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2nd International Conference on Information Engineering and Applications, IEA 2012

作者： Yu, Ben-Gong Zhao, Shu Ping School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Hefei 230009 China

ISBN: (纸本)9781447148463

Knowledge Management has become one of the core competitions in the enterprise. In order to minimize product development cycle and promote market competitiveness, we need to optimize the level of knowledge management in complex product development processes based on the analysis of the characteristics of enterprise knowledge management for product R&D process, and make use of knowledge management theory and information technology. This paper put forward the knowledge management process of the product R&D enterprise and the architecture model of enterprise knowledge portal (EKP) system, which can provide the theoretical basis and the reference model for the implementation of Enterprise Knowledge Management for product R&D. Finally, the EKP system for product R&D based on the platform of sharepoint is constructed. © 2013 Springer-Verlag.

关键词： Knowledge management

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RS-LSSVM: A new ensemble method for sentiment classification based on random subspace

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ICIC Express Letters, Part B: Applications 2014年第6期5卷 1773-1778页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As analyzing and predicting the polarity of the sentiment plays an important role in understanding social phenomena and general society trends, sentiment classification problem has become a popular topic in academia and industry in recent years. However, comparing with Bagging and Boosting, another popular ensemble method, i.e., Random Subspace, is paid much less attention to the sentiment classification problem. In this research, we propose a new ensemble method, RS-LSSVM, for sentiment classification based on Random Subspace and LSSVM. Ten public sentiment classification datasets are used to verify the effectiveness of the proposed RS-LSSVM. Experimental results reveal that RS-LSSVM can get the better results than the four base learners, Bagging, and Boosting. All these results indicate that RS-LSSVM can be used as an alternative method for sentiment classification.

关键词： Support vector machines

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Development and implementation of WEB-based online hotel reservation system

Development and implementation of WEB-based online hotel res...

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2013 International Conference on Precision Mechanical Instruments and Measurement Technology, ICPMIMT 2013

作者： Hu, Ping Gu, Dong Xiao Department of Management of Hefei University HeFei Anhui 230601 China Key Laboratory of Process Optimization Intelligent Decision-making of Ministry of Education China School of Management at Hefei University of Technology Hefei Anhui 230009 China

ISBN: (纸本)9781629933405

The Internet accelerates the communication and understandings between people, which make information unprecedented important. Furthermore, it changes the way that people book rooms, which makes rooms-booking diversified, convenient, and individualized. Out of the demand of modern hotels and based on the B/S model, this paper analyzes and designs the hotel booking operation, and achieves the functions of register, login-in, reservation, customer management, and reservation management, and etc., in order to improve the efficiency of hotel reservation. © 2013 Trans Tech Publications Ltd, Switzerland.

关键词： Hotels

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Wrapper feature selection embedded Bagging for financial distress prediction

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ICIC Express Letters, Part B: Applications 2013年第2期4卷 375-380页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

The prediction of financial distress for financial institutions has been extensively researched for a long time. Latest studies have shown that such ensemble techniques have performed better than single AI technique in financial distress prediction. In this paper a new wrapper feature selection embedded Bagging, WFS-Bagging, is proposed to predict financial distress. WFS-Bagging utilizes the feature selection, e.g., wrapper feature selection, to enhance the accuracy and diversity of base learners. For the testing and illustration purposes, two real world financial distress data sets are selected to demonstrate the effectiveness and feasibility of proposed method. Experimental results reveal that WFS-Bagging can be used as an alternative technique for the financial distress prediction. © 2013 ICIC International.

关键词： Forecasting

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The utility frequent pattern mining based on slide window in data stream

The utility frequent pattern mining based on slide window in...

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2012 5th International Conference on intelligent Computation Technology and Automation, ICICTA 2012

作者： Li, Feng-Gang Sun, Ying-Jia Ni, Zhi-Wei Liang, Yu Mao, Xue-Ming Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei 230009 China

ISBN: (纸本)9780769546377

In traditional study of mining data stream, each item in the data stream is of equal importance. However, in practice, each item has a different significance, which is known as utility. This paper combines frequent mining item sets with utility and proposes an efficient algorithm for utility frequent pattern mining (UFPM). It combines bitmap with tree structure that can store and update the pattern of data stream quickly and completely by scanning only once. The algorithm generated by lexicographic order, proposes a novel tree U-tree and makes convenience for pattern updating and user reading. With a pattern growth approach in mining, the algorithm can effectively avoid the problem of a mass candidacy generation by level-wise searching. The experiments results show that our algorithm which is in high efficiency and good scalability outperforms the existing analogous algorithm. © 2012 IEEE.

