Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.

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Open-Source Essential Protein Prediction Model by Integrating Chi-Square and Support Vector Machine

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.2020070103 ◽

2020 ◽

Vol 11 (3) ◽

pp. 38-56

Author(s):

S. R. Mani Sekhar ◽

Siddesh G. M. ◽

Sunilkumar S. Manvi

Keyword(s):

Support Vector Machine ◽

Open Source ◽

Vital Role ◽

Support Vector ◽

Chi Square ◽

Essential Proteins ◽

Topological Features ◽

Proposed Model ◽

Protein Prediction ◽

Svm Model

Identification and analysis of protein play a vital role in drug design and disease prediction. There are several open-source applications that have been developed for identifying essential proteins which are based on biological or topological features. These techniques infer the possibility of proteins to be essential by using the network topology and feature selection, which can ignore some of the features to reduce the complexity and, subsequently, results in less accuracy. In the paper, the authors have used selenium driver to scrap the dataset. Later, the authors integrated the chi-square method with support vector machine for the prediction of essential proteins in baker yeast. Here, chi-square is a test of dissimilarity used for altering the record, and afterward, the support vector machine is used to classify the test dataset. The results show that the proposed model Chi-SVM model achieves an accuracy of 99.56%, whereas BC and CC achieved an accuracy of 84.0% and 86.0%. Finally, the proposed model is validated using Statistical performance measures such as PPA, NPA, SA, and STA.

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Analysis of Speaker Verification System Using Support Vector Machine

JOURNAL OF ADVANCES IN CHEMISTRY ◽

10.24297/jac.v13i10.5839 ◽

2017 ◽

Vol 13 (10) ◽

pp. 6531-6542

Author(s):

P Shanmugapriya ◽

Y. Venkataramani

Keyword(s):

Support Vector Machine ◽

Speaker Recognition ◽

Speaker Verification ◽

Fuzzy Theory ◽

Support Vector ◽

Fuzzy Support Vector Machine ◽

System A ◽

Verification System ◽

Svm Model

The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system.Â This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speakerâ€™s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields better classification accuracy and requires less number of support vectors. Experiments were conducted on 2001 NIST speaker recognition evaluation corpus. Performance of GMM-FSVM based speaker verification system is compared with the conventional GMM-UBM and GMM-SVM based systems.Â Experimental results indicate that the fuzzy SVM based speaker verification system with GMM super vector achieves better performance to GMM-UBM system. Â

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Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110766 ◽

2021 ◽

Vol 10 (11) ◽

pp. 766

Author(s):

Xishihui Du ◽

Kefa Zhou ◽

Yao Cui ◽

Jinlin Wang ◽

Shuguang Zhou

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Hybrid Genetic Algorithm ◽

Geochemical Data ◽

Support Vector ◽

Adaptive Optimization ◽

Spatial Efficiency ◽

Svm Model ◽

Mineral Prospectivity ◽

Optimization Search

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

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Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission

Journal of Vibration Engineering & Technologies ◽

10.1007/s42417-019-00143-y ◽

2019 ◽

Vol 7 (5) ◽

pp. 455-464 ◽

Cited By ~ 5

Author(s):

Henry Ogbemudia Omoregbee ◽

P. Stephan Heyns

Keyword(s):

Support Vector Machine ◽

Genetic Algorithms ◽

Acoustic Emission ◽

Fault Classification ◽

Support Vector ◽

Low Speed

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Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.1495 ◽

2011 ◽

Vol 131 (8) ◽

pp. 1495-1501

Author(s):

Dongshik Kang ◽

Masaki Higa ◽

Hayao Miyagi ◽

Ikugo Mitsui ◽

Masanobu Fujita ◽

...

Keyword(s):

Support Vector Machine ◽

Acoustic Signals ◽

Support Vector ◽

Amplitude Difference

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Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

International Journal of Computational Physics Series ◽

10.29167/a1i1p120-130 ◽

2018 ◽

Vol 1 (1) ◽

pp. 120-130 ◽

Cited By ~ 1

Author(s):

Chunxiang Qian ◽

Wence Kang ◽

Hao Ling ◽

Hua Dong ◽

Chengyao Liang ◽

...

Keyword(s):

Support Vector Machine ◽

Environmental Factors ◽

Cross Validation ◽

Concrete Strength ◽

Simulation Method ◽

Support Vector ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Influence Degree ◽

Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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