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Journal articles on the topic 'SVM classification model'

Author: Grafiati

Published: 7 September 2021

Last updated: 14 February 2022

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Zong, Xinlu, Chunzhi Wang, and Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification." International Journal of Future Generation Communication and Networking 6, no. 6 (2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.

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Zhao, Yun, Xing Xu, and Yong He. "Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification." Key Engineering Materials 460-461 (January 2011): 667–72. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.667.

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The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

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Mao, Qing Hua, Hong Wei Ma, and Xu Hui Zhang. "SVM Classification Model Parameters Optimized by Improved Genetic Algorithm." Advanced Materials Research 889-890 (February 2014): 617–21. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.617.

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SVM classification model has been widely applied to mechanical equipment fault diagnosis and material defects classification. It is difficult to choose the optimal value of penalty factor C and kernel function parameter for SVM model. Therefore, an improved genetic algorithm to optimize SVM parameters is put forward, which improves crossover and mutation operators and enhances convergence properties by using the best individual retention strategy. UCI data set is used to verify the algorithm. The testing results show that the algorithm can quickly and effectively select optimal SVM parameters and improve SVM classification accuracy.

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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 extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.

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Athavale, Vijay Anant, Suresh Chand Gupta, Deepak Kumar, and Savita. "Human Action Recognition Using CNN-SVM Model." Advances in Science and Technology 105 (April 2021): 282–90. http://dx.doi.org/10.4028/www.scientific.net/ast.105.282.

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In this paper, a pre-trained CNN model VGG16 with the SVM classifier is presented for the HAR task. The deep features are learned via the VGG16 pre-trained CNN model. The VGG 16 network is previously used for the image classification task. We used VGG16 for the signal classification of human activity, which is recorded by the accelerometer sensor of the mobile phone. The UniMiB dataset contains the 11771 samples of the daily life activity of humans. A Smartphone records these samples through the accelerometer sensor. The features are learned via the fifth max-pooling layer of the VGG16 CNN model and feed to the SVM classifier. The SVM classifier replaced the fully connected layer of the VGG16 model. The proposed VGG16-SVM model achieves effective and efficient results. The proposed method of VGG16-SVM is compared with the previously used schemes. The classification accuracy and F-Score are the evaluation parameters, and the proposed method provided 79.55% accuracy and 71.63% F-Score.

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Wang, Mei, Pai Wang, Jzau-Sheng Lin, Xiaowei Li, and Xuebin Qin. "Nonlinear Inertia Classification Model and Application." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/987686.

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Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

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Liu, Bingchun, Mingzhao Lai, Jheng-Long Wu, Chuanchuan Fu, and Arihant Binaykia. "Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model." Multimedia Tools and Applications 79, no. 15-16 (2019): 10177–97. http://dx.doi.org/10.1007/s11042-019-7422-x.

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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 its performance on the specific dataset. The low classification accuracy problem after template-based feature extraction is solved using the optimal feature description extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has a better performance in vehicle detection and type classification.

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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 Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

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Xu, Jie, Rong Zhu, and Bo Hong. "A Novel Hybrid Model for Image Classification." Applied Mechanics and Materials 48-49 (February 2011): 98–101. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.98.

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Image classification poses challenges to retrieval technology. Though the Support Vector Machine (SVM) has been successfully applied to pattern recognition, its performance is limited by the feature space and parameters in the training process. Our work thus has two central themes. Construct the optimum feature space for training SVM from image features extraction by nonlinear dimensionality reduction based on manifold learning, and meanwhile establish the RBF kernel based SVM classifier by training with the best parameters with a global search capacity of the Quantum-behaved Particle Swarm Optimization (QPSO). Experiments show that our model not only improves the learning ability, but also significantly enhances the accuracy of image classification.

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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 problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

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Sarker, Chandrama, Luis Mejias, Frederic Maire, and Alan Woodley. "Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information." Remote Sensing 11, no. 19 (2019): 2331. http://dx.doi.org/10.3390/rs11192331.

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Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.

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Guo, Husheng, and Wenjian Wang. "An active learning-based SVM multi-class classification model." Pattern Recognition 48, no. 5 (2015): 1577–97. http://dx.doi.org/10.1016/j.patcog.2014.12.009.