关键词： Data mining

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An Empirical Study on Personality Characteristics of College Volunteers: Based on the Collected Data from Shanghai Expo

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Procedia - Social and Behavioral Sciences 2013年 83卷 969-974页

作者： Ya-di Gu Dong-xiao Gu Qing Wei Shen Pan Wen-en Chen Yi Ge Students’ Affairs Division at University of Shanghai for Science and Technology Shanghai200093 P.R. CHINA School of Management at Hefei University of Technology Hefei Anhui230009 P.R. CHINA School of Foreign Studies at Hefei University of Technology Hefei Anhui230009 P.R. CHINA Engineering Research Center of Intelligent Decision-Making and Information System Technology of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Department of Electronic Information Technology at Tianjin Normal University Tianjin 300387 P.R. CHINA

Objective : This study is to investigate the personality characteristics of student volunteers, which would provide a reference to select and train qualified volunteers for the future. Methods : 233 student volunteers and 236 non-volunteers are selected for our study. Based on the data of 16PF personality exported from the database, statistical software SPSS15.0 is used to conduct data analysis. Result : Volunteers and non-volunteers have significant differences of personality: the 16 basic personality factors, the gregariousness, excitement, daring and independence scores are significantly different; in the other eight binary factors, emotional and serene in the alert type, introverted and extroverted type, timid and bold type factors have significantly different scores. Male and female volunteers have significant personality difference in some respects. Conclusion : College student volunteers have obvious personality traits of extroversion, optimism, cheerfulness, enthusiasm, self-confidence etc. In addition, male and female volunteers have gender differences in personality characteristics in some aspects.

关键词： College students Volunteers Personality traits Shanghai Expo Selection

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Data stream closed frequent itemsets mining in blend window

Data stream closed frequent itemsets mining in blend window

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2012 International Conference on Computer Science and Service System, CSSS 2012

作者： Wu, Hao Wang, Huiying Li, Huaiying Jiang, Miao School of Management Hefei University of Technology India Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China

ISBN: (纸本)9780769547190

In data stream mining, sliding window can record the latest and most useful patterns, but the best size can not be accurately determined. To aim at data with the characteristics of data flow in some simulation systems, this paper proposes a method for mining the closed frequent patterns in the mixed window of data stream. The pattern of data stream could be completely recorded by scanning the stream only once. And the pruning method of T-Moment could reduce the space complexity of sliding window tree and the maintenance cost of the closed frequent patterns tree. To differentiate the historical and the latest patterns, a time decaying model was applied. The experimental results show that the algorithm has good efficiency and accuracy. © 2012 IEEE.

关键词： Forestry

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Short-term power load forecasting based on self-adapting PSO-BP neural network model

Short-term power load forecasting based on self-adapting PSO...

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4th International Conference on Computational and Information Sciences, ICCIS 2012

作者： He, Yaoyao Xu, Qifa School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China

ISBN: (纸本)9780769547893

To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in china are selected in the experiments. Simulation results show that our approach outperforms simple BP neural network. © 2012 IEEE.

关键词： Neural network models

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Analysis on medicine compounding for stroke prevention treated by Xin'an physicians based on association rules

Analysis on medicine compounding for stroke prevention treat...

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2012 5th International Conference on intelligent Computation Technology and Automation, ICICTA 2012

作者： Gao, Luan Li, Feng-Gang Wang, Jian Liang, Yu Li, Yu Chinese Medicine Clinic Institute Anhui Chinese Medical College Hefei 230038 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei 230009 China

ISBN: (纸本)9780769546377

The purpose of this paper is to probe into the rules of medicine compounding for stroke prevention treated by Xin'an physicians by data mining. The method is in two steps. First step is to build the database of the Xin'an physical stroke prevention. Second step is to use the method of association rules contained in professional data mining software and probe into the medicine rules for stroke prevention treated by Xin'an physicians. The result is that Chenpi, Fuling, Gancao, Luxiancao, Xiqiancao, Baijili, and Shinanye which are of the seven common Chinese medical compounding prescriptions can be used for Xin'an physicians' stroke prevention. The conclusion is that we should utilize the eliminating phlegm, calming wind and clearing the channels when Xin'an physicians treat stroke problem. © 2012 IEEE.

关键词： Association rules

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Forecasting Modeling and Analysis of Power Engineering in china Based on Gauss-Chebyshev Formula

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Systems Engineering Procedia 2012年 5卷 131-136页

作者： Xiaojia Wang China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei Anhui. 230009China

This paper used Gauss-Chebyshev formula to construct a new class of gray prediction model- GCGM (1,1) to overcome the lack of existed gray model and made accurate forecasting of electricity consumption for power engineering. A case study using the power engineering data of china is presented to demonstrate the effectiveness of our approach.

关键词： Gauss-Chebyshev formula GM (1 1) model background value forecasting power engineering

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