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Journal articles on the topic 'MSVM'

Author: Grafiati

Published: 11 September 2023

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1

Indrawan, Gede, Heri Setiawan, and Aris Gunadi. "Multi-class SVM Classification Comparison for Health Service Satisfaction Survey Data in Bahasa." HighTech and Innovation Journal 3, no. 4 (December 1, 2022): 425–42. http://dx.doi.org/10.28991/hij-2022-03-04-05.

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This study aimed to compare the Multi-class Support Vector Machine (MSVM) classification with the One-versus-One (OvO) and One-versus-Rest (OvR) approaches using unigram and bigram features. The study used the service satisfaction survey report of Denpasar public health centers by the Center for Public Health Innovation (CPHI), Medical School, Udayana University. As Bali is known as the world's main tourism destination, it is important to know about its supporting public health service through its representative capital city, Denpasar. Moreover, this study laid the foundation for the classification process using the available methods to fit in Indonesian health service satisfaction survey data, which assists in making decisions to improve health services. Since Bali is one of the provinces in Indonesia and all of those provinces refer to the same national regulation, health service satisfaction survey data that is in the Indonesian language (Bahasa) should have the same aspects, like category, priority, word-related matters (including abbreviations, acronyms, terminology), etc. that overall make it unique and need specific processing. That work was considered a contribution since there is no such study to the best of the author's knowledge and the foundation would be useful as a part of the future vision for the integrated system of Indonesian health big data. Since in reality, satisfaction survey data tends to be unbalanced, this study also compares the developed models using unigram and bigram features without and with feature selection (FS). Those features were then processed using the OvO MSVM and OvR MSVM models. k-fold cross-validation was used to divide training data and testing data and, at the same time, validate the models. Through experiments without and with FS, the OvO MSVM and OvR MSVM models with unigram features had better performance in general than the same models with bigram features. Without FS and with unigram features, comparable differences were found where the OvO MSVM model was slightly better on accuracy and precision, while the OvR MSVM model was slightly better on recall and the F1score. Without FS and with bigram features, comparable differences were also found, where the OvR MSVM model had slightly better performance than the OvO MSVM model. With FS and with unigram and bigram features, the OvR MSVM model had better performance in general than the OvO MSVM model. Doi: 10.28991/HIJ-2022-03-04-05 Full Text: PDF

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Chen, Xiao Yun, Xian Fu Chen, Shao Quan Zhang, and Wen Bin Zhang. "Classification Moving Vehicle Based on Multisensor Data Using Fusion of Multi-Class SVMs Methods." Advanced Materials Research 945-949 (June 2014): 1978–81. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1978.

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In this paper, we propose a special Multi-class SVMs (MSVM) data fusion strategy which is applied to classify vehicle based on multiple pavement structural strain time histories. The centralized and distributed fusion strategies are applied to combine information from several data sources. In the centralized strategy, all information from several data sources is centralized and combined to construct an input space. Then a MSVM classifier is trained. In distributed schemes, the individual data sources are processed separately and modeled by using the MSVM. Then new data fusion strategies are used to combine the information from the individual MSVM to acquire the final classification outputs. Two popular Multi-class SVMs algorithms (One-against-all OAA, One-against-one OAO) are used to construct classifier based on aforementioned two fusion strategies, respectively. The results are compared between SVM-based fusion approach and single data source SVM using two MSVM algorithms, respectively. The result shows this SVM-based fusion approach significantly improves the results of classification accuracy and robustness. The proposed Multisensor data fusion methods can also be applied in other fields.

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Zhang, Shuai. "Quality Diagnosis in Dynamic Process Based on Multi-Feature." Advanced Materials Research 945-949 (June 2014): 1293–96. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1293.

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Recognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. Based on the analysis of the quality abnormal patterns in a dynamic process, a novel method based on multi-feature of quality abnormal patterns by using a multi-SVM (MSVM) was proposed. The simulation results indicate that the recognition accuracies of the MSVM classifiers with the different features are quite different. It is shown that this MSVM model with suitable features can increase the recognition accuracy.

