A method based on projection entropy for image recognition was introduced in this paper. Since original definition of projection entropy does not make full use of image information and is not scale invariant, a new definition was proposed. The Local Projection Entropy (LPE) of normalized image was used for image recognition. In the process of recognition, firstly, Gaussian Mixture Model (GMM) of training set images’ LPE was obtained by Expectation Maximization (EM) algorithm. Then the Mahalanobis distance of target image’s LPE and GMM was calculated. The category of image was determined according to the distance discriminant law. Computer vision laboratory databases of Columbia university were used in the experiments, and the results show that the proposed algorithm is an effective approach for image recognition and has a proper structure for parallel computing.

The application of traditional software prediction model has poor generalized performance. This paper put forward a software reliability prediction model based on Learning Vector Quantization (LVQ) neural network. First, this paper analyzed the structure characteristics of LVQ neural network and its relation with software reliability prediction. Then the network was used to predict the software reliability. In the end, the authors confirmed the algorithm through multiple simulation experiments under the Matlab environment and the data from Metrics Data Program (MDP) database of National Aeronautics and Space Administration （NASA） of USA. The experimental results indicate that the method is feasible and has a higher prediction precision than the traditional software prediction method.