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
{"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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53016
M. Iskandarani
A novel approach to damage detection in composite structures using hyperspectral image index analysis algorithm with neural network modeling employing Weight Elimination Algorithm (WEA) is presented and discussed. The matrix band based technique allows the monitoring and analysis of a component’s structure based on correlation between sequentially pulsed thermal images. The technique produces several matrices resulting from frame deviation and pixel redistribution calculations with ability for prediction. The obtained results proved the technique to be capable of identifying damaged components with ability to model various types of damage under different conditions.
{"title":"Application of Neural Networks to Matlab Analyzed Hyperspectral Images for Characterization of Composite Structures","authors":"M. Iskandarani","doi":"10.4236/JILSA.2013.53016","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53016","url":null,"abstract":"A novel approach to damage detection in composite structures using hyperspectral image index analysis algorithm with neural network modeling employing Weight Elimination Algorithm (WEA) is presented and discussed. The matrix band based technique allows the monitoring and analysis of a component’s structure based on correlation between sequentially pulsed thermal images. The technique produces several matrices resulting from frame deviation and pixel redistribution calculations with ability for prediction. The obtained results proved the technique to be capable of identifying damaged components with ability to model various types of damage under different conditions.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"143-151"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329734","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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53017
Qing‐Guo Wang, Xian Li, Qin Qin
In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.
{"title":"Feature Selection for Time Series Modeling","authors":"Qing‐Guo Wang, Xian Li, Qin Qin","doi":"10.4236/JILSA.2013.53017","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53017","url":null,"abstract":"In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"152-164"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329785","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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53020
S. R. N. Kalhori, X. Zeng
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.
{"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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53018
S. O. Olatunji, L. Cheded, W. Al-Khatib, O. Khan
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.
{"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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53019
S. Ghwanmeh, A. Mohammad, A. Al-Ibrahim
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%.
{"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}
Pub Date : 2013-07-31DOI: 10.4236/JILSA.2013.53015
R. Mansour, E. M. Abdelrahim, A. Al‐Johani
Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification. In this paper, we present a simple and effective method of detect and classify hard exudates. Automatic detection of hard exudates from retinal images is worth-studying problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Discrete Cosine Transform (DCT) analysis and SVM makes use of color information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 1200 retinal images with variable color, brightness, and quality. Results of the proposed system can achieve a diagnostic accuracy with 97.0% sensitivity and 98.7% specificity for the identification of images containing any evidence of retinopathy.
{"title":"Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine","authors":"R. Mansour, E. M. Abdelrahim, A. Al‐Johani","doi":"10.4236/JILSA.2013.53015","DOIUrl":"https://doi.org/10.4236/JILSA.2013.53015","url":null,"abstract":"Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification. In this paper, we present a simple and effective method of detect and classify hard exudates. Automatic detection of hard exudates from retinal images is worth-studying problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Discrete Cosine Transform (DCT) analysis and SVM makes use of color information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 1200 retinal images with variable color, brightness, and quality. Results of the proposed system can achieve a diagnostic accuracy with 97.0% sensitivity and 98.7% specificity for the identification of images containing any evidence of retinopathy.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"135-142"},"PeriodicalIF":0.0,"publicationDate":"2013-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329693","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}
Pub Date : 2013-05-20DOI: 10.4236/JILSA.2013.52011
Chao Yu, B. Le, Xian Li, Qing‐Guo Wang
This paper proposes a method for determining the stabilizing parameter regions for general delay control systems based on randomized sampling. A delay control system is converted into a unified state-space form. The numerical stability condition is developed and checked for sample points in the parameter space. These points are separated into stable and unstable regions by the decision function obtained from some learning method. The proposed method is very general and applied to a much wider range of systems than the existing methods in the literature. The proposed method is illustrated with examples.