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Renukalatha, S., and K. V. Suresh. "CLASSIFICATION OF GLAUCOMA USING SIMPLIFIED-MULTICLASS SUPPORT VECTOR MACHINE." Biomedical Engineering: Applications, Basis and Communications 31, no. 05 (September 9, 2019): 1950039. http://dx.doi.org/10.4015/s101623721950039x.

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Detection and diagnosis of glaucoma disease of eye fundus images at early stage is very important as this disorder leads to complete loss of vision if ignored. Usually, 80–90% of glaucoma cases are analyzed manually by ophthalmologists. As the manual analysis varies from one expert to other, diagnosis cannot be effective. Hence, there is a need for automatic assessment of glaucoma disease using computer aided diagnosis (CAD). Many researchers have devised several CAD techniques for glaucoma analysis using various classification techniques. However, most of the classifiers are efficient only for two level classification to detect whether disease is glaucoma or not. But, glaucoma disease has several stages and demands multilevel approaches with high degree of classification accuracy. Among several multiclass methods, literature suggests multiclass support vector technique (MSVM) as a better performing statistical classifier. However, many MSVMS suffer from data loss during training phase. To address this issue, a robust hybrid classification approach consisting of Naïve Bayes binary classifier in the first stage and simplified multiclass support vector machine (Sim-MSVM) in the second stage is proposed in this paper.

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Liu, Yumin, and Haofei Zhou. "MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions." Journal of Systems Science and Information 2, no. 5 (October 25, 2014): 473–80. http://dx.doi.org/10.1515/jssi-2014-0473.

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AbstractRecognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. As the dynamic data from a quality abnormal process is linearly inseparable, the recognition efficiency of a support vector machine (SVM) model mainly depends on the selection of the kernel functions and the optimizing of their parameters. Based on the analysis of the quality abnormal patterns in a dynamic process, this paper presents a recognition framework of quality abnormal patterns by using a multi-SVM (MSVM). For the different quality abnormal patterns, the simulation results indicate that the recognition accuracies of the MSVM classifiers with the selected kernel functions are quite different. A MSVM recognition model for quality abnormal patterns in a dynamic process is proposed by the kernel functions being of high accuracies. It is shown that this MSVM model with suitable kernel functions can increase the recognition accuracy.

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Kaka, Jhansi Rani, and K. Satya Prasad. "Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification." Security and Communication Networks 2022 (January 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/7275433.

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Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

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Topannavar, Preeti Sadanand, and Dinkar M. Yadav. "An effective feature selection using improved marine predators algorithm for Alzheimer’s disease classification." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5126. http://dx.doi.org/10.11591/ijece.v13i5.pp5126-5134.

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<span lang="EN-US">Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the <a name="_Hlk134426295"></a>deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.</span>

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Zhang, Min, and Wenming Cheng. "Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/382395.

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Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.

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Jiang, Hong, Xi Chen, Bai Lin Liu, and Yun Qing Liu. "Breast Tumor Recognition Based on Multiple Support Vector Machine." Advanced Materials Research 490-495 (March 2012): 252–56. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.252.

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In order to solve unfixed size and individual difference with the breast tumor, this paper provides a method of Multi-Support Vector Machine (MSVM) for breast tumor recognition. We take Support Vector Machine (SVM) on the eight direction of bump area to generate vector classifier and select Gauss kernel function as kernel function. The breast tumor recognition accuracy can reach 97.3% when σ=30. The experiment shows that the application of MSVM in breast tumor recognition can achieve good result, and provide the reliable basis for further medical diagnosis.

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Liu, Yu Min, Hao Fei Zhou, and Shuai Zhang. "A MSVM Quality Pattern Recognition Model for Dynamic Process." Applied Mechanics and Materials 433-435 (October 2013): 555–61. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.555.

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Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition model of quality pattern in dynamic process using multi-kernel function according to the experiment results.

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Liu, Xiaoguang, Lu Shi, Cong Ye, Yangyang Li, and Jing Wang. "Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data." Bioengineering 10, no. 9 (August 31, 2023): 1027. http://dx.doi.org/10.3390/bioengineering10091027.

