A neural network systems approach to image processing

2002 ◽  
Author(s):  
C.T. Harston
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Systems Approach ◽  
Network Systems

Public finance flow in contemporary Russia

2020 ◽  
Vol 26 (11) ◽  
pp. 2501-2523
Author(s):  
V.V. Smirnov
Keyword(s):  
Neural Network ◽  
Public Finance ◽  
Systems Approach ◽  
Management Decisions ◽  
Public Authorities ◽  
Cluster Analyses ◽  
The Public ◽  
National Wealth ◽  
Economic Formation

Subject. This article discusses the issues related to public finance. Objectives. The article aims to identify the determinants, indicators, and priorities of the public finance flow in contemporary Russia. Methods. For the study, I used the methods of statistical, neural network, and cluster analyses, and the systems approach. Results. The article identifies and describes the determining indicators of the main aggregates and balances of public finance, sources, and the use of funds. It establishes a link between the main aggregates and balances of public finance, defining the form and content of Russian capitalism. Conclusions. Understanding the issue and problem of public finance flow in contemporary Russia helps identify the reasons for the inability to transit to a capitalist socio-economic formation. The provisions of the study expand the scope of knowledge and develop the competence of public authorities to make management decisions on the distribution and redistribution of the value of a public product and part of the national wealth.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

Multi-Digit Recognition using Image Processing and Neural Network

2021 ◽  
Author(s):  
Anway Shirgaonkar ◽  
Neeraj Sahasrabudhe ◽  
Prathamesh Sandikar ◽  
Tapan Sawant ◽  
Shahid Sayyad ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Digit Recognition
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