Malicious URL Detection Algorithm Based on Multi Neural Network Series

Convolutional neural network based on attention mechanism and a bidirectional independent recurrent neural network tandem joint algorithm (CATIR) are proposed. In natural language processing related technologies, word vector features are extracted based on URLs, and the extracted URL information features and host information features are merged. The proposed CATIR algorithm uses CNN (Convolutional Neural Network) to obtain the deep local features in the data, uses the Attention mechanism to adjust the weights, and uses IndRNN (Independent Recurrent Neural Network) to obtain the global features in the data. The experimental results shows that the CATIR algorithm has significantly improved the accuracy of malicious URL detection based on traditional algorithms to 96.9%.

Multi-neural network-based sentiment analysis of food reviews based on character and word embeddings

Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.

BNNG Algorithm Modeling for Vehicle Classification Recognition under Non Line-of -sight Environment

At present, the automatic classification of vehicles on roads is mostly based on image recognition, and there are defects in adaptability under non-line-of-sight environments. In this paper, based on the similarity of the integration of the ecosystem model and multi-neural network model, an artificial neural network group (BNNG) algorithm was proposed. The vehicle’s driving acoustic signal was taken as the research object, and it was calculated using the Artificial Neural Network (BNNG) algorithm to achieve automatic classification and recognition of vehicle models. Through experimental tests, it is shown that under non-line-of-sight environments, the accuracy of vehicle classification can be improved, and the misrecognition rate of similar models can be greatly reduced. This provided a new method for the automatic classification and identification of vehicles on roads, which was of great significance to monitor vehicle safety in non-line-of-sight environments.