Application Research of Deep Convolutional Neural Network in Computer Vision
by Lei Wang
Journal of Networking and Telecommunications, Vol.4, No.1, 2022;
368 Views
As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of “abstract conceptsâ€. Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.
Research on Telecom Fraud Detection Model Based on Cellular Network Data
by Kaiyuan Guo, Wenbo Wang
Journal of Networking and Telecommunications, Vol.2, No.1, 2020;
396 Views
With the rapid development of wireless communication technology, the use of mobile phones and other means of communication for telecommunications fraud has become a major problem that endangers user security. Aiming at this problem, this paper constructs a telecom fraud user detection model by in-depth analysis and mining of cellular network data. The model includes data processing, CNNcombine algorithm and model evaluation. First, in the data processing part, the data set is subjected to feature screening, coding, sampling, and the like. Secondly, the CNNcombine algorithm is a combination of a one-dimensional convolutional neural network and multiple traditional classification algorithms. The convolutional neural network is applied to solve classification problems other than text image signals. Finally, in the model evaluation part, it is proved that the CNNcombine algorithm has higher accuracy than the common machine learning classification algorithm such as XGBoost to detect telecom fraud users.
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