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Marwa T. Naser
University of Baghdad
Computer Science & IT Electrical & Electronics Engineering

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Simulation based comparison of routing protocols in wireless multihop adhoc networks Ali H. Wheeb; Marwa T. Naser
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3186-3192

Abstract

Routing protocols are responsible for providing reliable communication between the source and destination nodes. The performance of these protocols in the ad hoc network family is influenced by several factors such as mobility model, traffic load, transmission range, and the number of mobile nodes which represents a great issue. Several simulation studies have explored routing protocol with performance parameters, but few relate to various protocols concerning routing and Quality of Service (QoS) metrics. This paper presents a simulation-based comparison of proactive, reactive, and multipath routing protocols in mobile ad hoc networks (MANETs). Specifically, the performance of AODV, DSDV, and AOMDV protocols are evaluated and analyzed in the presence of varying the number of mobile nodes, pause time, and traffic connection numbers. Moreover, Routing and QoS performance metrics such as normalized routing load, routing packet, packet delivery ratio, packet drop, end-to-end delay, and throughput are measured to conduct a performance comparison between three routing protocols. Simulation results indicate that AODV outperforms the DSDV and AOMDV protocols in most of the metrics. AOMDV is better than DSDV in terms of end-to-end delay. DSDV provides lower throughput performance results. Network topology parameters have a slight impact on AODV Performance.
Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network Dina A. AbdulQader; Asma T. Saadoon; Marwa T. Naser; Ali Hassan Jabbar
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.pp1078-1085

Abstract

Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy.
Co-Authors Ali H. Wheeb Ali Hassan Jabbar Asma T. Saadoon Dina A. AbdulQader