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Ali, Adnan Hussein
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Electrical & Electronics Engineering

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Performance evaluation of software defined networking into vanets system Taher, Younus Hasan; Alsaadi, Israa; Saad, Mohammed Ayad; Ali, Adnan Hussein; Essa, Mohammed; Rashid, Ahmed Hashim
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.4675

Abstract

Vehicular ad hoc networks (VANETs) is an important topic nowadays. A lot of research deal and attracts consideration owing to potential for increasing traffic and travel efficiency, improving road safety for vehicles, providing convenience and comfort to both drivers and passengers. The need for a packet delivery ratio (PDR) and low delivery delay time in communication are the key elements in modern life especially when traveling in vehicles. To satisfy these demands; researchs in VANET systems aims to develop some new technologies. One of these technologies is using software-defined- network (SDN) to enhance communication between vehicles on the road. Because of this, project evaluates using SDN protocol with two most viable VANET protocols which are ad hoc on demand distance vector (AODV) and optimized link state routing (OLSR) in LTE communication. Two performance metrics are used to evaluate the performances, the PDR and the delivery delay time. The simulation is performed in the varying density network and varying speed vehicles. The simulation results show that SDN displays better performance than AODV and OLSR in both PDR and delivery delay time. SDN uses global views of SDN controller to determine the shortest route with the highest vehicle density. Additionally, it solves the local maximum issue and adds dense connectivity.
Based on deep convolutional neural network, COVID-19 identification utilizing computed tomography scans Yonan, Janan Farag; Fadheel, Fadil Raafat; Al-Doori, Mohammed A. J. Hammeid; Ali, Adnan Hussein
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5124

Abstract

In the year 2019 specifically, on March 11th, the coronavirus illness two thousand nineteen (COVID-19) was announced a worldwide epidemic due to its rapid spread and lack of treatment options. As a result, infected individuals must be identified and quarantined quickly to prevent the illness from spreading. The method used to test for COVID-19 is called real-time-polymerase chain reaction (RT-PCR), which has problems with having low sensitivity and taking an extended amount of time. Because chest computed tomography (CT) scans are more sensitive than RT-PCR, it follows that such scans can be employed for diagnostic purposes. This study developed a deep convolutional neural network (CNN) approach to detect COVID-19 using CT scan images. An architecture of deep learning (DL) called convolutional neural network computed tomography scans (CT-CNN) was utilized to efficiently identify COVID-19. The findings of our suggested model are highly encouraging, with an accuracy of 96.14%, an F1 score of 96.21%, and a recall of 97.53% when it comes to classifying CT scans as either infected or not infected by COVID-19.
Co-Authors Al-Doori, Mohammed A. J. Hammeid Alsaadi, Israa Essa, Mohammed Fadheel, Fadil Raafat Rashid, Ahmed Hashim Saad, Mohammed Ayad Taher, Younus Hasan Yonan, Janan Farag