{"title":"Randomized Algorithm for Determining Stabilizing Parameter Regions for General Delay Control Systems","authors":"Chao Yu, B. Le, Xian Li, Qing‐Guo Wang","doi":"10.4236/JILSA.2013.52011","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52011","url":null,"abstract":"This paper proposes a method for determining the stabilizing parameter regions for general delay control systems based on randomized sampling. A delay control system is converted into a unified state-space form. The numerical stability condition is developed and checked for sample points in the parameter space. These points are separated into stable and unstable regions by the decision function obtained from some learning method. The proposed method is very general and applied to a much wider range of systems than the existing methods in the literature. The proposed method is illustrated with examples.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"99-107"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329580","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}
Pub Date : 2013-05-20DOI: 10.4236/JILSA.2013.52013
T. Kathirvalavakumar, J. Vasanthi
An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.
提出了一种基于平均小波包系数、紧凑而有意义的特征向量降维和径向基函数(RBF)神经网络识别的高效人脸识别系统。采用二维小波包变换对人脸图像进行分解。采用两种不同的方法对小波包变换得到的小波包系数进行平均。第一种方法是对一类样本的小波包系数取平均，然后进行分解。第二种方法是对一类样本的小波包系数取平均值。用RBF网络识别小波包平均系数。在olive - oracle Research Lab (ORL)、Japanese Female Facial Expression (JAFFE)和Essexface数据库三个人脸数据库上进行了测试。该方法具有降维、计算复杂度低、识别率高等优点。该方法降低了输入模式的维数，降低了计算复杂度。
{"title":"Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks","authors":"T. Kathirvalavakumar, J. Vasanthi","doi":"10.4236/JILSA.2013.52013","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52013","url":null,"abstract":"An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"115-122"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329637","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}
Pub Date : 2013-05-20DOI: 10.4236/JILSA.2013.52008
T. Sheu, Tzu-Liang Chen, Ching-Pin Tsai, J. Tzeng, C. Deng, M. Nagai
The study analyzed students’ misconception based on rough set theory and combined with interpretive structural model (ISM) to compare students’ degree of two classes. The study then has provided an effective diagnostic assessment tool for teachers. The participants were 30 fourth grade students in Central Taiwan, and the exam tools were produced by teachers for math exams. The study has proposed three methods to get common misconception of the students in class. These methods are “Deleting conditional attributes”, “Using Boolean logic to calculate discernable matrix”, and “Calculating significance of conditional attributes.” The results showed that students of Class A had common misconceptions but students of Class B had not common misconception. In addition, the remedial decision-making for these two classes of students is pointed out. While remedial decision-making of two classes corresponded to structural graph of concepts, it can be found the overall performance of the Class B was higher than Class A.
{"title":"Analysis of Students’ Misconception Based on Rough Set Theory","authors":"T. Sheu, Tzu-Liang Chen, Ching-Pin Tsai, J. Tzeng, C. Deng, M. Nagai","doi":"10.4236/JILSA.2013.52008","DOIUrl":"https://doi.org/10.4236/JILSA.2013.52008","url":null,"abstract":"The study analyzed students’ misconception based on rough set theory \u0000and combined with interpretive structural model (ISM) to compare students’ \u0000degree of two classes. The study then has provided an effective diagnostic \u0000assessment tool for teachers. The participants were 30 fourth grade students in Central Taiwan, and the exam tools were produced by teachers for math exams. The \u0000study has proposed three methods to get common misconception of the students in \u0000class. These methods are “Deleting conditional attributes”, “Using Boolean \u0000logic to calculate discernable matrix”, and “Calculating significance of \u0000conditional attributes.” The results showed that students of Class A had common \u0000misconceptions but students of Class B had not common misconception. In \u0000addition, the remedial decision-making for these two classes of students is \u0000pointed out. While remedial decision-making of two classes corresponded to \u0000structural graph of concepts, it can be found the overall performance of the \u0000Class B was higher than Class A.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"2013 1","pages":"67-83"},"PeriodicalIF":0.0,"publicationDate":"2013-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329521","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：