scholarly journals TRATAMIENTO DE IMPAGOS BAJO EL ENFOQUE DE LA INCERTIDUMBRE CON LA APLICACIÓN DE REDES NEURONALES (CASO ARTESANOS DE CALZADO CANTÓN GUALACEO PROVINCIA DEL AZUAY)

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Kléber Luna Altamirano ◽  
Jaime Tinto Arandes ◽  
William Sarmiento Espinoza ◽  
Diego Cisneros Quintanilla
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Neural Network Analysis ◽  
The Neural Network

Uno de los problemas más graves que atraviesan las microempresas y empresas en nuestro país, es el referido al problema de los impagos en gestión de tesorería. Este artículo aborda las distintas acciones encaminadas al recobro de un impago mediante la utilización de teoría del expertizaje para alimentar una matriz de efectos olvidados que permita tomar decisiones  y sirva como  instrumento de diseño para representar la política de gestión de cada empresa en las distintas acciones a tomar, es el caso de estudio de los artesanos de calzado del cantón Gualaceo Provincial del Azuay, donde no existe una política adecuada de gestión empresarial para el recobro de impagos por parte de clientes de sus productos. Se indicará paso a paso un grafo representativo de estas políticas a ejecutar en los pagos atrasados por parte de clientes u otras organizaciones con la empresa de calzado, justificando cada arco mediante el uso del análisis de redes neuronales. Una vez conocido la existencia de los impagos se aplica el expertizaje para construir la matriz de convolución que permite descubrir las acciones que han sido olvidadas y que deberá atacar la gerencia para poder tener éxito en la recuperación de los recobros no procesados.Palabras clave: Calzado, expertizaje, impagos, lógica difusa, redes neuronales.  ABSTRACTOne of the most serious problems that the micro companies and companies cross in our country, is recounted to the problem of the non-payments in financiering. This article tackles the different actions directed to the recovery of a non-payment by means of the use of theory of the expertizaje to feed a counterfoil of forgotten effects that allows to take decisions and serves like design instrument to represent the politics of management of every company in the different actions to take, it is the case of study of the craftsmen of footwear of the canton Gualaceo Provincial of the Azuay, where a suitable politics of managerial management does not exist for the non-payments recovery on the part of clients of its products. A graph representative of this politics will be indicated step by step to execute in the payments slowed down on the part of clients or other organizations with the company of footwear, justifying every arch by means of the use of the neural network analysis. Once known the existence of the non-payments applies the expertizaje to itself to construct the counterfoil of convolución that allows to discover the actions that have been forgotten and that the management will have to attack to be able to be successful in the recovery of the not processed recoveries.Keywords: Footwear, expertizaje, non-payments, diffuse logic, neural networks.

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

scholarly journals Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Koffi Dumor ◽  
Li Yao ◽  
Jean-Paul Ainam ◽  
Edem Koffi Amouzou ◽  
Williams Ayivi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Bilateral Trade ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Analysis Techniques ◽  
Belt And Road ◽  
The Impact ◽  
Trade Partners

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

scholarly journals ARCHITECTURE OF A NEURAL NETWORK ANALYZER OF BIOMETRIC PARAMETERS OF LISTENERS OF THE DISTANCE LEARNING SYSTEM

2020 ◽  
pp. 91-99
Author(s):  
Liudmyla Tereikovska
Keyword(s):  
Neural Network ◽  
Distance Learning ◽  
Network Analysis ◽  
Learning System ◽  
Integrated Analysis ◽  
Network Analyzer ◽  
Neural Network Analysis ◽  
Biometric Parameters ◽  
The Neural Network ◽  
Distance Learning System