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A person’s present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%.

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Martins, Maria, Cristina Santos, Lino Costa, and Anselmo Frizera. "Feature reduction with PCA/KPCA for gait classification with different assistive devices." International Journal of Intelligent Computing and Cybernetics 8, no. 4 (November 9, 2015): 363–82. http://dx.doi.org/10.1108/ijicc-04-2015-0012.

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Purpose – The purpose of this paper is to propose a gait analysis technique that aims to identify differences and similarities in gait performance between three different assistive devices (ADs). Design/methodology/approach – Two feature reduction techniques, linear principal component analysis (PCA) and nonlinear kernel-PCA (KPCA), are expanded to provide a comparison of the spatio-temporal, symmetrical indexes and postural control parameters among the three different ADs. Then, a multiclass support vector machine (MSVM) with different approaches is designed to evaluate the potential of PCA and KPCA to extract relevant gait features that can differentiate between ADs. Findings – Results demonstrated that symmetrical indexes and postural control parameters are better suited to provide useful information about the different gait patterns that total knee arthroplasty (TKA) patients present when walking with different ADs. The combination of KPCA and MSVM with discriminant functions (MSVM DF) resulted in a noticeably improved performance. Such combination demonstrated that, with symmetric indexes and postural control parameters, it is possible to extract with high-accuracy nonlinear gait features for automatic classification of gait patterns with ADs. Originality/value – The information obtained with the proposed technique could be used to identify benefits and limitations of ADs on the rehabilitation process and to evaluate the benefit of their use in TKA patients.

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Guo, Ming Wei, Shi Hong Ni, and Jia Hai Zhu. "Diagnosing Intermittent Faults to Restrain BIT False Alarm Based on EMD-MSVM." Applied Mechanics and Materials 105-107 (September 2011): 729–32. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.729.

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Intermittent fault is an important factor causing built-in test(BIT) false alarm. Diagnosing intermittent fault is an important approach to restrain BIT false alarms. This paper proposes a intermittent faults diagnostic methods based on empirical mode decomposition (EMD) and multiple support vector machine (MSVM). Firstly, the EMD method is used to decompose the original signal into a number of intrinsic mode function (IMF), the auto-regressive (AR) model of each IMF component is established. The AR model parameters and the variance of remnant are regarded as the feature vectors, are input to MSVM classifier, so the working conditions and faults are classified. The experimental results show that the BIT false alarm caused by intermittent fault can be effectively reduced by this proposed method.

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He, Ai Xiang, Yong Qiang Wang, and Yan Rong Shi. "An Approach to Subtype Recognition and Extraction of Informative Genes for SRBCT." Applied Mechanics and Materials 40-41 (November 2010): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.619.

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An approach to tumor molecular classification based on their gene expression profiles was presented. A new measure known as Between-groups to within-groups sums of squares ratio (BSS/WSS) was used as the criterion of screening predictive genes for SRBCT subtype recognition. The 152 genes were chosen by this criterion and formed the feature set whose subsets would be used to create MSVM models to identify the subtypes. The trained MSVM based on the top 25 genes ranked by BSS/WSS was able to achieve 100% accuracy on the training and blind test dataset. Then this subset was analyzed by the dissimilarity distance to remove its redundancy. As a result, the 15 genes were retained with the same accuracy as the subset of 25 genes and were regarded as the final subset. Comparison with other methods demonstrates efficiency and feasibility of the method and the predictive models proposed in this work.

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Dong, Shaojiang, Dihua Sun, Baoping Tang, Zhenyuan Gao, Wentao Yu, and Ming Xia. "A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/293878.

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A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

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Rustam, Zuherman, Arfiani Arfiani, and Jacub Pandelaki. "Cerebral infarction classification using multiple support vector machine with information gain feature selection." Bulletin of Electrical Engineering and Informatics 9, no. 4 (August 1, 2020): 1578–84. http://dx.doi.org/10.11591/eei.v9i4.1997.

