Open Access
CORRECTION
Correction: Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction
1
Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka,
72388, Saudi Arabia
2
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
3
Department of Management Information Systems, Prince Sattam Bin Abdulaziz University, Al Kharaj, Riyadh, Saudi Arabia
4 Information Systems Department, Faculty of Management, Comenius University in Bratislava, Odbojárov, Bratislava, 440,
Slovakia
5 Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk, 71491, Saudi
Arabia
6 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
7 Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia
*
Corresponding Authors: Chitapong Wechtaisong. Email: chitapong@g.sut.ac.th; Natalia Kryvinska. Email:
natalia.kryvinska@uniba.sk
Computer Systems Science and Engineering 2024, 48(3), 855-856. https://doi.org/10.32604/csse.2024.052412
Issue published 20 May 2024
This article is a correction of:
Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction
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Abstract
This article has no abstract.In the article “Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction” by Ahmad Almadhor, Chitapong Wechtaisong, Usman Tariq, Natalia Kryvinska, Abdullah Al Hejaili, Uzma Ghulam Mohammad and Mohana Alanazi (Computer Systems Science and Engineering, 2024, Vol. 48, No. 1, pp. 199–216. DOI: 10.32604/csse.2023.039546). We include some of reference to refer to claims in the articles.
We referred to the following references that the journal thought were irrelevant to the paper:
35. A. Singh, K. Raj, T. Kumar, S. Verma and A. M. Roy, “Deep learning-based cost effective and responsive robot for autism treatment,” Drones, vol. 7, no. 2, pp. 81, 2023.
36. A. Singh, R. Ranjbarzadeh, K. Raj, T. Kumar and A. M. Roy, “Understanding EEG signals for subjectwise definition of Armoni activities,” arXiv preprint arXiv:2301.00948, 2023.
52. E. K. Ampomah, Z. Qin and G. Nyame, “Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement,” Information, vol. 11, no. 6, pp. 332, 2020.
It can be noted that these references are referred in sentences where we need to support our claims such as references 35 is referred at “The author utilized deep learning [35,36] and a time-series-based technique to predict temperature preferences by framing the challenge as a multivariate, multi-class classification problem” which clearly tells about the deep learning usage. Beside this we understand that the latter part of the of both references are not focusing on the healthcare problem and similarly for other reference. Hence, we request you to delete these refences.
The authors wish to apologize for any inconvenience caused due to the fact that these references cover on part of the focused problem. Please check the following updates:
1. Delete References [35], [36], [52]:
35. A. Singh, K. Raj, T. Kumar, S. Verma and A. M. Roy, “Deep learning-based cost effective and responsive robot for autism treatment,” Drones, vol. 7, no. 2, pp. 81, 2023.
36. A. Singh, R. Ranjbarzadeh, K. Raj, T. Kumar and A. M. Roy, “Understanding EEG signals for subject- wise definition of Armoni activities,” arXiv preprint arXiv:2301.00948, 2023.
52. E. K. Ampomah, Z. Qin and G. Nyame, “Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement,” Information, vol. 11, no. 6, pp. 332, 2020.
2. Delete content referencing Reference [35], [36], [52] in the main text:
“The author utilized deep learning [35,36] and a time-series-based technique to predict temperature preferences by framing the challenge as a multivariate, multi-class classification problem.”
“The training data are used instead of bootstrap duplicates to reduce bias. One of this algorithm’s key advantages is its computational effectiveness [52].”
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.
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