While BP neural network,classical theory of probability and its derivative on algorithm were used to fire loss prediction,the system structure is complex,the detection data is not stable,and the result is easy to fall into local minimum, etc. To resolve these troubles, a method of reasoning and forecasting the regional fire data was proposed based on adaptive fuzzy Generalized Regression Neural Network (GRNN). The improved fuzzy C-clustering algorithm was used to correct weight for the initial data in network input layer, and it reduced the influence of noise and isolated points on the algorithm, improved the approximation accuracy of the predicted value. The adaptive function optimization of GRNN algorithm was introducd to adjust the expansion speed of the iterative convergence, change the step, and found the global optimal solution. The method was used to resolve the premature convergence problem and improved the search efficiency. While the identified fire loss data is put into the algorithm, the experimental results show that the method can overcome the problem of instable detection data, and has good ability of nonlinear approximation and generalization capability.

The false positive rate and the false negative rate are too high for the open source software Cppcheck, and defects cannot be detected during program running. Interval set algorithm was put forward on the basis of Cppcheck program and was used for detecting array bound overflow. Shaping interval set and array interval set were established by introducing the concept of interval set. Each program variables and expressions interval values were constructed under the framework of Cppcheck to detect contradictions to locate defects. The precision rate increased by 18.5%, the false negative rate decreased by 22.5% and the false positive rate increased by 3.5% with the algorithm compared to Cppcheck. The experimental results show that the proposed algorithm can effectively detect the defects of running program and the detection performance gets improved.

Current Pornographic images filtering algorithms have some problems, such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model based on High-level Semantic Bag-Of-Visual-Words. Firstly, local feature points in sex scene are detected using the SURF algorithm and then high-level semantic dictionary is constructed by fusing the context of the visual vocabularies and spatial-related high-level semantic features of pornographic images. Experimental results show that the model has an accuracy up to 87.6% when testing the multi-person pornographic images, which is significantly higher than the existing pornographic image filtering algorithm based on Bag-Of-Visual-Words.