scholarly journals International trade and finance exploration using network model of computer trade platform

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260883
Author(s):  
Yi Zhang ◽  
Yi Yuan
Keyword(s):  
Big Data ◽  
International Trade ◽  
Foreign Trade ◽  
Network Model ◽  
Forecast Error ◽  
Rapid Development ◽  
Practical Significance ◽  
Prediction Algorithm ◽  
Dynamic Prediction ◽  
Prediction Mechanism

International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.

scholarly journals Research on the Effect of DPSO in Team Selection Optimization under the Background of Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Qian Zhao ◽  
Lian-ying Zhang
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Particle Swarm ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Enterprise Information ◽  
Strategic Objectives ◽  
Management Platform ◽  
Team Selection ◽  
Swarm Algorithm

Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data.

Research on big data audit based on financial sharing service model using fuzzy AHP

2021 ◽  
pp. 1-10
Author(s):  
Sai Jiang
Keyword(s):  
Big Data ◽  
Fuzzy Ahp ◽  
Evaluation Model ◽  
Rapid Development ◽  
Implementation Process ◽  
Practical Significance ◽  
Service Model ◽  
Specific Content ◽  
Process Framework ◽  
Data Audit

With the rapid development of artificial intelligence and big data technology, the traditional audit method has been constantly impacted by big data. In the era of big data, enterprises actively explore and build a financial sharing service model, and through this model, build audit methods based on big data. In this paper, based on the financial sharing service model, we elaborate the preprocessing process of big data collection, clarity and storage, and build the simulation process framework of big data audit under the service model. Evaluation model is developed based on fuzzy analytic hierarchy process (AHP) and methodology for order estimation by similarity of solution. Finally, on the basis of the implementation process framework, the specific content of each link of big data audit is briefly given. Under the financial sharing service mode, it provides theoretical guidance and practical significance for the implementation of big data audit

REPUBLIC OF UZBEKISTAN AND THE EURASIAN ECONOMIC UNION: INTEGRATION OPPORTUNITIES

2020 ◽  
Vol 9 (3) ◽  
pp. 87-99
Author(s):  
Nabi Ziyadullayev ◽  
◽  
Ulugbek Ziyadullayev ◽  
Keyword(s):  
International Trade ◽  
Foreign Trade ◽  
Central Asia ◽  
National Economy ◽  
Effective Interaction ◽  
Regional Powers ◽  
Eurasian Economic Union ◽  
Economic Union ◽  
The Republic ◽  
Cis Countries

The article reveals the features of the international trade, economic and integration priorities of the Republic of Uzbekistan. The conceptual approaches to joining the WTO, diversification of the geography and structure of foreign trade, as well as the expansion of foreign economic cooperation with world and regional powers, the CIS countries and Central Asia are substantiated. Particular attention is paid to risks and building vectors for effective interaction with the Eurasian Economic Union (EAEU), as well as mitigating the effects of the coronavirus pandemic on the national economy.

scholarly journals Administrative Costs and Tariff Rates in the Presence of Customs Evasion: Evidence from Ecuador

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Jazmín González Aguirre ◽  
Alberto Del Villar
Keyword(s):  
International Trade ◽  
Transfer Function ◽  
Gross Domestic Product ◽  
Foreign Trade ◽  
Transfer Function Model ◽  
Administrative Cost ◽  
Function Model ◽  
Administrative Costs ◽  
Tariff Rates ◽  
Budgetary Expenditure

This paper seeks to assess the effectiveness of customs policies in increasing the resources devoted to controlling and inspection. Specifically, it seeks to analyze whether an increase in the administrative cost of collecting taxes on foreign trade in Ecuador contributes to reducing customs fraud. To this end, we identify and estimate a transfer function model (ARIMAX), considering information on foreign trade such as official international trade statistics report and tariff rates, as well as the execution of budgetary expenditure and Ecuador’s gross domestic product (GDP). The period under study includes quarterly series from 2006 to 2018. The results obtained by the model indicate that allocating greater material and budgetary resources to combat customs fraud does not always achieve the objective of reducing customs evasion.

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.

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

scholarly journals Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Sehrish Malik ◽  
DoHyeun Kim
Keyword(s):  
Smart Factory ◽  
Prediction Algorithm ◽  
Utilization Rate ◽  
Task Management ◽  
Management Mechanism ◽  
Machine Maintenance ◽  
Machine Utilization ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

2021 ◽  
pp. 002072092098399
Author(s):  
Zhang Xiao-Wen ◽  
Zeng Min
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Fuzzy Model ◽  
Reference Value ◽  
Developed Countries ◽  
Theoretical Research ◽  
Prediction Algorithm ◽  
Real Economy ◽  
Stock Volatility ◽  
The Us

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.

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