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Guo, Yan Feng, Na Sun, and Yuan Yao. "An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk." Advanced Materials Research 271-273 (July 2011): 1286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1286.

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Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two public credit datasets to certify the validity of our ensemble model. The experimental result shows that the ensemble SOM-SVM model can overcome the single model weakness and improve the accuracy of classification, which is good for constructing a better credit scoring system in future.

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Lad, Sumit S., and Amol C. Adamuthe. "Malware Classification with Improved Convolutional Neural Network Model." International Journal of Computer Network and Information Security 12, no. 6 (2020): 30–43. http://dx.doi.org/10.5815/ijcnis.2020.06.03.

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Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

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Christovam, L. E., G. G. Pessoa, M. H. Shimabukuro, and M. L. B. T. Galo. "LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1841–47. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019.

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Abstract. Land Use and Land Cover (LULC) information is an important data source for modeling environmental variables, so it is essential to develop high quality LULC maps. The hundreds of continuous spectral bands gathered with hyperspectral sensors provide high spectral detail and consequently confirm hyperspectral remote sensing as an appropriate option for many LULC applications. Despite increased spectral detail, issues like high dimensionality, huge volume of data and redundant information, mean that hyperspectral image classification is a complex task. It is therefore essential to develop classification approaches that deals with these issues. Since classification results are directly dependent on the dataset used, it is fundamental to compare and validate the classification approaches in public datasets. With this in mind, aiming to provide a baseline, four classification models in the relatively new hyperspectral HyRANK dataset were evaluated. The classification models were defined with three well-known classification algorithms: Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Random Forest (RF). A classification model with SAM and another with RF were defined with the 176 surface reflectance bands. A dimensionality reduction with principal component analysis was carried out and a classification model with SVM and another with RF were defined using 14 principal components as features. The results show that SVM and RF algorithms outperformed by far the SAM in terms of accuracy, and that the RF is slightly better than the SVM in this respect. It is also possible to see from the results that the use of principal components as features provided an improvement in the accuracy of the RF and an improvement of 28% in the time spent fitting the classification model.

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Wei, Li Wei, Qiang Xiao, Ying Zhang, and Xiong Fei Ji. "Credit Risk Evaluation Using a New Classification Model: L1-LS-SVM." Applied Mechanics and Materials 321-324 (June 2013): 1917–20. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1917.

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Least squares support vector machine (LS-SVM) has an outstanding advantage of lower computational complexity than that of standard support vector machines. Its shortcomings are the loss of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization performance. In this paper, a least squares support vector machine with L1 penalty (L1-LS-SVM) is proposed to deal with above shortcomings. A minimum of 1-norm based object function is chosen to get the sparse and robust solution based on the idea of basis pursuit (BP) in the whole feasibility region. Some UCI datasets are used to demonstrate the effectiveness of this model. The experimental results show that L1-LS-SVM can obtain a small number of support vectors and improve the generalization ability of LS-SVM.

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Takeda, Akiko, Hiroyuki Mitsugi, and Takafumi Kanamori. "A Unified Classification Model Based on Robust Optimization." Neural Computation 25, no. 3 (2013): 759–804. http://dx.doi.org/10.1162/neco_a_00412.

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A wide variety of machine learning algorithms such as the support vector machine (SVM), minimax probability machine (MPM), and Fisher discriminant analysis (FDA) exist for binary classification. The purpose of this letter is to provide a unified classification model that includes these models through a robust optimization approach. This unified model has several benefits. One is that the extensions and improvements intended for SVMs become applicable to MPM and FDA, and vice versa. For example, we can obtain nonconvex variants of MPM and FDA by mimicking Perez-Cruz, Weston, Hermann, and Schölkopf's ( 2003 ) extension from convex ν-SVM to nonconvex Eν-SVM. Another benefit is to provide theoretical results concerning these learning methods at once by dealing with the unified model. We give a statistical interpretation of the unified classification model and prove that the model is a good approximation for the worst-case minimization of an expected loss with respect to the uncertain probability distribution. We also propose a nonconvex optimization algorithm that can be applied to nonconvex variants of existing learning methods and show promising numerical results.

