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Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models

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GEOMATICS NATURAL HAZARDS & RISK 2018年第1期9卷 1053-1070页

作者： Oh, Hyun-Joo Kadavi, Prima Riza Lee, Chang-Wook Lee, Saro Korea Inst Geosci & Mineral Resources Deajeon South Korea Kangwon Natl Univ Div Sci Educ Chunchon Gangwon Do South Korea

The main purpose of this study was to produce landslide susceptibility maps using evidential belief function (EBF), logistic regression (LR) and support vector machine (SVM) models and to compare their results for the region surrounding Yongin, South Korea. We compiled a landslide inventory map of 82 landslides based on reports and aerial photographs and confirmed these data through extensive field surveys. All landslides were randomly separated into two data sets of 41 landslide data points each;half were selected to establish the model, and the remaining half were used for validation. We divided 18 landslide conditioning factors into the following four categories: topography factors, hydrology factors, soil map and forest map;these were considered for landslide susceptibility mapping. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the EBF, LR and SVM models. The three models were then validated using the area under the curve (AUC) method. According to the validation results, the prediction accuracy of the LR model (AUC = 94.59%) was higher than those of the EBF model (AUC = 92.25%) and the SVM model (AUC = 81.78%);the LR model also had the highest training accuracy.

关键词： Area under the curve evidential belief function model landslide susceptibility logistic regression support vector machine model

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Using support vector machine models for crash injury severity analysis

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ACCIDENT ANALYSIS AND PREVENTION 2012年 45卷 478-486页

作者： Li, Zhibin Liu, Pan Wang, Wei Xu, Chengcheng Southeast Univ Sch Transportat Nanjing 210096 Jiangsu Peoples R China

The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does. The research also investigated the potential of using the SVM model for evaluating the impacts of external factors on crash injury severities. The sensitivity analysis results show that the SVM model produced comparable results regarding the impacts of variables on crash injury severity as compared to the OP model. For several variables such as the length of the exit ramp and the shoulder width of the freeway mainline, the results of the SVM model are more reasonable than those of the OP model. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： support vector machine model Ordered probit model Crash severity Freeway diverge area

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Investigating driver injury severity patterns in rollover crashes using support vector machine models

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ACCIDENT ANALYSIS AND PREVENTION 2016年 90卷 128-139页

作者： Chen, Cong Zhang, Guohui Qian, Zhen Tarefder, Rafiqul A. Tian, Zong Univ New Mexico Dept Civil Engn Albuquerque NM 87131 USA Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Heinz Coll Pittsburgh PA 15213 USA Univ Nevada Dept Civil & Environm Engn Reno NV 89557 USA

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Driver injury severity Rollover crash support vector machine model Kernel function Traffic safety

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Using support vector machine models for Size of Motorcycle Taxi Terminal in Bang Yai District Analysis 19

Using Support Vector Machine Models for Size of Motorcycle T...

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8th International Conference on Informatics, Environment, Energy and Applications (IAEA)

作者： Worachairungreung, Morakot Thanakunwutthirot, Kunyaphat Songka, Thammasak Suan Sunandha Rajabhat Univ Fac Humanities & Social Sci Bangkok Thailand Suan Sunandha Rajabhat Univ Fuculty Ind Technol Bangkok Thailand Burapha Univ Acad Serv Ctr Chon Buri Thailand

ISBN: (纸本)9781450361040

This study aims to study the factors of building usage that influence the size of the motorcycle taxi terminal. Also, as there are many factors of building usage, this research aims to study whether the modern non-parametric model, such as, the support vector machine (SVM), is able to classify the size of the motorcycle taxi terminal, using the dataset of the building usage. Based on random 50 grid cells, with the size of 500 meters. The ratio of the correct classification of the training data is 85 percents, whereas the SVM model is able to predict the size correctly. The ratio of the correct classification of the testing data is 100 percents. Even though, one of the SVM model's limitations is the Black Box problem. The Black Box prevents the model from classifying the factors that directly influence the motorcycle taxi terminal's sizes. Therefore, the author uses that dataset for the correlation test, in order to find the correlation coefficient. In this case, the factors that are highly correlated with the motorcycle taxi terminal's sizes are the number of ATMs and the bus stations.

关键词： support vector machine model Motorcycle taxi terminal Correlation Building usage

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Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2010年第1期10卷 16-16页

作者： Yu, Wei Liu, Tiebin Valdez, Rodolfo Gwinn, Marta Khoury, Muin J. Ctr Dis Control & Prevent Natl Off Publ Hlth Genom Coordinating Ctr Hlth Promot Atlanta GA 30333 USA

Background: We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U. S. population. Methods: We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or prediabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. Results: For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables-sex and physical activity-were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Conclusions: support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.

