Information Computing and Applications: Second International by Jiuyuan Huo, Yaonan Zhang (auth.), Baoxiang Liu, Chunlai

By Jiuyuan Huo, Yaonan Zhang (auth.), Baoxiang Liu, Chunlai Chai (eds.)

This e-book constitutes the refereed lawsuits of the second one overseas convention on info Computing and functions, ICICA 2010, held in Qinhuangdao, China, in October 2011.
The ninety seven papers provided have been rigorously reviewed and chosen from quite a few submissions. they're geared up in topical sections on computational economics and finance, computational records, cellular computing and purposes, social networking and computing, clever computing and purposes, web and net computing, paralelle and allotted computing, and method simulation and computing.

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Read or Download Information Computing and Applications: Second International Conference, ICICA 2011, Qinhuangdao, China, October 28-31, 2011. Proceedings PDF

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Sample text

It applies principles of evolution to the challenges of problem solving by creating an initial set of guesses as potential solutions to a problem and then repeatedly rearrange these guesses in the order of how well they solve the problem through selection, cross-over and mutation until a good solution is found [10]. The strength of genetic algorithms is that they only require one to recognize a good solution. However, there is no guarantee of an optimal solution. CBR uses old experiences to solve current problems.

Fig. 4 shows the real observed values and predicted rate for sale for both MLR and neural network (labeled as “NN” in Fig. 4) models, using the testing data. Fig. 4. Comparison of MLR and neural network in prediction The goodness of fits for the obtained MLR and neural network model was calculated by mean percentage error (MPE) and mean square error (MSE). The MPE and MSE are computed as MPE = 1 n y t − yˆ t  n t =1 y t (2) Data Mining for Seasonal Influences in Broiler Breeding Based on Observational Study (3) n MSE = 31 ( y t − yˆ t ) 2  t =1 n where yt equals the observed value at time t , yˆ t equals the estimated value, and n equals the number of observations.

CBR uses old experiences to solve current problems. It retrieves similar cases stored in a case base and adapts the solutions of the retrieved cases to solve a new problem [4]. In CBR, most knowledge is represented in cases. A case is comprised of a problem state, its corresponding solution and/or outcome. CBR is particularly useful in dealing with problems that are impossible or difficult to understand completely. It is capable of dealing with various data types and can justify its solution. The weakness of this approach is that the approach is less appropriate to use when cases are difficult or impossible to obtain [5].

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