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Gang, Sin-Moon, Han-Jo Kim, Won-Seok Oh, Sun-Young Kim, Kyoung-Tai No, and Ky-Youb Nam. "Development of Classification Model for hERG Ion Channel Inhibitors Using SVM Method." Journal of the Korean Chemical Society 53, no. 6 (2009): 653–62. http://dx.doi.org/10.5012/jkcs.2009.53.6.653.

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Gu, Peng, Yao-Ze Feng, Le Zhu, et al. "Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning." Molecules 25, no. 8 (2020): 1797. http://dx.doi.org/10.3390/molecules25081797.

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A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

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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 set, We propose a new SVM model to solve the above classification problem by introducing the fuzzy memberships to each unlabeled point. Simulation results demonstrate that the proposed method can exploit unlabeled data to yield good performance effectively.

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Jin, Zhen Lu, Quan Pan, Chun Hui Zhao, and Wen Tian Zhou. "SVM Based Land/Sea Clutter Classification Algorithm." Applied Mechanics and Materials 236-237 (November 2012): 1156–62. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.1156.

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In this paper, a support vector machine (SVM) based land/sea clutter classification algorithm was proposed. For target location error correction based on passive beacon reference source of over-the-horizon radar (OTHR), the signal model of land/sea clutter is established, the three kinds of multi-features of land/sea clutter are analyzed, and the classification algorithm based on SVM using multi-features is detailed. Simulation experiments were carried out for different clutter-noise- ratios, and the results showed that the proposed algorithm has a higher recognition rate of land/sea clutter than algorithms based on single feature of backscatter amplitude or linear discriminant analysis. This paper could provide theoretical guidance for improving target location accuracy.

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Wang, Jian, Yuan Yuan Zhang, and Shu Hang Guo. "User Classification Prediction Research Based on Clustering SVM." Advanced Materials Research 712-715 (June 2013): 2676–79. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2676.

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Users interest discovery can be belonged to category of classification knowledge discovery, which can divide user information need into two groups as interested group and uninterested group by classification forecast analysis of user information need which reflects historical visiting information behavior of user. After analysis the use of a several methods of user classification prediction, this paper presents a modified model combining clustering algorithm was presented for improving the forecasting accuracy of SVM. The efficiency of the proposed method was tested by the user access informations log data. The results have shown that the higher accuracy is expressed in this proposed model, and it is applicable to practice.

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Liu, Bingxin, Ying Li, Guannan Li, and Anling Liu. "A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 160. http://dx.doi.org/10.3390/ijgi8040160.

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Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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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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Ali, Zafar, Nengroo, Hussain, Park, and Kim. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features." Energies 12, no. 22 (2019): 4366. http://dx.doi.org/10.3390/en12224366.

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Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

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López-Franco, Carlos, Luis Villavicencio, Nancy Arana-Daniel, and Alma Y. Alanis. "Image Classification Using PSO-SVM and an RGB-D Sensor." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/695910.

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Image classification is a process that depends on the descriptor used to represent an object. To create such descriptors we use object models with rich information of the distribution of points. The object model stage is improved with an optimization process by spreading the point that conforms the mesh. In this paper, particle swarm optimization (PSO) is used to improve the model generation, while for the classification problem a support vector machine (SVM) is used. In order to measure the performance of the proposed method a group of objects from a public RGB-D object data set has been used. Experimental results show that our approach improves the distribution on the feature space of the model, which allows to reduce the number of support vectors obtained in the training process.

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Assegie, Tsehay Admassu. "Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (2021): 9–14. http://dx.doi.org/10.35882/ijeeemi.v3i1.2.

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Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.

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Yao, Jian-Rong, and Jia-Rui Chen. "A New Hybrid Support Vector Machine Ensemble Classification Model for Credit Scoring." Journal of Information Technology Research 12, no. 1 (2019): 77–88. http://dx.doi.org/10.4018/jitr.2019010106.

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Credit scoring plays important role in the financial industry. There are different ways employed in the field of credit scoring, such as the traditional logistic regression, discriminant analysis, and linear regression; methods used in the field of machine learning include neural network, k-nearest neighbors, genetic algorithm, support vector machines (SVM), decision tree, and so on. SVM has been demonstrated with good performance in classification. This paper proposes a new hybrid RF-SVM ensemble model, which uses random forest to select important variables, and employs ensemble methods (bagging and boosting) to aggregate single base models (SVM) as a robust classifier. The experimental results suggest that this new model could achieve effective improvement, and has promising potential in the field of credit scoring.

