智能学习系统与应用(英文)最新文献_第9页

Study on the Real-Time Security Evaluation for the Train Service Status Using Safety Region Estimation 基于安全区域估计的列车服务状态实时安全评估研究

智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54025

Guiling Liao, Yong Qin, Xiaoqing Cheng, Lisha Pan, Lin He, Shan Yu, Yuan Zhang

{"title":"Study on the Real-Time Security Evaluation for the Train Service Status Using Safety Region Estimation","authors":"Guiling Liao, Yong Qin, Xiaoqing Cheng, Lisha Pan, Lin He, Shan Yu, Yuan Zhang","doi":"10.4236/JILSA.2013.54025","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54025","url":null,"abstract":"For the important issues of security service of rail vehicles, the online quantitative security assessment method of the service status of rail vehicles and the key equipments is urgently needed, so the method based on safety region was proposed in the paper. At first, the formal description and definition of the safety region were given for railway engineering practice. And for the research objects which their models were known, the safety region estimation method of system stability analysis based on Lyapunov exponent was proposed; and for the research objects which their models were unknown, the data-driven safety region estimation method was presented. The safety region boundary equations of different objects can be obtained by these two different approaches. At last, by real-time analysis of the location relationship and generalized distance between the equipment service status point and safety region boundary, the online safety assessment model of key equipments can be established. This method can provide a theoretical basis for online safety evaluation of trains operation; furthermore, it can provide support for real-time monitoring, early warning and systematic maintenance of rail vehicles based on the idea of active security.a","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"221-226"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Neural Networks to Matlab Analyzed Hyperspectral Images for Characterization of Composite Structures 神经网络在Matlab分析复合材料结构高光谱图像中的应用

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53016

M. Iskandarani

引用次数: 4

Feature Selection for Time Series Modeling 时间序列建模的特征选择

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53017

Qing‐Guo Wang, Xian Li, Qin Qin

引用次数: 11

Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course 不同机器学习方法预测结核病治疗过程结果的评估与比较

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53020

S. R. N. Kalhori, X. Zeng

{"title":"Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course","authors":"S. R. N. Kalhori, X. Zeng","doi":"10.4236/JILSA.2013.53020","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53020","url":null,"abstract":"Tuberculosis treatment course completion is crucial to protect patients against prolonged infectiousness, relapse, lengthened and more expensive therapy due to multidrug resistance TB. Up to 50% of all patients do not complete treatment course. To solve this problem, TB treatment with patient supervision and support as an element of the “global plan to stop TB” was considered by the World Health Organization. The plan may require a model to predict the outcome of DOTS therapy; then, this tool may be used to determine how intensive the level of providing services and supports should be. This work applied and compared machine learning techniques initially to predict the outcome of TB therapy. After feature analysis, models by six algorithms including decision tree (DT), artificial neural network (ANN), logistic regression (LR), radial basis function (RBF), Bayesian networks (BN), and support vector machine (SVM) developed and validated. Data of training (N = 4515) and testing (N = 1935) sets were applied and models evaluated by prediction accuracy, F-measure and recall. Seventeen significantly correlated features were identified (P CI = 0.001 - 0.007); DT (C 4.5) was found to be the best algorithm with %74.21 prediction accuracy in comparing with ANN, BN, LR, RBF, and SVM with 62.06%, 57.88%, 57.31%, 53.74%, and 51.36% respectively. Data and distribution may create the opportunity for DT out performance. The predicted class for each TB case might be useful for improving the quality of care through making patients’ supervision and support more case—sensitive in order to enhance the quality of DOTS therapy.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"2013 1","pages":"184-193"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches 利用韵律特征识别阿拉伯语独白中的问句和非问句片段:新型2型模糊逻辑和基于灵敏度的线性学习方法

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53018

S. O. Olatunji, L. Cheded, W. Al-Khatib, O. Khan

{"title":"Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches","authors":"S. O. Olatunji, L. Cheded, W. Al-Khatib, O. Khan","doi":"10.4236/JILSA.2013.53018","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53018","url":null,"abstract":"In this paper, we extend our previous study of addressing the important problem of automatically identifying question and non-question segments in Arabic monologues using prosodic features. We propose here two novel classification approaches to this problem: one based on the use of the powerful type-2 fuzzy logic systems (type-2 FLS) and the other on the use of the discriminative sensitivity-based linear learning method (SBLLM). The use of prosodic features has been used in a plethora of practical applications, including speech-related applications, such as speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. In this paper, we continue to specifically focus on the Arabic language, as other languages have received a lot of attention in this regard. Moreover, we aim to improve the performance of our previously-used techniques, of which the support vector machine (SVM) method was the best performing, by applying the two above-mentioned powerful classification approaches. The recorded continuous speech is first segmented into sentences using both energy and time duration parameters. The prosodic features are then extracted from each sentence and fed into each of the two proposed classifiers so as to classify each sentence as a Question or a Non-Question sentence. Our extensive simulation work, based on a moderately-sized database, showed the two proposed classifiers outperform SVM in all of the experiments carried out, with the type-2 FLS classifier consistently exhibiting the best performance, because of its ability to handle all forms of uncertainties.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"165-175"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis 基于人工神经网络的心脏病诊断决策支持系统

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53019

S. Ghwanmeh, A. Mohammad, A. Al-Ibrahim

{"title":"Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis","authors":"S. Ghwanmeh, A. Mohammad, A. Al-Ibrahim","doi":"10.4236/JILSA.2013.53019","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53019","url":null,"abstract":"Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"71 1","pages":"176-183"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 64

Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine 基于支持向量机的彩色数字图像中糖尿病视网膜渗出物的识别

智能学习系统与应用(英文) Pub Date : 2013-07-31 DOI: 10.4236/JILSA.2013.53015

R. Mansour, E. M. Abdelrahim, A. Al‐Johani

引用次数: 15

Randomized Algorithm for Determining Stabilizing Parameter Regions for General Delay Control Systems 一般时滞控制系统稳定参数区域的随机化确定算法

智能学习系统与应用(英文) Pub Date : 2013-05-20 DOI: 10.4236/JILSA.2013.52011

Chao Yu, B. Le, Xian Li, Qing‐Guo Wang

引用次数: 2

Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks 基于小波包系数和径向基函数神经网络的人脸识别

智能学习系统与应用(英文) Pub Date : 2013-05-20 DOI: 10.4236/JILSA.2013.52013

T. Kathirvalavakumar, J. Vasanthi