Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM)

Kumar, Lov; Sripada, Sai Krishna; Sureka, Ashish; Rath, Santanu Kumar

doi:10.1016/j.jss.2017.04.016

Cited by 86 publications

(31 citation statements)

References 33 publications

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“…Least square support vector machine Least Square Support Vector Machines are supervised learning methods which have several applications such as: classification, regression and so on. It is a modified version of support vector machine (SVM) and is presented by Suykens and Vandewalle [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Enhancement Spectral Resolution for the Prediction Amount of Sofosbuvir and Ledipasvir Using Least Squares Support Vector Machine and Artificial Neural Networks in Pharmaceutical Formulation

Safakhoo

Sohrabi

Khalili

et al. 2019

J. Chil. Chem. Soc.

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In this study, least squares support vector machine (LS-SVM) and artificial neural networks (ANNs) as intelligent methods combined with spectrophotometry method, were used for determination of Sofosbuvir (SOF) and Ledipasvir (LED) in synthetic mixtures and Harvoni tablet simultaneously. In the LS-SVM method, Radial Basis Function (RBF) was used as kernel function. Then, the regularization parameter () and Bandwidth (2) were optimized and root mean square error prediction (RMSE) was 0.4164, 0.6033 for SOF and LED respectively. Afterwards, Feed-forward back-propagation network with different training algorithms was used in artificial neural network method. These training algorithms compared with each other for selecting the best model. On the other hand, radial basis function neural network (RBFNN) was applied as an efficient network. Finally, these methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. According to one way analysis of variance (ANOVA) test at the 95 % confidence level, there were no significant differences between LS-SVM, ANN and reference methods.

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Section: Introductionmentioning

confidence: 99%

Enhancement Spectral Resolution for the Prediction Amount of Sofosbuvir and Ledipasvir Using Least Squares Support Vector Machine and Artificial Neural Networks in Pharmaceutical Formulation

Safakhoo

Sohrabi

Khalili

et al. 2019

J. Chil. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…SVM can be applied as a pattern classification technique proposed by Vapnik in 1995 [ 36 ]. Suykens et al [ 37 ] proposed least squares support vector machine (LS-SVM) to improve SVM in the classification accuracy. To facilitate practical applications, LS-SVM has evolved from a binary classification method to a multi-classification one [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

Yang

Bai

et al. 2018

Sensors

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Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

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“…In accordance with SVR specifically applied to defect prediction, we only identify three studies applying a SVR to defect-prone [12] [22] [23] rather than to DD.…”

Section: Introductionmentioning

confidence: 99%

Upsilon-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects

López-Martín

Azzeh

Bou-Nassif

et al. 2018

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

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An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., ε-SVR and ʋ-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that ʋ-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation.

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Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM)

Cited by 86 publications

References 33 publications

Enhancement Spectral Resolution for the Prediction Amount of Sofosbuvir and Ledipasvir Using Least Squares Support Vector Machine and Artificial Neural Networks in Pharmaceutical Formulation

Enhancement Spectral Resolution for the Prediction Amount of Sofosbuvir and Ledipasvir Using Least Squares Support Vector Machine and Artificial Neural Networks in Pharmaceutical Formulation

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

Upsilon-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects

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