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Cai, Hong Xia, and Wen Wei Wu. "Material Classification Based on SVM for Civil Aircraft." Advanced Materials Research 452-453 (January 2012): 746–49. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.746.

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Inventory control plays a crucial role for civil aircraft in reducing production cost to improve the competitiveness. Aiming at the special requirements of aircraft manufacturing industry, we propose a solution to classify the material instead of ABC inventory control. In order to balance economy and reliability, we build a new criterion system to classify the aircraft material into nine categories. We investigate the Support Vector Machine (SVM) method to classify the material for it has been proved very powerful and effective in establishing classification model with small sample, nonlinearity, high dimension and local minima. The choice of corresponding parameters, the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done to determine the appropriate kernel function and to optimize SVM’s parameters for the classification of civil aircraft materials. Research results prove the validity of the classification model by the software implementation.

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Li, Yu, and Lizhuang Liu. "Image quality classification algorithm based on InceptionV3 and SVM." MATEC Web of Conferences 277 (2019): 02036. http://dx.doi.org/10.1051/matecconf/201927702036.

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In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different design choices, ranging from the use of various CNN architectures to the use of features extracted from different layers of a CNN model. To cope with the problem of a lack of adequate amounts of distorted picture data, a novel training strategy of multi-scale training, which is selecting a new image size for training after several batches, combined with data augmentation is introduced. The experimental results tested on the actual monitoring video images shows that the proposed model can accurately classify distorted images.

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Yang, Xiao Li, and Huan Yun He. "Variable Selection in Proteomic Profile Classification by Interval Support Vector Machines (iSVM)." Applied Mechanics and Materials 556-562 (May 2014): 347–50. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.347.

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For variable selection in proteomic profile classification, we present a new local modeling procedure called interval support vector machine (iSVM). This procedure builds a series of SVM models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of SVM models reaching a desired error level. We applied iSVM in variable selection for proteomic profile classification. The results show that the proposed procedure are very promising for classification target-based variable selection and obtain much better classification than full-spectrum SVM model.

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WANG, YEN-WEN, PEI-CHANN CHANG, CHIN-YUAN FAN, and CHIUNG-HUA HUANG. "DATABASE CLASSIFICATION BY INTEGRATING A CASE-BASED REASONING AND SUPPORT VECTOR MACHINE FOR INDUCTION." Journal of Circuits, Systems and Computers 19, no. 01 (2010): 31–44. http://dx.doi.org/10.1142/s0218126610005950.

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Database classification suffers from two common problems, i.e., the high dimensionality and nonstationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a Support Vector Machine (SVM), and Genetic Algorithms to construct a decision-making system for data classification in various database applications. The model is mainly based on the concept that the historic database can be transformed into a smaller case-base together with a group of SVM models. As a result, the model can more accurately respond to the current data under classifying from the inductions by these SVM models generated from these smaller case bases. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.

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Wei, Li Wei, Chuan Shen Wei, and Xia Qing Wan. "Data Classification Using Support Vector Machines with Mixture Kernels." Advanced Materials Research 662 (February 2013): 936–39. http://dx.doi.org/10.4028/www.scientific.net/amr.662.936.

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Recent studies have showed that machine learning techniques are advantageous to statistical models for medicine database classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. Three UCI databases are used to demonstrate the good performance of the SVM- MK.

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Cao, Cong, Suzana Dragićević, and Songnian Li. "Land-Use Change Detection with Convolutional Neural Network Methods." Environments 6, no. 2 (2019): 25. http://dx.doi.org/10.3390/environments6020025.

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Convolutional neural networks (CNN) have been used increasingly in several land-use classification tasks, but there is a need to further investigate its potential. This study aims to evaluate the performance of CNN methods for land classification and to identify land-use (LU) change. Eight transferred CNN-based models were fully evaluated on remote sensing data for LU scene classification using three pre-trained CNN models AlexNet, GoogLeNet, and VGGNet. The classification accuracy of all the models ranges from 95% to 98% with the best-performed method the transferred CNN model combined with support vector machine (SVM) as feature classifier (CNN-SVM). The transferred CNN-SVM model was then applied to orthophotos of the northeastern Cloverdale as part of the City of Surrey, Canada from 2004 to 2017 to perform LU classification and LU change analysis. Two sources of datasets were used to train the CNN–SVM model to solve a practical issue with the limited data. The obtained results indicated that residential areas were expanding by creating higher density, while green areas and low-density residential areas were decreasing over the years, which accurately indicates the trend of LU change in the community of Cloverdale study area.