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Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

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Xiao, Yun, Chongzhao Han, Qinghua Zheng, and Junjie Zhang. "Network intrusion detection method based on RS-MSVM." Journal of Electronics (China) 23, no. 6 (November 2006): 901–5. http://dx.doi.org/10.1007/s11767-005-0078-x.

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Mohd Amidon, Aqib Fawwaz, Noratikah Zawani Mahabob, Siti Mariatul Hazwa Mohd Huzir, Zakiah Mohd Yusoff, Nurlaila Ismail, and Mohd Nasir Taib. "MSVM Modelling on Agarwood Oil Various Qualities Classification." Journal of Electrical & Electronic Systems Research 21, OCT2022 (November 1, 2022): 108–13. http://dx.doi.org/10.24191/jeesr.v21i1.014.

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Zhang, Min, Zhenyu Cai, and Wenming Cheng. "Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA." Shock and Vibration 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/6209371.

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Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE_PPA_MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

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Kishore, Bhamidipati, Ali Yasar, Yavuz Selim Taspinar, Ramazan Kursun, Ilkay Cinar, Venkatesh Gauri Shankar, Murat Koklu, and Isaac Ofori. "Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/2062944.

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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Fattah, Mohamed Abdel. "The Use of MSVM and HMM for Sentence Alignment." Journal of Information Processing Systems 8, no. 2 (June 30, 2012): 301–14. http://dx.doi.org/10.3745/jips.2012.8.2.301.

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Creager, Mark A., Marc P. Bonaca, Mary M. McDermott, Judith G. Regensteiner, and Heather L. Gornik. "In Memoriam: William R. Hiatt, MD, MSVM (1950–2020)." Vascular Medicine 26, no. 4 (June 2, 2021): 469–74. http://dx.doi.org/10.1177/1358863x211012052.

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S Brinthakumari, Priyanka Dhiraj Sananse, Punam Bagul. "mSVM: An Approach for Customer Churn Prediction using modified Support Vector Machine and various Machine Learning Techniques." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 1158–69. http://dx.doi.org/10.52783/cienceng.v11i1.258.

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Consumer attrition is a major issue across the world companies and that is one of their primary worries. Organizations, specifically in the telecommunication sector, are working to build technologies to forecast future customer churn due to the obvious direct impact on profits. It's essential to define the variables that lead to customer churn before making the required efforts to minimize churn. An important impact was the creation of an attrition estimation method that aids telecommunication corporations in predicting which consumers are willing to churn. The methodology proposed in this study employs mathematical methodologies on a big data framework to provide a unique strategy to feature design and evaluation. This research work has proposed a customer churn prediction using a modified Support Vector Machine Learning (mSVM) classifier. The significant contribution of this research is that we have changed the distance function of SVM in both training and testing. Similar machine learning algorithms are also validated on similar datasets such as Naïve Bayes (NB), K-nearest Neighbor (KNN) and Decision Tree (DT) with J48 classifiers. The BigML dataset has been used for detecting churn in the telecom industry in a real-time scenario. In an extensive experimental analysis, we demonstrate mSVM which produces higher accuracy over the traditional machine learning algorithm for different cross-validation methods.

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Zhou, Shao Na, Shao Rui Xu, and Hua Xiao. "A Novel Background Subtraction Method Using Multiclass Support Vector Machine." Applied Mechanics and Materials 701-702 (December 2014): 265–69. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.265.

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Background subtraction, where the foreground is segmented from the background, is the first step of data analysis and processing in automated visual surveillance. Aiming to solve the problems associated with dynamic, multi-modal background, we explore a new approach which can handle the unconstrained environment. Based on multiclass support vector machines, a new MSVM is proposed for the classification of the background and the foreground. The simulation indicates our proposed algorithm is feasible.

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Yang, Xue Mei, Long Zhang, and Zhi Xi Li. "Vinegar Identification by Ultraviolet Spectrum Technology and Improved Multi-Class Support Vector Machines." Advanced Materials Research 271-273 (July 2011): 1657–60. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1657.

