scholarly journals Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wang Xianfang ◽  
Wang Junmei ◽  
Wang Xiaolei ◽  
Zhang Yue
Keyword(s):  
Ion Channel ◽  
Pearson Correlation ◽  
Support Vector ◽  
Small Peptides ◽  
Information Redundancy ◽  
Significance Level ◽  
Average Accuracy ◽  
Proposed Model ◽  
Svm Model ◽  
Weak Independence

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

scholarly journals A Forecast Model of the Number of Containers for Containership Voyage

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 193
Author(s):  
Yuchuang Wang ◽  
Guoyou Shi ◽  
Xiaotong Sun
Keyword(s):  
Container Terminal ◽  
Forecast Model ◽  
Gray Relational Analysis ◽  
Support Vector ◽  
Model Parameters ◽  
Container Ship ◽  
Kernel Support Vector Machine ◽  
Proposed Model ◽  
Svm Model ◽  
Pass Through

Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage.

A computational model for predicting transmembrane regions of retroviruses

2017 ◽  
Vol 15 (03) ◽  
pp. 1750010 ◽  
Author(s):  
Ze Liu ◽  
Hongqiang Lv ◽  
Jiuqiang Han ◽  
Ruiling Liu
Keyword(s):  
Selection Criterion ◽  
Training Dataset ◽  
Evolutionary Information ◽  
Support Vector ◽  
Hydrophobic Residues ◽  
Proposed Model ◽  
Svm Model ◽  
Feature Importance ◽  
Covalently Linked ◽  
Env Glycoprotein

Transmembrane region (TR) is a conserved region of transmembrane (TM) subunit in envelope (env) glycoprotein of retrovirus. Evidences have shown that TR is responsible for anchoring the env glycoprotein on the lipid bilayer and substitution of the TR for a covalently linked lipid anchor abrogates fusion. However, universal software could not achieve sufficient accuracy as TM in env also has several motifs such as signal peptide, fusion peptide and immunosuppressive domain composed largely of hydrophobic residues. In this paper, a support vector machine-based (SVM) model is proposed to identify TRs in retroviruses. Firstly, physicochemical and evolutionary information properties were extracted as original features. And then, the feature importance was analyzed by minimum Redundancy Maximum Relevance (mRMR) feature selection criterion. Our model achieved an Sn of 0.955, Sp of 0.998, ACC of 0.995, MCC of 0.954 using 10-fold cross-validation on the training dataset. These results suggest that the proposed model can be used to predict TRs in non-annotation retroviruses and 11917, 3344, 2, 289 and 6 new putative TRs were found in HERV, HIV, HTLV, SIV, MLV, respectively.

scholarly journals Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Sun ◽  
Xianda Feng ◽  
Lingqiang Yang
Keyword(s):  
Predictive Accuracy ◽  
Average Error ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Multiclass Support Vector Machine ◽  
Multiclass Svm ◽  
Tunnel Instability ◽  
Weak Rock Masses

Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

scholarly journals Forecasting Dry Bulk Freight Index with Improved SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qianqian Han ◽  
Bo Yan ◽  
Guobao Ning ◽  
B. Yu
Keyword(s):  
Wavelet Transform ◽  
Support Vector ◽  
Data Series ◽  
Shipping Industry ◽  
Combined Model ◽  
Final Model ◽  
Proposed Model ◽  
Svm Model ◽  
The Impact ◽  
Short Term Trend

An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

scholarly journals Support Vector Machine Based Classification Model for Screening Plasmodium falciparum Proliferation Inhibitors and Non-Inhibitors

2011 ◽  
Vol 3 ◽  
pp. BECB.S7503 ◽  
Author(s):  
Sangeetha Subramaniam ◽  
Monica Mehrotra ◽  
Dinesh Gupta
Keyword(s):  
Support Vector Machine ◽  
High Throughput Screening ◽  
Disease Burden ◽  
Classification Model ◽  
Rapid Screening ◽  
Support Vector ◽  
Data Set ◽  
Chemical Libraries ◽  
Average Accuracy ◽  
Svm Model