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CAO, JIE, HONGKE LU, WEIWEI WANG, and JIAN WANG. "A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO." International Journal of Information Technology & Decision Making 11, no. 04 (2012): 857–74. http://dx.doi.org/10.1142/s021962201250023x.

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Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

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Liang, Yinghong, Haitao Liu, and Su Zhang. "Micro-blog sentiment classification using Doc2vec + SVM model with data purification." Journal of Engineering 2020, no. 13 (2020): 407–10. http://dx.doi.org/10.1049/joe.2019.1159.

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Kung, S. Y., and Man-Wai Mak. "PDA-SVM Hybrid: A Unified Model for Kernel-Based Supervised Classification." Journal of Signal Processing Systems 65, no. 1 (2011): 5–21. http://dx.doi.org/10.1007/s11265-011-0588-8.

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R.S., Rajasree, S. Brintha Rajakumari, Gajanan Babhulkar, and Madhuri Gurale. "Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease." International Journal of Computer Applications 174, no. 27 (2021): 37–40. http://dx.doi.org/10.5120/ijca2021921144.

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Chaudhary, Sonia. "An Approach for Detecting Fruit Quality with RBF-SVM Classification Model." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1052–58. http://dx.doi.org/10.22214/ijraset.2021.36426.

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For the regular development of rural area especially for the agriculture domain, automation is very important. Currently fruit's quality plays an important factor in their sales and production. Detecting the quality of fruits by using manual methods are not recommended because of the reasons that it cause delay and the results are also not upto the mark. Therefore, machine learning and computer vision is gaining much interest from current researchers to develop fruit quality detection systems. This paper contributes to provide an effective and advanced Orange fruit quality detection system. the proposed scheme is focused on giving an Fuzzy C-Mean based region of interest extracting scheme along with RBF-SVM classification model to improve the classification rate in comparison to existing approaches. The proposed scheme is simulated in MATLAB software and results are evaluated in terms of various performance factors such as Accuracy, Sensitivity, Specificity, Precision, Recall and F-Score. Finally a comparison of the proposed scheme is given that show an improvement of approximately 18% with respect to various state of art techniques. this strengthen the recommendation of proposed scheme for future fruit quality analysis system.

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Cui, Dongyan, and Kewen Xia. "Strip Surface Defects Recognition Based on PSO-RS&SOCP-SVM Algorithm." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4257273.

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In order to improve the strip surface defect recognition and classification accuracy and efficiency, Rough Set (RS) attribute reduction algorithm based on Particle Swarm Optimization (PSO) algorithm was used on the optimal selection of strip surface defect image decision features, which removed redundant attributes, provided reduction data for the follow-up Support Vector Machine (SVM) model, reduced vector machine learning time, and constructed the SVM classifier, which uses Second-Order Cone Programming (SOCP) and multikernel Support Vector Machine classification model. Six kinds of typical defects such as rust, scratch, orange peel, bubble, surface crack, and rolled-in scale are recognized and classification is made using this classifier. The experimental results show that the classification accuracy of the proposed algorithm is 99.5%, which is higher than that of SVM algorithm and Relevance Vector Machine (RVM) algorithm. And because of using the Rough Set attribute reduction algorithm based on PSO algorithm, the learning time of SVM is reduced, and the average time of the classification and recognition model is 58.3 ms. In summary, the PSO-RS&SOCP-SVM evaluation model is not only more efficient in time, but also more worthy of popularization and application in the accuracy.

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You, Lei, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, and Jin Wang. "A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis." Shock and Vibration 2019 (February 12, 2019): 1–16. http://dx.doi.org/10.1155/2019/1908485.

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Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

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Kamble, Madhubala. "Calorie Detection of Food Image based on SVM Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1836–40. http://dx.doi.org/10.22214/ijraset.2021.35419.

