scholarly journals Sparse representation optimization of Gaussian mixed feature of image based on convolution neural network

2022 ◽  
Author(s):  
Yuguang Ye
Keyword(s):  
Neural Network ◽  
Sparse Representation ◽  
Convolution Neural Network

scholarly journals Research on software credibility algorithm based on deep convolutional sparse coding

2021 ◽  
Vol 336 ◽  
pp. 08013
Author(s):  
Zhaosheng Xu
Keyword(s):  
Neural Network ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Classification System ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the algorithm from two aspects: factor processing based on deep convolution neural network and trust classification based on sparse representation.

scholarly journals Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

2021 ◽  
Author(s):  
Yuguang Ye
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Sparse Representation ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Response Speed ◽  
Gaussian Mixture ◽  
Image Features ◽  
Convolution Neural Network ◽  
Processing Efficiency

Abstract With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

Identification of Violence Images Using Convolution Neural Network in Social Network

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
Santhi Baskaran ◽  
Jahnavi Korrapati ◽  
Sooriya K. ◽  
Pavithra R.
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Convolution Neural Network

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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