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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mahmood Zaki Abdullah
Mustansiriyah University
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Web and IoT-based hospital location determination with criteria weight analysis Abeer Hadi; Mahmood Zaki Abdullah
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3214

Abstract

The hospital location selection for COVID-19-infected patients is out to be one of the most critical decisions for healthcare sectors in high-case countries. In this study, optimal urban hospital location selection for COVID-19-infected patients has been done out of multiple alternative locations in city of Baghdad Iraq by introducing a web application system that can find the best site from alternatives by using MEREC and modified technique for order of preference by similarity to ideal solution (TOPSIS) algorithms. MEREC algorithm is utilized to obtain criteria weights and modified TOPSIS for ranking the alternatives. Four criteria are considered with eight alternatives sites. The proposed system has two-part, hardware part (embedded systems) designed by utilizing NEO-6M GPS receiver with ESP8266NodeMCU to obtain coordinate of regions and then, using the HTTP protocol to communicate to submit these data to database server. The second part is the web application developed by PHP, JavaScript, CSS, HTML, and MySQL used to allow the system admin to enter the locations of the alternatives with their criteria into the system to get the best urban hospital location for COVID-19-patients. The results showed effectiveness of overall suggested system and appropriateness of the modified TOPSIS method over the traditional TOPSIS method in ranking the alternative.
Distributed denial of service attacks detection for software defined networks based on evolutionary decision tree model Hasan Kamel; Mahmood Zaki Abdullah
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3835

Abstract

The software defined networks (SDN) system has modern techniques in networking, it separates the forwarding plane from the control plane and works to collect control functions in a central unit (controller), and this separation process leads to many advantages, such as cost reduction and programming ability. Concurrently, because of its centralized architecture, it is prone to a variety of attacks. Distributed denial of service (DDoS) attack has a significant impact on SDN, it is characterized by its ability to consume network resources as well as its ability to turn off the entire network. The work in this study aims to improve and increase the security and robustness of SDN systems against the attack or intrusion, by using a machine learning model to detect attack traffic and classify traffic of SDN as (attack or normal), and optimization algorithm (genetic algorithm) for improving the accuracy of the classification. After preparing and preprocessing the dataset, we used the genetic algorithm (GA) to optimize the hyperparameters of the decision tree (DT) model, and the proposed evolutionary decision tree (EDT) model was used to classify traffic into normal and attack traffic. The results indicate that the suggested model achieved a high classification accuracy of 99.46.
Co-Authors Abeer Hadi Hasan Kamel Mohammed Ali Tawfeeq Samir Ghaly