Skip to Content
MathematicsMathematics
  • Correction
  • Open Access

6 May 2023

Correction: Ali et al. A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem. Mathematics 2022, 10, 2675

,
and
1
Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
2
Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
3
Computer Science Department, Faculty of Computer Science, Misr International University, Cairo 12585, Egypt
4
Faculty of Information Technology, Middle East University, Amman 11831, Jordan
Additional Affiliation(s)
In the original publication [1], there was an error regarding the affiliation(s) for **Diaa Salama Abd Elminaam**. In addition to affiliation(s) **3,4**, the updated affiliation(s) should include: *2,3,4*
2 
Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
3 
Computer Science Department, Faculty of Computer Science, Misr International University, Cairo 12585, Egypt
4 
Faculty of Information Technology, Middle East University, Amman 11831, Jordan
And the author also wants to change the email information to: diaa.salama@fci.bu.edu.eg or diaa.salama@miuegypt.edu.eg or ds_desert@yahoo.com.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Ali, M.A.S.; P. P., F.R.; Salama Abd Elminaam, D. A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem. Mathematics 2022, 10, 2675. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.