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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Information Technology Management
- نوع مقاله: Journal Article
- کلمات کلیدی: knowledge management,E-government,Service Chain,Textual datamining
- چکیده:
- چکیده انگلیسی: Systems related to knowledge management can improve quality and efficiency of knowledge used for decision making process. Approximately 80 percent of corporate information are in textual data formats. That is why text mining is useful and important in service chain knowledge management. For example, one of the most important applications of text mining is in managing on-line source of digital documents and the analysis of internal documents. This research is based on text-based documents and textual information and interviews processed by Grounded theory. In this research clustering techniques were applied at first step. In the second step, Apriori association rules techniques for discovering and extracting the most useful association rules were applied. In other words, integration of datamining techniques was emphasized to improve the accuracy and precision of classification. Using decision tree technique for classification may result in reducing classification precision. But, the proposed method showed a significant improvement in classification precision.
- انتشار مقاله: 19-12-1393
- نویسندگان: Jalal Rezaeenour,MohammadReza SheikhBahaei
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Health Management and Informatics
- نوع مقاله: Journal Article
- کلمات کلیدی:
- چکیده:
- چکیده انگلیسی: Introduction: Manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. Various data mining techniques exist for prediction of thermostable proteins. Furthermore, ANN methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate approach to mapping the non-linear input-output relationships and massive parallel computing.Method: An Extreme Learning Machine (ELM) was applied to estimate thermal behavior of 1289 proteins. In the proposed algorithm, the parameters of ELM were optimized using a Genetic Algorithm (GA), which tuned a set of input variables, hidden layer biases, and input weights, to and enhance the prediction performance. The method was executed on a set of amino acids, yielding a total of 613 protein features. A number of feature selection algorithms were used to build subsets of the features. A total of 1289 protein samples and 613 protein features were calculated from UniProt database to understand features contributing to the enzymes’ thermostability and find out the main features that influence this valuable characteristic.Results:At the primary structure level, Gln, Glu and polar were the features that mostly contributed to protein thermostability. At the secondary structure level, Helix_S, Coil, and charged_Coil were the most important features affecting protein thermostability. These results suggest that the thermostability of proteins is mainly associated with primary structural features of the protein. According to the results, the influence of primary structure on the thermostabilty of a protein was more important than that of the secondary structure. It is shown that prediction accuracy of ELM (mean square error) can improve dramatically using GA with error rates RMSE=0.004 and MAPE=0.1003.Conclusion: The proposed approach for forecasting problem significantly improves the accuracy of ELM in prediction of thermostable enzymes. ELM tends to require more neurons in the hidden-layer than conventional tuning-based learning algorithms. To overcome these, the proposed approach uses a GA which optimizes the structure and the parameters of the ELM. In summary, optimization of ELM with GA results in an efficient prediction method; numerical experiments proved that our approach yields excellent results.Keywords: Protein Stability, Primary and secondary structures, Extreme learning machine, Neural networks, Genetic algorithm
- انتشار مقاله: 10-07-1395
- نویسندگان: Jalal Rezaeenour,Mansoureh Yari Eili,Zahra Roozbahani,Mansour Ebrahimi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Computing and Security
- نوع مقاله: Journal Article
- کلمات کلیدی: Artificial Neural Networks,Fuzzy Clustering,Intrusion Detection,Stacking,Ensemble classifiers
- چکیده:
- چکیده انگلیسی: Data mining techniques are widely used for intrusion detection since they have the capability of automation and improving the performance. However, using a single classification technique for intrusion detection might involve some difficulties and limitations such as high complexity, instability, and low detection precision for less frequent attacks. Ensemble classifiers can address these issues as they combine different classifiers and obtain better results for predictions. In this paper, a novel ensemble method with neural networks is proposed for intrusion detection based on fuzzy clustering and stacking combination method. We use fuzzy clustering in order to divide the dataset into more homogeneous portions. The stacking combination method is used to aggregate the predictions of the base models and reduce their errors in order to enhance detection accuracy. The experimental results on NSL-KDD dataset demonstrate that the performance of our proposed ensemble method is higher compared to other well-known classification techniques, particularly when the classes of attacks are small.
- انتشار مقاله: 09-01-1393
- نویسندگان: Mohammad Amini,Jalal Rezaeenour,Esmaeil Hadavandi
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Evaluation of product,fuzzy ANP,Product configuration,TOPSIS-ELECTRE approach
- چکیده:
- چکیده انگلیسی: Product selection is done according to its specifications. In modern competitive markets, product survival refers back to its appropriate price, quality, and innovations in accordance with customers’ needs. In order to increase customers’ satisfaction, the quality of products and services should be improved. In this study, we evaluated different configurations of laptops using Multi-criteria Decision Making (MCDM) approaches. First, we employed a structured questionnaire to collect important features about laptop selection from customers’ viewpoints, and the customers scored the features based on their own opinions. Then, in solving the problem, it was used fuzzy Analytical Hierarchy Process (AHP) to weigh criteria such as product weight, price and time spending for full battery charge. Afterwards, TOPSIS-ELECTRE approach was used to rank laptop alternatives to propose the best one. Based on the results, good price and having main features at a desirable level were identified as main factors to improve configuration and customer satisfaction.
- انتشار مقاله: 04-09-1394
- نویسندگان: Jalal Rezaeenour,Nahid Farzanmanesh,Hossein Amoozad Khalili
- مشاهده