关键词： support vector machine Classification Scheme support vector machine model Undiagnosed Diabetes support vector machine Algorithm

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AntiBP2: improved version of antibacterial peptide prediction

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BMC BIOINFORMATICS 2010年第s1期11卷 1-7页

作者： Lata, Sneh Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh India

Background: Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using support vector machine (SVM). Results: During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result. Conclusion: Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial pep

关键词： support vector machine Antimicrobial Peptide support vector machine model Independent Dataset Antibacterial Peptide

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A novel approach for protein subcellular location prediction using amino acid exposure

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BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Mer, Arvind Singh Andrade-Navarro, Miguel A. Max Delbruck Ctr Mol Med D-13125 Berlin Germany

Background: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure. Results: Our method has two stages. For stage one, we trained multiple support vector machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity. Conclusions: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem.

关键词： support vector machine Artificial Neural Network Amino Acid Composition support vector machine model Protein Pair

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Data-enhanced machine recognition model of DC serial arc in electric vehicle power system

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IET POWER ELECTRONICS 2020年第19期13卷 4677-4684页

作者： Xia, Kun Zhang, Zihan Liu, Bangzheng Guo, Haotian Yu, Wei Xu, Jingjun Dong, Hui Univ Shanghai Sci & Technol Elect Engn Dept 516 JunGong Rd Shanghai 200093 Peoples R China Hella Shanghai Elect Co Ltd R&D Ctr Shanghai 201201 Peoples R China

Electric vehicle (EV) power system is the key to the development of EVs. If direct current (DC) arc occurs in the power system, it is difficult to extinguish at zero point. The arc fault will release a huge amount of energy and continuous sparking, which may cause spontaneous combustion or even explosion. In this study, an arc detection algorithm based on the classification of windowed Fourier transform and support vector machine (SVM) model is proposed for DC serial arc detection of EV power system. In order to optimise the arc detection algorithm, the authors use the pre-detection algorithm, which can effectively reduce the false detection rate of DC arc fault and ensure the reliability of detection algorithm. In addition, they propose an arc fault data enhancement model, which can generate arc fault current data. Finally, the experimental results show that the arc detection algorithm has a high accuracy and a false detection rate of 0%. After data enhancement, it has generalisation of the SVM classification model under the condition of high power experiment.

关键词： arcs (electric) fault diagnosis electric vehicles support vector machines fault currents Fourier transforms power engineering computing direct current arc arc detection algorithm support vector machine model DC serial arc detection EV power system pre-detection algorithm false detection rate DC arc fault arc fault data enhancement model arc fault current data SVM classification model high power experiment data-enhanced machine recognition model electric vehicle power system windowed Fourier transform classification

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Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein

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BMC BIOINFORMATICS 2005年第1期6卷 1-14页

作者： Raghava, GPS Han, JH Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang 790784 South Korea Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India

Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., ( 1998) to understand the relationship between expression level and amino acid composition. Results: We compute the correlation between expression of a gene and amino acid composition of its protein. It was observed that some residues ( like Ala, Gly, Arg and Val) have significant positive correlation ( r > 0.20) and some other residues ( Like Asp, Leu, Asn and Ser) have negative correlation ( r < - 0.15) with the expression of genes. A significant negative correlation ( r = - 0.18) was also found between length and gene expression. These observations indicate the relationship between percent composition and gene expression level. Thus, attempts have been made to develop a support vector machine ( SVM) based method for predicting the expression level of genes from its protein sequence. In this method the SVM is trained with proteins whose gene expression data is known in a given condition. Then trained SVM is used to predict the gene expression of other proteins of the same organism in the same condition. A correlation coefficient r = 0.70 was obtained between predicted and experimentally determined expression of genes, which improves from r = 0.70 to 0.72 when dipeptide composition was used instead of residue composition. The method was evaluated using 5-fold cross validation test. We also demonstrate that amino acid composition information along with gene expression data can be used for improving the fu

关键词： support vector machine Amino Acid Composition support vector machine model Percent Composition Reference Dataset

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Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

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BMC BIOINFORMATICS 2008年第1期9卷 1-9页

作者： Zhang, Guang Lan Khan, Asif M. Srinivasan, Kellathur N. Heiny, A. T. Lee, K. X. Kwoh, Chee Keong August, J. Thomas Brusic, Vladimir Inst Infocomm Res Singapore 119613 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Biochem Singapore 117597 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Microbiol Singapore 117597 Singapore Johns Hopkins Sch Med Dept Pharmacol & Mol Sci Baltimore MD 21205 USA Hlth Sci Author Ctr Drug Adm Prod Evaluat & Registrat Div Singapore 138667 Singapore Dana Farber Canc Inst Canc Vaccine Ctr Boston MA 02115 USA Univ Queensland Sch Land Crop & Food Sci Brisbane Qld 4072 Australia

Background: T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots. Results: Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and support vector machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the fo

关键词： Human Leukocyte Antigen support vector machine model Human Leukocyte Antigen Class Myelin Oligodendrocyte Glycoprotein NCBI Accession

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