There is an urgent need to develop novel anti-malarials in view of the increasing disease burden and growing resistance of the currently used drugs against the malarial parasites. Proliferation inhibitors targeting P. falciparum intraerythrocytic cycle are one of the important classes of compounds being explored for its potential to be novel antimalarials. Support Vector Machine (SVM) based model developed by us can facilitate rapid screening of large and diverse chemical libraries by reducing false hits and prioritising compounds before setting up expensive High Throughput Screening experiment. The SVM model, trained with molecular descriptors of proliferation inhibitors and non-inhibitors, displayed a satisfactory performance on cross validations and independent data set, with an average accuracy of 83% and AUC of 0.88. Intriguingly, the method displayed remarkable accuracy for the recently submitted P. falciparum whole cell screening datasets. The method also predicted several inhibitors in the National Cancer Institute diversity set, mostly similar to the known inhibitors.

scholarly journals Use of GA-ANN and GA-SVM for a QSPR study on aqueous solubility of pesticides

2020 ◽  
pp. 66-66
Author(s):  
Amel Bouakkadia ◽  
Noureddine Kertiou ◽  
Rana Amiri ◽  
Youssouf Driouche ◽  
Djelloul Messadi
Keyword(s):  
Pearson Correlation ◽  
Chemical Properties ◽  
Aqueous Solubility ◽  
Important Variable ◽  
Support Vector ◽  
Ann Model ◽  
Experimental Procedure ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Selection Of

The partitioning tendency of pesticides, in these study herbicides in particular, into different environmental compartments depends mainly of the physic-chemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure determining aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, every time associated with genetic algorithm (GA) selection of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series 80 herbicides. The values of log S of the studied compounds were well correlated with de descriptors. Considering the pertinent descriptors, a Pearson Correlation Squared (R2) coefficient of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

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

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.

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

2014 ◽  
Vol 628 ◽  
pp. 383-389 ◽  
Author(s):  
Ya Hui Peng ◽  
Kang Peng ◽  
Jian Zhou ◽  
Zhi Xiang Liu
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Rock Burst ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Rock Burst Risk ◽  
Rock Burst Prediction ◽  
Optimization Search

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.

Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China

2016 ◽  
Vol 17 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Yihui Fang ◽  
Xingwei Chen ◽  
Nian-Sheng Cheng
Keyword(s):  
Prediction Accuracy ◽  
Computing Time ◽  
High Tide ◽  
River Estuary ◽  
Support Vector ◽  
Min River ◽  
Proposed Model ◽  
Svm Model ◽  
Tide Level ◽  
Min River Estuary

Estuary salinity predictions can help to improve water safety in coastal areas. Coupled genetic algorithm-support vector machine (GA-SVM) models, which adopt a GA to optimize the SVM parameters, have been successfully applied in some research fields. In light of previous research findings, an application of a GA-SVM model for tidal estuary salinity prediction is proposed in this paper. The corresponding model is developed to predict the salinity of the Min River Estuary (MRE). By conducting an analysis of the time series of daily salinity and the results of simulation experiments, the high-tide level, runoff and previous salinity are considered as the major factors that influence salinity variation. The prediction accuracy of the GA-SVM model is satisfactory, with coefficient of determination (R2) of 0.85, Nash–Sutcliffe efficiency of 0.84 and root mean square error of 119 (μS/cm). The proposed model performs significantly better than the traditional SVM model in terms of prediction accuracy and computing time. It can be concluded that the proposed model can successfully predict the salinity of MRE based on the high-tide level, runoff and previous salinity.

Human Activity Recognition Using The Human Skeleton Provided by Kinect

2021 ◽  
Vol 17 (2) ◽  
pp. 183-189
Author(s):  
Heba Salim ◽  
Musaab Alaziz ◽  
Turki Abdalla
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Human Body ◽  
Support Vector ◽  
Classification Models ◽  
Kinect Sensor ◽  
People Tracking ◽  
Human Skeleton ◽  
Average Accuracy ◽  
Svm Model

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.

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