Regional Economy Using Hybrid Sequence‐to‐Sequence‐Based Deep Learning Approach
Author(s) -
Bo Peng
Publication year - 2022
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1155/2022/9235012
Subject(s) - field (mathematics) , sequence (biology) , economy , globalization , deep learning , information economy , process (computing) , shift share analysis , world economy , computer science , economic system , artificial intelligence , economics , political science , market economy , genetics , mathematics , pure mathematics , biology , operating system , law
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.
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