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In this paper, we proposed a new method to identify vinegar. First, we obtained the ultraviolet spectrum curves(samples) of five kinds of vinegar by ultraviolet spectrum scanning technology. Then, the samples of five kinds of vinegar were trained and tested by improved Multi-class Support Vector Machines(MSVM) for identification. The experimental results indicate that the method of combining ultraviolet spectrum technology and improved multi-class support vector machines is effective and accurate. The identification accuracy is 100%.

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Ravish, Rahul Katarya, Deepak Dahiya, and Saksham Checker. "Fake News Detection System Using Featured-Based Optimized MSVM Classification." IEEE Access 10 (2022): 113184–99. http://dx.doi.org/10.1109/access.2022.3216892.

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Sun, Yongkui, Guo Xie, Yuan Cao, and Tao Wen. "Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors." Sensors 19, no. 1 (December 20, 2018): 3. http://dx.doi.org/10.3390/s19010003.

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As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility.

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Ragab, Mahmoud, Khalid Eljaaly, Nabil A. Alhakamy, Hani A. Alhadrami, Adel A. Bahaddad, Sayed M. Abo-Dahab, and Eied M. Khalil. "Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images." Biology 11, no. 1 (December 29, 2021): 43. http://dx.doi.org/10.3390/biology11010043.

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Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

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Wajeed, Mohammed Abdul, Shivam Tiwari, Rajat Gupta, Aamir Junaid Ahmad, Seema Agarwal, Sajjad Shaukat Jamal, and Simon Karanja Hinga. "A Breast Cancer Image Classification Algorithm with 2c Multiclass Support Vector Machine." Journal of Healthcare Engineering 2023 (July 8, 2023): 1–12. http://dx.doi.org/10.1155/2023/3875525.

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Breast cancer is the most frequent type of cancer in women; however, early identification has reduced the mortality rate associated with the condition. Studies have demonstrated that the earlier this sickness is detected by mammography, the lower the death rate. Breast mammography is a critical technique in the early identification of breast cancer since it can detect abnormalities in the breast months or years before a patient is aware of the presence of such abnormalities. Mammography is a type of breast scanning used in medical imaging that involves using x-rays to image the breasts. It is a method that produces high-resolution digital pictures of the breasts known as mammography. Immediately following the capture of digital images and transmission of those images to a piece of high-tech digital mammography equipment, our radiologists evaluate the photos to establish the specific position and degree of the sickness in the breast. When compared to the many classifiers typically used in the literature, the suggested Multiclass Support Vector Machine (MSVM) approach produces promising results, according to the authors. This method may pave the way for developing more advanced statistical characteristics based on most cancer prognostic models shortly. It is demonstrated in this paper that the suggested 2C algorithm with MSVM outperforms a decision tree model in terms of accuracy, which follows prior findings. According to our findings, new screening mammography technologies can increase the accuracy and accessibility of screening mammography around the world.

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Li, Yuanhong, Congyue Wang, Chaofeng Wang, Yangfan Luo, and Yubin Lan. "An Intelligent Detection Method for Obstacles in Agricultural Soil with FDTD Modeling and MSVMs." Electronics 12, no. 11 (May 29, 2023): 2447. http://dx.doi.org/10.3390/electronics12112447.

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Unknown objects in agricultural soil can be important because they may impact the health and productivity of the soil and the crops that grow in it. Challenges in collecting soil samples present opportunities to utilize Ground Penetrating Radar (GPR) image processing and artificial intelligence techniques to identify and locate unidentified objects in agricultural soil, which are important for agriculture. In this study, we used finite-difference time-domain (FDTD) simulated models to gather training data and predict actual soil conditions. Additionally, we propose a multi-class support vector machine (MSVM) that employs a semi-supervised algorithm to classify buried object materials and locate their position in soil. Then, we extract echo signals from the electromagnetic features of the FDTD simulation model, including soil type, parabolic shape, location, and energy magnitude changes. Lastly, we compare the performance of various MSVM models with different kernel functions (linear, polynomial, and radial basis function). The results indicate that the FDTD-Yee method enhances the accuracy of simulating real agricultural soils. The average recognition rate of the hyperbola position formed by the GPR echo signal is 91.13%, which can be utilized to detect the position and material of unknown and underground objects. For material identification, the directed acyclic graph support vector machine (DAG-SVM) model attains the highest classification accuracy among all soil layers when using an RBF kernel. Overall, our study demonstrates that an artificial intelligence model trained with the FDTD forward simulation model can effectively detect objects in farmland soil.

