Correction: Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

In the article “Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer” by K. S. Bhuvaneshwari, Ahmed Najat Ahmed, Mehedi Masud, Samah H. Alajmani, Mohamed Abouhawwash (Computer Systems Science and Engineering, 2023, Vol. 46, No. 3, pp. 2933–2945. DOI: 10.32604/csse.2023.034288), The following References [26–32] are irrelevant to the topic.

The authors wish to apologize for any inconvenience caused due to the fact that these references are irrelevant to the topic. Please check the following updates:

Original Content/Reference:

1. Delete References [26–32]

26. S. Mirjalili, S. M. Mirjalili and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, no. 5, pp. 46–61, 2014.

27. P. Silva, E. Luz, G. Silva, G. Moreira, R. Silva et al., “COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analyzes,” Informatics in Medicine Unlocked, vol. 20, no. 3, pp. 100427, 2020.

28. M. Abdel-Basset, R. Mohamed, M. Elhoseny, M. Abouhawwash, Y. Nam et al., “Efficient MCDM model for evaluating the performance of commercial banks: A case study,” Computers, Materials & Continua, vol. 67, no. 3, pp. 2729–2746, 2021.

29. B. Gomathi, S. Balaji, V. K. Kumar, M. Abouhawwash, S. Aljahdali et al., “Multi-objective optimization of energy aware virtual machine placement in the cloud data centre,” Intelligent Automation & Soft Computing, vol. 33, no. 3, pp. 1771–1785, 2022.

30. M. Kumar, K. Venkatachalam, M. Masud and M. Abouhawwash, “Novel dynamic scaling algorithm for energy-efficient cloud computing,” Intelligent Automation & Soft Computing, vol. 33, no. 3, pp. 1547–1559, 2022.

31. R. S. Ram, K. Venkatachalam, M. Masud and M. Abouhawwash, “Air pollution prediction using dual graph convolution LSTM technique,” Intelligent Automation & Soft Computing, vol. 33, no. 3, pp. 1639–1652, 2022.

32. A. J. Basha, N. Rajkumar, M. A. AlZain, M. Masud and M. Abouhawwash, “Fog-based self-sovereign identity with RSA in securing IoMT data,” Intelligent Automation & Soft Computing, vol. 34, no. 3, pp. 1693–1706, 2022.

2. Keep the paragraph where References [26–32] are cited and remove the citation:

“The dataset of COVID-CT consists of 812 images of CT scans, out of which 349 are Covid-infected patients’ CT images, and 463 are studied non-Covid patient images. Sample CT images from the above datasets are stated in Figs. 1 and 2, respectively.”

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.