Mona Nagy ElBedwehy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mona Nagy ElBedwehy has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Mohamed Elsayed Ghoneim
Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, Egypt
2 shared publications Aboul Ella Hassanien
IT department, Faculty of Computers and Information, Cairo University Scientific Research Group in Egypt (SRGE)
1 shared publication- Aboul Ella Hassanien
Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
1 shared publication - Ahmad Taher Azar
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
1 shared publication G. M. Behery
Mathematics Department, Faculty of Science, Damietta University, Damietta 34511, Egypt
1 shared publication- Gamal Mohamed Behery
Computer Science Department, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, Egypt
1 shared publication
Latest Publications
- 5 May 2020
- Published by Springer Nature in Arabian Journal for Science and Engineering
- Vol. 45 (12) , 9925-9937
Face Recognition Based on Relative Gradient Magnitude Strength
- 5 May 2020
- Published by Springer Nature in Arabian Journal for Science and Engineering
- Vol. 45 (12) , 9925-9937
Face Recognition Based on Relative Gradient Magnitude Strength
- 19 February 2020
- Published by World Scientific Pub Co Pte Ltd in International Journal of Pattern Recognition and Artificial Intelligence
- Vol. 34 (11)
Emotional Speech Recognition Based on Weighted Distance Optimization System
- 19 February 2020
- Published by World Scientific Pub Co Pte Ltd in International Journal of Pattern Recognition and Artificial Intelligence
- Vol. 34 (11)
Emotional Speech Recognition Based on Weighted Distance Optimization System
- Open Access
- 21 May 2014
- Published by Springer Nature in Neural Computing and Applications
- Vol. 25 (7-8) , 1517-1534
A computational knowledge representation model for cognitive computers
- 21 May 2014
- Published by Springer Nature in Neural Computing and Applications
- Vol. 25 (7-8) , 1517-1534
Open AccessA computational knowledge representation model for cognitive computers
- 1 November 2013
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 9-14
Computational model for artificial learning using fonnal concept analysis
The field of artificial intelligence embraces two approaches to artificial learning. The first is motivated by the study of mental processes and states that artificial learning is the study of mechanisms embodied in the human mind. It aims to understand how these mechanisms can be translated into computer programs. The second approach initiated from a practical computing standpoint and has less grandiose aims. It involves developing programs that learn from past data, and may be considered as a branch of data processing. In this paper, we are concerned with the first approach. Artificial learning is interested in the classification learning that is a learning algorithm for categorizing unseen examples into predefined classes based on a set of training examples. We formulated a computational model for binary classification process using formal concept analysis. The classification rules are derived and applied successfully for different study cases.
- 1 November 2013
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 9-14
Computational model for artificial learning using fonnal concept analysis
The field of artificial intelligence embraces two approaches to artificial learning. The first is motivated by the study of mental processes and states that artificial learning is the study of mechanisms embodied in the human mind. It aims to understand how these mechanisms can be translated into computer programs. The second approach initiated from a practical computing standpoint and has less grandiose aims. It involves developing programs that learn from past data, and may be considered as a branch of data processing. In this paper, we are concerned with the first approach. Artificial learning is interested in the classification learning that is a learning algorithm for categorizing unseen examples into predefined classes based on a set of training examples. We formulated a computational model for binary classification process using formal concept analysis. The classification rules are derived and applied successfully for different study cases.