Abstract
The influencing factors of community risk are complex. For the low accuracy of traditional prediction model, a multichannel convolutional neural network community risk prediction model is proposed by improving convolutional neural network of deep learning. First of all, in the community risk prediction model, the structure of multichannel input convolutional neural network is selected. Then, add it into the full connection layer. Subsequently, the DenseNet layer is added to establish connections between different network layers. Finally, the receptive field is improved, and the gradient disappearance is solved. Thus, the prediction accuracy of model is improved. Compared with the traditional model, the proposed multichannel convolutional neural network model has better prediction accuracy. In addition, it performs better on the three indicators, namely, correlation coefficient R , coefficient of determination R 2 , and mean square root error RMSE. Compared with the commonly used LSTM model and logic regression model, the proposed model also has certain advantages, which is more suitable for community risk prediction.
Highlights
Aditya et al explored the details of cell phenotypes based on the advantages of deep learning in the recognition, analysis and prediction of visual phenotypes, which provides a reference for the application of the details of cell phenotypes in important biological problems [4]
The proposed model improves the prediction accuracy of model, and the structure of convolutional neural network model is improved
The proposed community risk prediction model improves the input of convolutional neural network
Summary
Community risk is related to the safety of residents in the community and related to the social public stability. A system of urban community safety evaluation indicators is constructed based on the static and dynamic factors. In the convolutional neural network model, multichannel input is adopted, and DenseNet layer is added to establish connections between different network layers, as can be seen that the sensory field and prediction accuracy are improved. There are eight parts for the multichannel convolutional neural network, namely, input layer, convolutional layer, pooling layer, flatten layer, fully connected layer, DenseNet, dropout layer, and output layer. Each feature input is convoluted by two convolution layers, pooled by a maximum pooling layer, and flattened by a flatten layer It passes fully connected layer and DenseNet layer. After passing through the dropout layer with a dropout value of 0.5, the final prediction result is output by the output layer
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