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Electronics
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Artificial Intelligence for Data Analysis
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Artificial Intelligence for Data Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 3749

Special Issue Editor

Prof. Dr. Ping-Feng Pai

E-Mail Website
Guest Editor
Department of Information Management, National Chi Nan University, Nantou 54561, Taiwan
Interests: machine learning; neural networks; deep learning; big data; data mining; metaheuristics and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid progress of information technology and information systems in both hardware and software, artificial intelligence has become a powerful and emerging technique in data analysis. Using artificial intelligence to conduct data analysis has been an effective and efficient trend in many areas. The objective of this Special Issue is to employ the latest artificial intelligence techniques to perform data analysis theoretically or in application aspects. The scope of the Special Issue includes, but is not limit to, using artificial intelligence approaches, such as artificial neural networks, deep learning networks, hybrid intelligent systems of unsupervised learning and supervised learning, evolution algorithms, for data analysis in the following topics:

  1. Operations management;
  2. Manufacturing systems;
  3. Finance management;
  4. Renewable energy;
  5. Hotel management;
  6. Healthcare systems;
  7. Public health;
  8. Hospital management;
  9. Models optimization of artificial intelligence;
  10. Survey papers in related areas.

Prof. Dr. Ping-Feng Pai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • data analysis
  • neural networks
  • machine learning
  • data mining
  • deep learning

Published Papers (1 paper)

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Research

19 pages, 7026 KiB  
Article
Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis
by Ying-Lei Lin, Chi-Ju Lai and Ping-Feng Pai
Electronics 2022, 11(21), 3513; https://doi.org/10.3390/electronics11213513 - 28 Oct 2022
Cited by 12 | Viewed by 3142
Abstract
Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual [...] Read more.
Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries’ texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine learning models have emerged as powerful ways of coping with forecasting problems, and parameter determination greatly influences forecasting models’ performance. This study used Long Short-Term Memory (LSTM) models employing the genetic algorithm (GA) to select parameters for predicting stock market indices and prices of company stocks by hybrid data in non-native English-speaking regions. Numerical results revealed that the developed LSTMGA model with hybrid multilingual sentiment data generates more accurate forecasting than the other machine learning models with various data types. Thus, the proposed LSTMGA model with hybrid multilingual sentiment analysis is a feasible and promising way of forecasting the stock market. Full article
(This article belongs to the Special Issue Artificial Intelligence for Data Analysis)
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