The urgency of the task of developing tools for neural network analysis of biometric parameters for recognizing the personality and emotions of students of the distance learning system has been substantiated. The necessity of formalizing the architectural solutions used in the creation of software for neural network analysis of biometric parameters is shown. As a result of the research carried out in terms of the UML modeling language, the architecture of the neural network analyzer of biometric parameters has been developed. Diagrams of options for using the neural network analyzer have been developed both for recognizing the personality of a student when entering the system, and for recognizing the personality and emotions of a student in the process of his interaction with the distance learning system. Also, based on the developed use case diagrams, a structural diagram of the analyzer is built. The necessity of including subsystems for determining the functional parameters of the analyzer, registration of biometric parameters, neural network analysis of registered biometric parameters, personality recognition and emotion recognition is substantiated. An original feature of the proposed architectural solutions is the introduction into the neural network analysis subsystem of an integrated analysis module designed to summarize the results of neural network analysis separately for each of the biometric parameters. A rule for making an integrated decision has been developed, taking into account the results of a neural network analysis of each of the registered biometric parameters and the corresponding weight coefficients determined by expert evaluation. The introduction of the integrated analysis module makes it possible to increase the accuracy of recognition of emotions and personality of a student, since the final classification is realized through a generalized assessment of several guaranteed significant biometric parameters. In addition, the use of this module makes it possible to increase the reliability of the neural network analyzer in case of difficulties associated with the registration of a particular biometric parameter. It has been established that the decision-making rule can be improved by using one or more neural networks in the integrated analysis module, designed to generalize the results of the neural network analysis of all registered biometric parameters. It is proposed to correlate the directions of further research with the development of appropriate neural network solutions.

scholarly journals Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 1449 ◽  
Author(s):  
Koffi Dumor ◽  
Li Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
International Trade ◽  
Network Analysis ◽  
Gravity Model ◽  
Accurate Estimation ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Belt And Road ◽  
The Belt And Road

The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.

Analyzing the dynamics of administrative indicators in Russia

2021 ◽  
Vol 17 (2) ◽  
pp. 361-384
Author(s):  
Valerii V. SMIRNOV
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Systems Approach ◽  
Political Stability ◽  
World Bank Group ◽  
Neural Network Analysis ◽  
Governance Indicators ◽  
The Neural Network ◽  
Bank Group ◽  
General Aspects

Subject. The article discusses the dynamics of the Russian indicators of administration. Objectives. I identify constraints that determine administrative criteria. Methods. The study is based on the systems approach and methods of statistical, cluster and neural network analysis. Results. The article spotlights the stewardship of Russia’s economy today and theoretical considerations on general administrative criterion. I analyzed trends in the Russian administrative indicators, referring to six general aspects of management (Worldwide Governance Indicators, World Bank Group). Using the cluster analysis of growth rates of the Russian administrative indicators, I found major and crucial clusters. Conducting the neural network analysis, I understood the hierarchy of priorities, with the governmental efficiency being the most important one. The supremacy of law, political stability and no violence/terrorism were found to of the least significance. Having evaluated the asymmetry of trends in the Russian administrative indicators against the average, I identified the priority, that us the governmental efficiency, which turns to be a determining criterion of management. Conclusions and Relevance. As a result of the study, I understood what hampers the dynamics of the Russian administrative indicators by determining administrative criteria. I especially point out the possibility of raising the governmental efficiency to maintain the political stability and prevent violence/terrorism by neglecting the supremacy of law, regulatory quality and simulating the anti-corruption activity. The findings contribute to the necessary scope of governmental authorities’ competence to make administrative decisions on the effective stewardship of Russia.

scholarly journals Bankruptcy in Indian Private Sector Banks: A Neural Network Analysis

2020 ◽  
Vol 19 (3) ◽  
pp. 89-114
Author(s):  
Surbhi Dhama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Private Sector ◽  
Financial Institutions ◽  
Economic Condition ◽  
Banking Industry ◽  
Financial Data ◽  
Neural Network Analysis ◽  
Private Banks

This paper aims to predict the bankruptcy in Indian private banks using financial ratios such as ROA, GNPA, EPS, PAT, and GNP of the country. This paper also explains the importance of Ohlson’s number, Graham’s number and Zmijewski number as the major predictors of bankruptcy while developing a model using neural networks. For the prediction, the financial data for private sector banks of India such as HDFC, HDFC, ICICI, AXIS, YES bank, KOTAK MAHINDRA Bank, FEDERAL BANK, INDUSIND Bank, RBL and KARUR VYSYA for the last 10 years from 2010-2019 have been analysed. The model developed during the research will help the financial institutions and banks in India to understand the economic condition of the banking industry.

scholarly journals The Neural Network Analysis of Colored Graphs

2016 ◽  
pp. 27-35
Author(s):  
Ilya Germashev ◽  
◽  
Evgeniya Derbisher ◽  
Vyacheslav Derbisher ◽  
Elena Markushevskaya ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Neural Network Analysis ◽  
Colored Graphs ◽  
The Neural Network
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