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S., A., Md Bayazid, and Mohammad Motiur. "Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features." International Journal of Computer Applications 181, no. 10 (August 14, 2018): 12–15. http://dx.doi.org/10.5120/ijca2018916773.

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Devi, Kaveri, and Arshdeep Kaur. "MSVM Based Technique Used To Detect Diabetic Retinopathy at Early Stage." International Journal of Computer Sciences and Engineering 9, no. 3 (March 31, 2021): 34–40. http://dx.doi.org/10.26438/ijcse/v9i3.3440.

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Devi, Samiksha. "Performance Analysis of MSVM Classifier based Botanical Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 7, no. 7 (July 31, 2019): 1019–26. http://dx.doi.org/10.22214/ijraset.2019.7166.

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Velappan, Subha, Murugan D, Prabha S, and Manivanna Boopathi A. "Genetic Algorithm Based Feature Subset Selection for Fetal State Classification." Journal of Communications Technology, Electronics and Computer Science 2 (November 21, 2015): 13. http://dx.doi.org/10.22385/jctecs.v2i0.20.

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Huge amount of data are available in the field of medicine which are used for diagnosing the diseases by analyzing them. Presently, prediction of diseases are made easier and accurate by employing various data mining techniques to extract information from these medical data. This paper presents an improved method of classifying the cardiotocogram (CTG) data using Multiclass Support Vector Machine (MSVM) through an optimized feature subset produced by Genetic Algorithm (GA). Various performance metrics have been evaluated and the experimental results exhibit improved classification performance when using optimized feature set comparing to the full feature set.

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Cai, Yong Ming, and Qing Chang. "Study on the Self-Organize Selective Fusion Support Vector Machine Algorithm." Advanced Materials Research 282-283 (July 2011): 165–68. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.165.

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As a major statistical learning method in case of small sample, Support Vector Machine Algorithm (SVM) has some disadvantages in dealing with vast amounts of data, such as the memory overhead and slow training. we use Multi-class Support Vector Machine (MSVM) with Self-Organize Selective Fusion (SOSF) to optimize the multiple classifiers selectively, which can update the classification and self-adjust its classification performance, and eliminate some redundancy and conflicts, achieve the fusion of multiple classifiers selectively, and effectively solve the shortcoming of disturbances by the sub-samples distribution in large sample, and improve the training efficiency and classification efficiency.

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Ganesh, S. Sankar. "Optimal Feature Subset Selection and MSVM Classification Based CBIR for Medical Images." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (August 25, 2020): 4746–52. http://dx.doi.org/10.30534/ijatcse/2020/81942020.

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Dixit, Karishma, and Anand Singh Jalal. "A Vision-Based Approach for Indian Sign Language Recognition." International Journal of Computer Vision and Image Processing 2, no. 4 (October 2012): 25–36. http://dx.doi.org/10.4018/ijcvip.2012100103.

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The sign language is the essential communication method between the deaf and dumb people. In this paper, the authors present a vision based approach which efficiently recognize the signs of Indian Sign Language (ISL) and translate the accurate meaning of those recognized signs. A new feature vector is computed by fusing Hu invariant moment and structural shape descriptor to recognize sign. A multi-class Support Vector Machine (MSVM) is utilized for training and classifying signs of ISL. The performance of the algorithm is illustrated by simulations carried out on a dataset having 720 images. Experimental results demonstrate that the proposed approach can successfully recognize hand gesture with 96% recognition rate.

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P. Thamil Selvi, C., and R. PushpaLaksmi. "SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter." Computer Systems Science and Engineering 44, no. 3 (2023): 2439–56. http://dx.doi.org/10.32604/csse.2023.029254.