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Nowadays, standard intake of healthy food is vital for keeping a diet to avoid obesity within the human body . In this paper, we present a totally unique system supported machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a machine learning model consisting of a support vector machine that classifies food into specific categories within the training a part of the prototype system. The most purpose of the proposed method is to reinforce the accuracy of the pre-training model. The paper designs a prototype system supported the client server network model. The client sends an image detection request and processes it on the server side. The prototype system is meant with three main software components, including a pre-trained support vector machine training module for classification purposes, a text data training module for attribute estimation models, and a server-side module. We experimented with a selection of food categories, each containing thousands of images, and therefore the machine learning training to understand higher classification accuracy.

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AlDhlan, Kawthar. "Speech Synthesis for Gender Classification." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 5, no. 1 (2017): 67. http://dx.doi.org/10.3991/ijes.v5i1.6690.

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This paper presents a gender identification system to be used for call forwarding in health related communications. The system listens to the caller then using speech synthesis, image processing, and linear support vector machine SVM identifies either he or she is a male or a female. This solution is imperative in a conservative country such as the Kingdom of Saudi Arabia in order to forward the call to a male or female practitioner. The originality of the approach is that no transcription is used to learn SVM models. To identify the gender of the caller, the trained SVM model of the reference pieces are compared to transcripts of the audio frequency record and are using the Levenshtein distance. For the identification of gender, we obtain an accuracy rate of 94% on a test flow containing 449 pieces of speech clips.

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Feng, Wei, Nicholas Van Halm-Lutterodt, Hao Tang, et al. "Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process." International Journal of Neural Systems 30, no. 06 (2020): 2050032. http://dx.doi.org/10.1142/s012906572050032x.

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In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

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Zhu, Hou Yao, Chun Liang Zhang, and Xia Yue. "Fault Diagnosis of Nuclear Power Equipment Based on HMM-SVM and Database Development." Advanced Materials Research 139-141 (October 2010): 2532–36. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2532.

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This paper mainly introduced the basic theory of Hidden Markov Model (HMM) and Support Vector Machines (SVM). HMM has strong capability of handling dynamic process of time series and the timing pattern classification, particularly for the analysis of non-stationary, poor reproducibility signals. It has good ability to learn and re-learn and high adaptability. SVM has strong generalization ability of small samples, which is suitable for handling classification problems, to a greater extent, reflecting the differences between categories. Based on the advantages and disadvantages between the two models, this paper presented a hybrid model of HMM-SVM. Experiments showed that the HMM-SVM model was more effective and more accurate than the two single separate models. The paper also explored the application of its database system development, which could help the managers to get and handle the data quickly and effectively.

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Li, Dong. "Facial Expression Recognition Based on RS-SVM." Applied Mechanics and Materials 543-547 (March 2014): 2329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2329.

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In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.

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Soula, Arbia, Khaoula Tbarki, Riadh Ksantini, Salma Ben Said, and Zied Lachiri. "A novel incremental Kernel Nonparametric SVM model (iKN-SVM) for data classification: An application to face detection." Engineering Applications of Artificial Intelligence 89 (March 2020): 103468. http://dx.doi.org/10.1016/j.engappai.2019.103468.

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Zurita, Baldemar, Luís Luna, José Hernández, and José Ramírez. "Hybrid Classification in Bag of Visual Words Model." Circulation in Computer Science 3, no. 4 (2018): 10–15. http://dx.doi.org/10.22632/ccs-2018-252-85.

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Classification of images by means of the BOVW method is well known and applied in different recognition projects, this method rely on three phases: detection and extraction of characteristics, representation of the image and finally the classification. SIFT, Kmeans and SVM is the most accepted combination. This article aims to demonstrate that this combination is not always the best choice for all types of datasets, different training sets of images were created from scratch and will be used for the bag of visual words model: the first phase of detection and extraction, SIFT will be used, later in the second phase a dictionary of words will be created through a clustering process using K-means, EM, K-means in combination with EM, finally, for classification it will be compared the algorithms of SVM, Gaussian NB, KNN, Decision Tree, Random Forest, Neural Network and AdaBoost in order to determine the performance and accuracy of every method.

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Zhou, M., C. R. Li, L. Ma, and H. C. Guan. "LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 447–52. http://dx.doi.org/10.5194/isprsarchives-xli-b3-447-2016.

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In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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