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DOI: 10.32604/csse.2024.053659

CORRECTION

Correction: Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

Seoyun Kim¹,#, Hyerim Yu²,#, Jeewoo Yoon¹,³ and Eunil Park¹,²,*

1 Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, 03063, Korea
2 Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, 03063, Korea
3 AI Team, Raon Data, Seoul, 03073, Korea
Corresponding Author: Eunil Park. Email: eunilpark@skku.edu
#These two authors contributed equally to this work
Published: 20 May 2024

In the article “Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models” by Seoyun Kim, Hyerim Yu, Jeewoo Yoon, Eunil Park (Computer Systems Science and Engineering, 2024, Vol. 48, No. 2, pp. 413–429. DOI: 10.32604/csse.2023.041575), The following references [36] is irrelevant to the topic.

The authors wish to apologize for any inconvenience caused due to the fact that the cited reference is irrelevant to the topic. Please check the following updates,

Original Content/Reference:

1. Delete reference [36]

36. S. Hwang, H. Ahn and E. Park, “iMovieRec: A hybrid movie recommendation method based on a user-image-item model,” International Journal of Machine Learning and Cybernetics, vol. 14, pp. 1–12, 2023.

2. Delete content referencing Reference [36] in the main text:

RMSE (formula (6)) is the square root of mean squared error (MSE) which mitigates the distortion resulting from MSE [36]. MAE (formula (7)) is the mean of the absolute variances between the observed and estimated values [37].

Based on the employed metrics and the findings of prior research [38–42], the RMSE and Pearson correlation are mainly considered our main metrics. Table 6 and Fig. 6 summarize the results.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Computer Systems Science and Engineering Editorial Office. The original publication has also been updated.

[picture] | Copyright © 2024 The Authors. Published by Tech Science Press. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.