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All Journal International Journal of Electrical and Computer Engineering
Walaa Hassan
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Bivariate modified hotelling’s T2 charts using bootstrap data Firas Haddad; Mutasem K. Alsmadi; Usama Badawi; Tamer Farag; Raed Alkhasawneh; Ibrahim Almarashdeh; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.277 KB) | DOI: 10.11591/ijece.v9i6.pp4721-4727

Abstract

The conventional Hotelling’s  charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s  charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively.  The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.
A genetic algorithm for shortest path with real constraints in computer networks Fahad. A. Alghamdi; Ahmed Younes Hamed; Abdullah M. Alghamdi; Abderrazak Ben Salah; Tamer Hashem Farag; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp435-442

Abstract

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints.
Co-Authors Abderrazak Ben Salah Abdullah M. Alghamdi Ahmed Younes Hamed Fahad. A. Alghamdi Firas Haddad Ibrahim Almarashdeh Mutasem K. Alsmadi Raed Alkhasawneh Tamer Farag Tamer Hashem Farag Usama Badawi