Abdelazeem A. Abdelsalam

Work place: Electrical Engineering – computer and control Faculty of Engineering, Suez Canal University, Ismalia, Egypt

E-mail: eng.abdelazeem@gmail.com


Research Interests: Computational Science and Engineering, Computational Engineering, Engineering


Abdelazeem A. Abdelsalam is an Assistant Professor at Suez Canal University, Egypt. He was a post doctorate fellow at University of Ontario Institute of Technology (UOIT), Canada. He received his B.Sc., M.Sc. and Ph. D. degrees in Electrical Engineering from Suez Canal University, Egypt, in 2001, 2005 and 2011, respectively. His current research areas include power quality issues, FACTS technology, distributed energy resources interface and control and application of artificial intelligent techniques on power systems.

Author Articles
Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

By Ahmed S. Mahedy Abdelazeem A. Abdelsalam Reham H. Mohamed Ibrahim F. El-Nahry

DOI: https://doi.org/10.5815/ijitcs.2020.03.01, Pub. Date: 8 Jun. 2020

The neural networks, AI applications, are effective prediction methods. Therefore, in the current research a prediction system was proposed using these neural networks. It studied the technical share indices, viewing price not only as a function of time, but also as a function depending on several indices among which were the opening and closing, top and bottom trading session prices or trading volume. The above technical indices of a number of Egyptian stock market shares during the period from 2007 to 2017, which can be used for training the proposed system, were collected and used as follows: The data were divided into two sets. The first one contained 67% of the total data and was used for training neural networks and the second contained 33% and was used for testing the proposed system. The training set was segmented into subsets used for training a number of neural networks. The output of such networks was used for training another network hierarchically. The system was, then, tested using the rest of the data.

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