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Computer Systems Science & Engineering
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DOI: 10.32604/csse.2024.053660

CORRECTION

Correction: Computational Linguistics Based Arabic Poem Classification and Dictarization Model

Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Najm Alotaibi3, Mohamed K. Nour4, Mahmoud Othman5, Gouse Pasha Mohammed1, Mohammed Rizwanullah1 and Mohamed I. Eldesouki6

1Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3Prince Saud AlFaisal Institute for Diplomatic Studies, Riyadh, Saudi Arabia
4Department of Computer Science, College of Computing and Information System, Umm Al-Qura University, Makkah, Saudi Arabia
5Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
6Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
*Corresponding Author: Manar Ahmed Hamza. Email: ma.hamza@psau.edu.sa

Published: 20 May 2024

In the article “Computational Linguistics Based Arabic Poem Classification and Dictarization Model” by Manar Ahmed Hamza, Hala J. Alshahrani, Najm Alotaibi, Mohamed K. Nour, Mahmoud Othman, Gouse Pasha Mohammed, Mohammed Rizwanullah, Mohamed I. Eldesouki (Computer Systems Science and Engineering, 2024, Vol. 48, No. 1, pp. 97–114. DOI: 10.32604/csse.2023.034520), The following references [22] 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 [22]

22. Y. Wang, W. Liao and Y. Chang, “Gated recurrent unit network-based short-term photovoltaic forecasting,” Energies, vol. 11, no. 8, pp. 2163, 2018.

2. Delete content referencing References [22] in the main text:

In recent times, GRU, a family of RNNs, was introduced for handling exploding or vanishing gradient problems. GRU is a modest and robust alternating for LSTM networks [22].

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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