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Dai Y.,Shanghai University of Finance and Economics | Han D.,Shanghai University of Finance and Economics | Han D.,Shanghai Financial Information Technology Key Research Laboratory | Dai W.,Fudan University
The Scientific World Journal | Year: 2014

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. © 2014 Yonghui Dai et al. Source


Han D.,Shanghai University of Finance and Economics | Han D.,Shanghai Financial Information Technology Key Research Laboratory | Shi Y.,Shanghai University of Finance and Economics | Wang W.,Shanghai University of Finance and Economics | Dai Y.,Shanghai University of Finance and Economics
Journal of Software | Year: 2013

This paper proposes a framework of multisource geo-knowledge discovery with association rules. Taking into account spatial data exist semantic fuzziness, and the conversion between qualitative concept and quantitative description is uncertain, in our study, conceptual partition algorithm and membership grade judgment algorithm based on cloud model was used. Meanwhile, there are many correlations among concepts in the field of geoscience, and the underlying correlation also need to be found with different membership grade functions, therefore, a method of multi-level association rules mining was proposed. In order to enhance frequent item set discovery efficiency, improved FP (Frequent Pattern)-Growth algorithm was presented. The algorithm was used in the empirical research on judgment of fault property at the south of Longmen Mountains in Chengdu city of China. The empirical result shows that the improved FP-Growth model acts better in frequent item-set mining. © 2013 Academy Publisher. Source


Han D.,Shanghai University of Finance and Economics | Han D.,Shanghai Financial Information Technology Key Research Laboratory | Dai Y.,Shanghai University of Finance and Economics | Han T.,Shanghai University of Finance and Economics | And 2 more authors.
Computational Intelligence and Neuroscience | Year: 2015

With the rapid development of the internet and information technology, the increasingly diversified portable mobile terminals, online shopping, and social media have facilitated information exchange, social communication, and financial payment for people more and more than ever before. In the meantime, information security and privacy protection have been meeting with new severe challenges. Although we have taken a variety of information security measures in both management and technology, the actual effectiveness depends firstly on people's awareness of information security and the cognition of potential risks. In order to explore the new technology for the objective assessment of people's awareness and cognition on information security, this paper takes the online financial payment as example and conducts an experimental study based on the analysis of electrophysiological signals. Results indicate that left hemisphere and beta rhythms of electroencephalogram (EEG) signal are sensitive to the cognitive degree of risks in the awareness of information security, which may be probably considered as the sign to assess people's cognition of potential risks in online financial payment. © 2015 Dongmei Han et al. Source


Chen H.,Shanghai University of Finance and Economics | Chen H.,Shanghai Open University | Han D.,Shanghai University of Finance and Economics | Han D.,Shanghai Financial Information Technology Key Research Laboratory | And 3 more authors.
Computational Intelligence and Neuroscience | Year: 2015

In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of "C programming language" are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate. © 2015 Haijian Chen et al. Source


Han D.,Shanghai University of Finance and Economics | Han D.,Shanghai Financial Information Technology Key Research Laboratory | Dai Y.,Shanghai University of Finance and Economics | Zhang Z.,Shanghai University of Finance and Economics
Journal of Software | Year: 2013

Currently, many scholars have theoretically studied the early warning index of real estate market, but few have focused on the implementation of the system. Our study provides a system for early warning and monitoring the economic situation in real estate market. We construct a boom index for China's real estate market based on the state space model. In particular, we provide users with intuitive graphical interface, which helps regulators distinguish the economic situation of real estate market in a scientific and convenient way. In addition, the system implementation includes some key technologies, such as model-solving, conversion and fusion among multi-data sources, cross-language invoking and hybrid programming. © 2013 ACADEMY PUBLISHER. Source

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