Abstract:
In the field of economic, social and environmental data analysis, machine learning technologies have been more and more used. The analysis target include stock process, GDP, exchange rate, Gini index, TFR (total fertility rate), working population, and profit of company. However, in some conferences concerning economics, there is not enough discussions on the data analysis technologies. Especially concerning the latest machine learning analysis methods, there is quite few comments because the audience have not enough knowledge on the machine learning technologies. The organizer will propose the special session so that we can discuss the data analysis method and the used data. We will exchange advices and comments using concrete library names such as sklearn and the source data such as World Bank. The special session will contribute a lot to economic data analysts, because they will be able to know other researcher methodologies. Therefore, the papers are limited to be ones that use machine learning methods including PCA(Principal Component Analysis) and SVD(Singular Value Decomposition) other than DNN and tree-based models and others. The target data must be no image data, because the image analysis is out of this theme. The main target is economics data analysis. However other non-image data analysis is welcome.
Topics of Interests include but are not limited to:
(1) Classification or regression of economics/social/environmental data
(2) Social media data analysis by machine learning methods
(3) Financial prediction/analysis by machine learning methods
(4) Sustainable Development Goals related data analysis by machine learning methods
(5) Visualization of those data analyses
Venue:
AIINA 7th, & 8th. Fl., Morioka, Iwate, Japan. http://www.aiina.jp/en/ (4mins. Walk from Morioka station, 8mins drive from Tohoku Expressway)
Paper Submission Procedure:
Electronic version of full paper (IEEE format, A4 six-page standard including figures and tables) complete with authors' affiliations should be submitted through official site. All accepted papers will be sent to IEEE Xplore Digital Library. Selected papers will be recommended for post-conference publication in some Science Citation Index Expanded, EI, or Scopus journals.
Important Dates: