A robust learning approach to predict the growth of Malaysian economy and technology based on system engineering specification

K. K. Karthick, Valliappan Raju, K. K. Ramachandran, Stanley James, Lakshmi, S. Tamil Maran

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

Abstract

World System Theory was developed in the late 1970s by prominent economist Immanuel Wallerstein. The hypothesis proposed a triad of global divisions according to economic status. Although this was similar to Dependency theory, Wallerstein provided further explanation of its effects here. Some indicators of a country's health include its gross domestic product (GDP), foreign direct investment (FDI), foreign exchange reserves (FOREX), export and import revenues, and so on. Each of these variables is important to globalization. To find out how each country is doing in comparison to others, a cross-national study is the way to go. According to World System Theory, there are three distinct economic zones on Earth: the Core, the Semi-Periphery, and the Periphery. The economy of Malaysia is the subject of this research study. An analysis was conducted to determine which of the three World System Theory options best suited the Malaysian economy. Malacca has been experiencing rapid economic growth since 1969, marking the beginning of its post-development phase. Foreign commerce has increased in Malaysia due to the country's large migrant population. Nevertheless, Malaysia is still considered to be on the perimeter. Economists should not rely on assumptions but rather provide evidence based on sound theory. No need to challenge its indicators as nearly all economists has classified Malaysia as a semi-periphery nation; nonetheless, proper-investigation needs to be done routinely at specified intervals. Until June 2015, the purpose of this study report is to list the real state of the Malaysian economy. In this paper, we present the Sensitive Learning for Economy Prediction Model (SLEPM), a novel learning-based approach to accurately forecasting a country's growth rate. We cross-validate our proposed scheme with the more traditional Artificial Neural Network (ANN) learning-based approach. This research article may be included among the many publications concerning the Malaysian economy, which helps to maintain the study's vitality and progress.

Original languageEnglish
Title of host publicationProceedings of the 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI 2024), Chennai, India, 9-10 May 2024
Place of PublicationU.S.
PublisherIEEE
Number of pages6
ISBN (Electronic)9798350389449
ISBN (Print)9798350389432
DOIs
Publication statusPublished - 2024
EventInternational Conference on Advances in Computing, Communication and Applied Informatics - Chennai, India
Duration: 9 May 202410 May 2024

Conference

ConferenceInternational Conference on Advances in Computing, Communication and Applied Informatics
Abbreviated titleACCAI
Country/TerritoryIndia
CityChennai
Period9/05/2410/05/24

Keywords

  • ANN
  • Artificial Neural Network
  • Deep Learning
  • Economy Prediction
  • Malaysian Economy Growth
  • Sensitive Learning
  • SLEPM
  • System Engineering

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