scholarly journals Construction of Rural Financial Organization Spatial Structure and Service Management Model Based on Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Liu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Convolutional Neural Network ◽  
Rural Areas ◽  
Financial Services ◽  
Financial Management ◽  
Service Management ◽  
Threshold Value ◽  
Deep Convolutional Neural Network ◽  
Credit Cooperatives

Local credit cooperatives have long played an important role in local financial services. It has made a significant contribution to agricultural production, farmers’ incomes, and the economic development of rural areas. In particular, as a financial instrument serving farmers, microfinance management by local credit cooperatives plays a key role in pursuing profits and fulfilling social responsibility. It was therefore important to obtain effective instruments for combating poverty in rural areas from all walks of society. This paper first outlines the development of microfinance loans in Germany and other countries and describes the current situation and some of the challenges facing local credit cooperatives in financial management. Next, we present the basic concepts of data mining, describe the common methods and key techniques of data mining, analyze and compare the properties of the individual data, and show how the associated mining can actually be performed. Next, we will explain the basic model of microfinance for farmers and some risks in detail and analyze and evaluate the characteristics of these risks in the context of local credit cooperatives. As a result, this paper proposes an improved deep convolutional neural network. The optimized algorithm selects the optimal weight threshold value and different iteration times. The results are fewer errors, the results are closer to the correct data, and the efficiency is better than before. The algorithm is more efficient because errors have been greatly reduced and the time spent on them has been slightly reduced.

scholarly journals Robust feature space separation for deep convolutional neural network training

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ali Sekmen ◽  
Mustafa Parlaktuna ◽  
Ayad Abdul-Malek ◽  
Erdem Erdemir ◽  
Ahmet Bugra Koku
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cost Function ◽  
Geodesic Distance ◽  
Feature Space ◽  
Threshold Value ◽  
Deep Convolutional Neural Network ◽  
Neural Network Training ◽  
Deep Convolutional Neural Networks ◽  
Network Training

AbstractThis paper introduces two deep convolutional neural network training techniques that lead to more robust feature subspace separation in comparison to traditional training. Assume that dataset has M labels. The first method creates M deep convolutional neural networks called $$\{\text {DCNN}_i\}_{i=1}^{M}$$ { DCNN i } i = 1 M . Each of the networks $$\text {DCNN}_i$$ DCNN i is composed of a convolutional neural network ($$\text {CNN}_i$$ CNN i ) and a fully connected neural network ($$\text {FCNN}_i$$ FCNN i ). In training, a set of projection matrices $$\{\mathbf {P}_i\}_{i=1}^M$$ { P i } i = 1 M are created and adaptively updated as representations for feature subspaces $$\{\mathcal {S}_i\}_{i=1}^M$$ { S i } i = 1 M . A rejection value is computed for each training based on its projections on feature subspaces. Each $$\text {FCNN}_i$$ FCNN i acts as a binary classifier with a cost function whose main parameter is rejection values. A threshold value $$t_i$$ t i is determined for $$i^{th}$$ i th network $$\text {DCNN}_i$$ DCNN i . A testing strategy utilizing $$\{t_i\}_{i=1}^M$$ { t i } i = 1 M is also introduced. The second method creates a single DCNN and it computes a cost function whose parameters depend on subspace separations using the geodesic distance on the Grasmannian manifold of subspaces $$\mathcal {S}_i$$ S i and the sum of all remaining subspaces $$\{\mathcal {S}_j\}_{j=1,j\ne i}^M$$ { S j } j = 1 , j ≠ i M . The proposed methods are tested using multiple network topologies. It is shown that while the first method works better for smaller networks, the second method performs better for complex architectures.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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