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Chen W.,Chinese Academy of Agricultural Sciences | Chen W.,Key Laboratory of Agri information Service Technology | Zhao X.,Beijing Research Center for Information Technology in Agriculture | Zhao X.,Chinese National Engineering Research Center for Information Technology in Agriculture
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

In current big data era, there has been an explosive growth of various data. Most of these large volume of data are non-structured or semi-structured (e.g., tweets, weibos or blogs), which are difficult to be managed and organized. Therefore, an effective and efficient classification algorithm for such data is essential and critical. In this article, we focus on a specific kind of non-structured/semi-structured data in our daily life: recipe data. Furthermore, we propose the document model and similarity-based classification algorithm for big non-structured and semistructured recipe data. By adopting the proposed algorithm and system, we conduct the experimental study on a real-world dataset. The results of experiment study verify the effectiveness of the proposed approach and framework. © Springer International Publishing Switzerland 2016. Source

Li D.,CAS Institute of Software | Zhao J.,CAS Institute of Software | Zhang H.,CAS Institute of Software | Qiao P.,CAS Institute of Software | And 2 more authors.
Proceedings - International Conference on Natural Computation | Year: 2016

An Improved Many Worlds Quantum Genetic Algorithm (IMWQGA) was proposed aiming at the shortcomings of the Quantum Genetic Algorithm, such as the multimodal function optimization problems easily falling into the local optimum and vulnerability to premature convergence. Using the concept of Many Worlds and the derivative way of parallel worlds' parallel evolution, we propose to update the population according to the main body and adopt the transition methods, such as parallel transition, backtracking, travel forth and so on. In addition, the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm were also proposed. © 2015 IEEE. Source

Meng X.-X.,Chinese Academy of Agricultural Sciences | Li J.,China National Institute of Standardization | Su X.-L.,Chinese Academy of Agricultural Sciences | Su X.-L.,Key Laboratory of Agri information Service Technology | And 2 more authors.
Journal of Integrative Agriculture | Year: 2012

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management - the knowledge domain framework (KDF), and introduces an integrated development environment (IDE) named large-scale ontology development environment (LODE), which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the principle of knowledge domain framework and LODE was described briefly. © 2012 Chinese Academy of Agricultural Sciences. Source

Li Z.-M.,Chinese Academy of Agricultural Sciences | Xu S.-W.,Key Laboratory of Agri information Service Technology | Cui L.-G.,Key Laboratory of Agri information Service Technology | Li G.-Q.,Key Laboratory of Agri information Service Technology | And 2 more authors.
Advanced Materials Research | Year: 2013

After analyzing and reviewing the short-term forecasting methods research of pork price at home and abroad, a chaotic neural network model based on genetic algorithm (CNN-GA) was put forward according to the nonlinear characteristics of pork price,which established on the base of the chaotic theory and the neural network technology. Chosen the daily retail price data of the pork (streaky pork) from January 1, 2008 to June 11, 2012,we designed the basic structure of CNN-GA, and thentrainedit in order to attain the trained CNN-GA model. Finally, the trained CNN-GA model was used to predict the 20 days' (from June 12, 2012 to July 1, 2012) retail price of pork (streaky pork) and then compared the predicted price with the real price to test the model's forecast accuracy and application ability.The result shows that the model has high prediction precision, good fitting effect and hasan important reference and practical significance for the short-term price forecasting of the pork market. © (2013) Trans Tech Publications, Switzerland. Source

Li G.,Chinese Academy of Agricultural Sciences | Li G.,Key Laboratory of Agri information Service Technology | Li Z.,Chinese Academy of Agricultural Sciences | Li Z.,Key Laboratory of Agri information Service Technology
Emerging Economies, Risk and Development, and Intelligent Technology - Proceedings of the 5th International Conference on Risk Analysis and Crisis Response, RACR 2015 | Year: 2015

It is critical for futures market risk quantitative management to effectively estimate the extreme risk. At present the indicator of Value at Risk (VaR) has been widely used to measure market risk. However, VaR estimation techniques are not in-depth enough to efficiently estimate the complicated agricultural futures market risks. Thus, taking the futures market of soybean oil, rapeseed oil and palm oil for example, this study analyzed summary statistics of futures market price returns during given sample period, established ARMA-GARCH models to estimate downside VaRs with different residual assumptions at the 95%, 97.5% and 99% confidence level respectively, and compared the accuracy of VaR estimation based on residual assumptions of standard normal distribution, Student-t distribution and generalized error distribution. These finding are observed: (i) Significant volatility clustering can be found for futures market returns. Estimating VaR using ARMA-GARCH approach can effectively depict the distribution and volatility of vegetable oil futures market return. (ii)At any confidence level of 95%, 97.5% and 99%, the VaR model based on t distribution is easier to overestimate the futures market risk. (iii) The accuracy of estimated VaR based on GED is better at a higher confidence level. At the confidence level of 95%, VaR model based on standard normal distribution and GED are almost the same in statistical sense. However, at the 97.5% and 99% confidence level, the accuracy of risk measure based on GED is better than that based on standard normal distribution method. © 2015 Taylor & Francis Group, London. Source

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