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Sosimi, A. A., T. Adegbola, and O. A. Fakinlede. "Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)." Journal of Applied Sciences and Environmental Management 23, no. 5 (June 18, 2019): 895. http://dx.doi.org/10.4314/jasem.v23i5.20.

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Bartholomew, John R., Michael R. Jaff, Bruce H. Gray, and Jeffrey W. Olin. "Remembering Jess R Young, MD, MSVM (1928–2021): SVM Founding Member and First President." Vascular Medicine 27, no. 2 (April 2022): 211–13. http://dx.doi.org/10.1177/1358863x221080192.

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Zhou, Xin, and David P. Tuck. "MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data." Bioinformatics 23, no. 9 (May 1, 2007): 1106–14. http://dx.doi.org/10.1093/bioinformatics/btm036.

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Zhou, X., and D. P. Tuck. "MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data." Bioinformatics 23, no. 15 (August 1, 2007): 2029. http://dx.doi.org/10.1093/bioinformatics/btm284.

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Sharif, Muhammad, Muhammad Attique, Muhammad Zeeshan Tahir, Mussarat Yasmim, Tanzila Saba, and Urcun John Tanik. "A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition." Journal of Organizational and End User Computing 32, no. 2 (April 2020): 67–92. http://dx.doi.org/10.4018/joeuc.2020040104.

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Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.

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Gautam, Divya. "SECURING MOBILE ADHOC NETWORKS AND CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 2 (April 27, 2020): 84–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.617.

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Securing mobile adhoc networks and cloud environment in opposition to denial of service attack by examine and predict the network traffic. DDoS attacks are most important threats next to the accessibility of cloud services. Prevention mechanisms to protect next to DDoS attacks are not forever efficient on their own. Unite dissimilar method (load balancing, throttling and Honey pots) to build hybrid defense method, in meticulous with dissimilar cloud computing layers, is extremely recommended. In this paper, a variety of DDoS attacks have been presented. We as well highlighted the defense methods to counter attack dissimilar types DDoS attacks in the cloud environment. This paper proposes SVM-based algorithm to anomaly intrusion detection. A multiclass SVM algorithm with parameter optimized by PSO (MSVM-PSO) is accessible to find out a classifier to detect multiclass attacks. This paper will extend the proposed techniques to new computing environments Mobile Ad-Hoc Networks to detect anomalous physical or virtual nodes.

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Zhang, Min, Ruiqi Wang, Zhenyu Cai, and Wenming Cheng. "Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis." Transactions of the Institute of Measurement and Control 42, no. 12 (March 24, 2020): 2324–37. http://dx.doi.org/10.1177/0142331220910885.

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For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

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Rajeshwari, M. R., and K. S. Kavitha. "Enhanced tolerance-based intuitionistic fuzzy rough set theory feature selection and ResNet-18 feature extraction model for arrhythmia classification." Multiagent and Grid Systems 18, no. 3-4 (February 3, 2023): 241–61. http://dx.doi.org/10.3233/mgs-220317.

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Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.

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Manimaran, A. "Anomaly Detection System using Ant Agent Rule Based Multiclass Support Vector Machine (AA-RB-MSVM) Algorithm." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 1297–305. http://dx.doi.org/10.26438/ijcse/v6i6.12971305.

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Zhang, Min, Yi Yuan, Ruiqi Wang, and Wenming Cheng. "Recognition of mixture control chart patterns based on fusion feature reduction and fireworks algorithm-optimized MSVM." Pattern Analysis and Applications 23, no. 1 (August 27, 2018): 15–26. http://dx.doi.org/10.1007/s10044-018-0748-6.

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Liu, Mingping, Yue Chen, Zhen Zhang, and Suhui Deng. "Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model." IEEE Access 11 (2023): 890–99. http://dx.doi.org/10.1109/access.2022.3233767.

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Agarwal, Kuldeep, Rajiv Shivpuri, Yijun Zhu, Tzyy-Shuh Chang, and Howard Huang. "Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling." Expert Systems with Applications 38, no. 6 (June 2011): 7251–62. http://dx.doi.org/10.1016/j.eswa.2010.12.026.

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