Regional Economy Using Hybrid Sequence-to-Sequence-Based Deep Learning Approach

In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industri...

Full description

Saved in:
Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2022; no. 1
Main Author Peng, Bo
Format Journal Article
LanguageEnglish
Published Hoboken Hindawi 01.01.2022
Hindawi Limited
Wiley
Subjects
Algorithms
Competitive advantage
Datasets
Deep learning
Economic development
Economic growth
Globalization
Innovations
Machine learning
Neural networks
New technology
Regional analysis
Regional development
Regional planning
Online AccessGet full text

Cover

More Information
Summary:In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industries and country policies. Modern studies tend to participate in important factors in this field such as energy intensity, labour skills, local industries, resources, and local expertise. Furthermore, in this study, to start developing the regional economy and make the revolution in this field to connect it with new technology, we train the deep learning algorithm of gathering factors to manage them perfectly and make a good prediction for the future economy. Hybrid sequence to sequence (seq2seq) algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years.
ISSN:1076-2787
1099-0526
DOI:10.1155/2022/9235012