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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Salama A. Mostafa
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Analyzing bit error rate of relay sensors selection in wireless cooperative communication systems Ahmed Allawy Alawady; Ahmed Alkhayyat; Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Salama A. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2492

Abstract

Cooperative communication systems, which make use of the intermediate relays between the transmitter and the receiver, have been employed as an effective technique to combat the channel fading and to enhance system performance. Cooperative systems have some drawbacks such as high latency and may diversity order not guaranteed. To alleviate the negative effects of these factors, the relay selection protocol is employed in cooperative communication systems to increase overall cooperative system performance. Relay selection in the cooperative systems enables the source to cooperate with the single relay node rather than multiple relay nodes which guaranteed the diversity order.
An accurate Alzheimer's disease detection using a developed convolutional neural network model Muhanad Tahrir Younis; Younus Tahreer Younus; Jamal Naser Hasoon; Ali Hussain Fadhil; Salama A. Mostafa
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.v11i4.3659

Abstract

Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households. Early, effective, and accurate detection represents an important blueprint for minimizing Alzheimer's progression risk. The modalities of brain imaging can assist in identifying the abnormalities associated with Alzheimer's disease. This research presents a developed deep learning scheme, which is designed and implemented to classify the brain images into multiclass, namely very mild, moderate, mild, and non-demented. The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in the visual geometric group (VGG16) model and the other related learning models. Consequently, this developed model can assist medical personnel by providing a facilitating tool to identify Alzheimer's disease stage and establishing a suitable medical treatment platform.
A smart water grid network for water supply management systems Ali Adil Ali; Saadi Mohammed Saadi; Tameem Mohammed Mahmood; Salama A. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3227

Abstract

This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.
Review of local binary pattern operators in image feature extraction Shihab Hamad Khaleefah; Salama A. Mostafa; Aida Mustapha; Mohammad Faidzul Nasrudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp23-31

Abstract

With the substantial expansion of image information, image processing and computer vision have significant roles in several applications, including image classification, image segmentation, pattern recognition, and image retrieval. An important feature that has been applied in many image applications is texture. Texture is the characteristic of a set of pixels that form an image. Therefore, analyzing texture has a significant impact on segmenting an image or detecting important portions of an image. This paper provides a review on LBP and its modifications. The aim of this review is to show the current trends for using, modifying and adapting LBP in the domain of image processing.
Competitive analysis of single and multi-path routing protocols in mobile Ad-Hoc network Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Salama A. Mostafa; Hairulnizam Mahdin; Aida Mustapha; Luqman Hanif Audah; Farooq Sijal Shaqwi; Ali Hashim Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp293-300

Abstract

A Mobile Ad-hoc Network (MANET) refers to a dynamic and wireless network, which can be designed without an existing infrastructure as every node serves as a router. A MANET is a self-configuring system of mobile nodes that are connected wirelessly. Every node serves as a sink, as well as a router to send packets. The movement of the nodes is not restricted as they can move in any direction, and they have the ability to get organized into a network. Due to their free and independent movement, they do not have a fixed position; they often change positions. In this study, the Dynamic Source Routing (DSR) and Ad-hoc On Multipath Demand Distance Vector (AOMDV) protocols are compared using Network Simulator NS2.35. DSR is a reactive gateway discovery algorithm whereby the connection of a MANET mobile device is established only on demand. Basically, AOMDV was specially tailored for ad-hoc networks that are highly dynamic to respond to link failures and breakages in the network. It ensures that the paths for destinations are sustained, and it defines the new routing information using destination serial numbers to ensure loop freedom always while avoiding problems. More so, it is a protocol that is based on a timer that can discover ways through which the mobile nodes respond to link breakages and change in topology. A comparison of protocols has been carried out individually and jointly with the aim of evaluating their performance. The performance is measured in terms of End-to-End Delay, Packet Delivery Ratio, Packet Loss Ratio, and Routing Overhead Ratio. The performance of the routing protocols was done using two scenarios; when there is a change in the simulation time and when there is a change in the number of nodes.
A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems Shihab Hamad Khaleefah; Salama A. Mostafa; Aida Mustapha; Noor Azah Samsudin; Mohammad Faidzul Nasrudin; Abdullah Baz
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1379-1387

Abstract

With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including local binary pattern (LBP) and gabor filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.
A collaborated genetic with lion optimization algorithms for improving the quality of forwarding in a vehicular ad-hoc network Sami Abduljabbar Rashid; Mustafa Maad Hamdi; Lukman Audah; Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Mohammed Salah Abood; Salama A. Mostafa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp667-677

Abstract

Vehicular ad-hoc network (VANET) is dynamic and it works on various noteworthy applications in intelligent transportation systems (ITS). In general, routing overhead is more in the VANETs due to their properties. Hence, need to handle this issue to improve the performance of the VANETs. Also due to its dynamic nature collision occurs. Up till now, we have had immense complexity in developing the multi-constrained network with high quality of forwarding (QoF). To solve the difficulties especially to control the congestion this paper introduces an enhanced genetic algorithmbased lion optimization for QoF-based routing protocol (EGA-LOQRP) in the VANET network. Lion optimization routing protocol (LORP) is an optimization-based routing protocol that can able to control the network with a huge number of vehicles. An enhanced genetic algorithm (EGA) is employed here to find the best possible path for data transmission which leads to meeting the QoF. This will result in low packet loss, delay, and energy consumption of the network. The exhaustive simulation tests demonstrate that the EGA-LOQRP routing protocol improves performance effectively in the face of congestion and QoS assaults compared to the previous routing protocols like Ad hoc on-demand distance vector (AODV), ant colony optimization-AODV (ACO-AODV) and traffic aware segmentAODV (TAS-AODV).
Co-Authors Abdullah Baz Ahmed Alkhayyat Ahmed Allawy Alawady Aida Mustapha Ali Adil Ali Ali Hashim Abbas Ali Hussain Fadhil Farooq Sijal Shaqwi Hairulnizam Mahdin Jamal Naser Hasoon Lukman Audah Luqman Hanif Audah Mohammad Faidzul Nasrudin Mohammed Ahmed Jubair Mohammed Ahmed Jubair Mohammed Salah Abood Muhanad Tahrir Younis Mustafa Hamid Hassan Mustafa Hamid Hassan Mustafa Maad Hamdi Noor Azah Samsudin Saadi Mohammed Saadi Sami Abduljabbar Rashid Shihab Hamad Khaleefah Tameem Mohammed Mahmood Younus Tahreer Younus