GNSS Jamming Classification Scheme." Journal of Korean Institute of Communications and Information Sciences 48, no. 12 (2023): 1589–92. http://dx.doi.org/10.7840/kics.2023.48.12.1589.

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Emilia Ayu Wijayanti, Tania Rahmadanti, and Ultach Enri. "Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga." Generation Journal 5, no. 2 (2021): 102–8. http://dx.doi.org/10.29407/gj.v5i2.16075.

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Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Mac

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Dewangan, Priyanka, and Vaibhav Dedhe. "Soil Classification Using Image Processing and Modified SVM Classifier." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 504–7. http://dx.doi.org/10.31142/ijtsrd18489.

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R, Thiruven Gatanadhan. "Speech/music classification using PLP and SVM." International Journal of Engineering and Computer Science 8, no. 02 (2019): 24469–72. http://dx.doi.org/10.18535/ijecs.v8i02.4277.

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Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extracti

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Ammar Tahir and Adil Pervaiz. "Hand written character recognition using SVM." Pacific International Journal 3, no. 2 (2020): 59–62. http://dx.doi.org/10.55014/pij.v3i2.98.

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Classification is one of the most important tasks for different applications such as text categorization, tone recognition, image classification, microarray gene expression, proteins structure predictions, data Classification, etc. Hand-written digit classification is a process that interprets handwritten digits by machine. There are many techniques used for HRC like neural networks and k-nearest neighbor (KNN).In this paper, a novel supervised learning technique, Support Vector Machine (SVM), is applied to blur images data. SVM is a powerful machine model use for classification for two or mor

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Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "Semi-Supervised Learning for Classification with Uncertainty." Advanced Materials Research 433-440 (January 2012): 3584–90. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3584.

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Support vector machine (SVM) is a general and powerful learning machine, which adopts supervised manner. However, for many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are very expensive to be obtained. Therefore, semi-supervised learning emerges as the times require. At present, the combination of SVM and semi-supervised learning (S3VM) has attracted more and more attentions. In general, S3VM deals with problems with small training sets and large working sets. When the training set is large relative to the working

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Chen, Yi, Yu Hui Li, Fan Zhang, and Feng Zhou. "Vehicle Classification Algorithm Based on Fuzzy SVM Models." Applied Mechanics and Materials 444-445 (October 2013): 841–48. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.841.

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As a typical binary classifier, its an inseparable sample problem about the Support Vector Machine (SVM) when processing the classification of the multi-class vehicle models. Since the SVM can not estimate the effect size of the samples classification accurately, and then reduces the classification generalization ability. In this paper, a fuzzy Support Vector Machine (FSVM) classification algorithm is applied to vehicle classification. According to the difference of the contribution which the vehicle characteristics make to the classification, the appropriate degree of membership is given, and

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Guizani, Douraied, Erika Buday-Bódi, János Tamás, and Attila Nagy. "An advanced classification method for urban land cover classification." Acta Agraria Debreceniensis, no. 1 (June 3, 2024): 51–57. http://dx.doi.org/10.34101/actaagrar/1/13652.

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This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. Th

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R, Vijaya Arjunan. "ECG SIGNAL CLASSIFICATION BASED ON STATISTICAL FEATURES WITH SVM CLASSIFICATION." International Journal of Advances in Signal and Image Sciences 2, no. 1 (2016): 5. http://dx.doi.org/10.29284/ijasis.2.1.2016.5-10.

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Deng, Geng, Yaoguo Xie, Xindong Wang, and Qiang Fu. "Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1481–94. http://dx.doi.org/10.3233/jifs-202155.

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Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essent

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Xie, Fu Dong, Wei Min Yang, Dao Hong Qiu, and Yi Li. "Stability Analysis of Surrounding Rock Based on QGA-SVM." Advanced Materials Research 1065-1069 (December 2014): 199–203. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.199.

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In order to analyze the stability of surrounding rock accurately and effectively, a rock classification method based on QGA (quantum genetic algorithm)-SVM (support vector machine) is put forward. QGA was used for global search in the solution space to optimize the kernel function parameters of SVM. And this method improved the classification accuracy of SVM in rock classification. Finally, a rock classification model based on QGA-SVM was established and applied to practical engineering. The result shows that the improved SVM has a higher accuracy in stability analysis of surrounding rock.

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Dr., R. Manjunatha Prasad, R. Manjunatha Prasad Dr., and B. S. Roopa. "SVM Classifiers at it Bests in Brain Tumor Detection using MR Images." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2410–15. https://doi.org/10.31142/ijtsrd18372.

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This paper presents some case study frameworks to limelight SVM classifiers as most efficient one compared to existing classifiers like Otsu, k means and fuzzy c means. In general, Computed Tomography CT and Magnetic Resonance Imaging MR are more dominant imaging technique for any brain lesions detection like brain tumor, Alzheimer's disease and so on. MR imaging takes a lead technically for imaging medical images due to its possession of large spatial resolution and provides better contrast for the soft tissues like white matter WM , gray matter GM and cerebrospinal fluid CSF . The usual

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Hussain, Zahraa Faiz, Hind Raad Ibraheem, Mohammad Alsajri, et al. "A new model for iris data set classification based on linear support vector machine parameter's optimization." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 1079. http://dx.doi.org/10.11591/ijece.v10i1.pp1079-1084.

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Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary proble

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