Ghaida A. Al-Suhail

Dr. Ghaida A. Al-Suhail received her B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering in 1984, 1989, and 2007, respectively all at University of Basrah in Iraq. She became an Assistant Professor in 1996. Currently, she is a Full Professor at the Department of Computer Engineering, College of Engineering, University of Basrah in Iraq. Her current research interests include Multimedia Communications, Wireless Networks, Cross-layer Design, Internet of Things, Routing Protocols in Ad hoc and Sensor Networks, Chaotic Radars, and Optical Communications. She was a Fulbright Scholar in 2011 at the Michigan State University (MSU), USA, and Endeavour Fellowship 2009 Scholar at the Australian National University (ANU), RSISE, Australia. She has published several papers in prestigious International Journals and Conferences.

RESEARCH INTERESTS

Multimedia Communications; Wireless Networks; Cross-layer Design; Internet of Things, Routing Protocols in Ad hoc Networks ( MANET, VANET and FANET); Wireless Sensor Networks; Chaotic Radar Systems; and Optical Communications

Scopus Publications

AlzONet: a deep learning optimized framework for multiclass Alzheimer’s disease diagnosis using MRI brain imaging
Hiba A. Alahmed and Ghaida A. Al-Suhail
Springer Science and Business Media LLC



Medical Image Classification Using Deep Learning for Brain Tumors Detection: An Overview
Hiba A. Alahmad and Ghaida A. Al-Suhail
Springer Nature Switzerland



Image-Based Oil Spill Detection using Deep Learning Techniques: A Review
Iman Ali Al-Sudani and Ghaida A. Al-Suhail
IEEE
Oil spills represent a significant danger to marine ecosystems, leading to considerable environmental disasters that adversely affect biodiversity. These incidents, whether accidental or intentional, introduce harmful petroleum hydrocarbons into water bodies, endangering marine life. Various techniques have been developed for oil spill detection, including classification, zone segmentation, threshold segmentation, and edge detection. Recently, deep learning (DL) and machine learning (ML) have shown a lot of potential for successfully detecting and tracking oil spills. This research provides a valuable review of AI-based methodologies for oil spill detection from 2020 to 2024, emphasizing the use of satellite imagery, aerial photographs, and drone imagery for extensive monitoring of marine and coastal areas. The review also discusses the advantages and limitations of these approaches and suggests future directions to improve oil spill detection systems. Ultimately, this article serves as a valuable guideline for researchers providing innovative solutions to mitigate oil spill impacts, aiding in better environmental management and policy development.


Lung Cancer Detection in Radiological Imaging using Deep Learning: A Review
Noor S. Jozi and Ghaida A. Al-Suhail
IEEE
Lung cancer remains a leading cause of cancer-related deaths globally, requiring creative methods to improve detection efficiency. Data quantity and quality, algorithm complexity, integration into healthcare workflows, and ethical and legal issues are some of the challenges facing artificial intelligence (AI). The study aims to provide a comprehensive overview of state-of-the-art deep learning techniques, in particular Convolutional Neural Networks (CNNs), for the diagnosis of lung cancer using medical imaging modalities such as Computed Tomography CT scans and X-rays. Some of the primary objectives are to assess algorithm performance, identify gaps and trends, enhance clinical applications, and propose new research directions. To focus future research efforts on more useful solutions, the paper also discusses the need for a larger range of datasets and improved model interpretability. It also considers how deep learning technology could be incorporated into routine screenings to improve patient outcomes and early detection rates. More stable datasets, improved algorithm performance, and the development of morally sound AI deployment frameworks for use in healthcare environments are some of the next directions to pursue. With increasingly powerful diagnostic instruments, notably Computer-Aided Diagnosis (CAD) systems, scholars and practitioners should acquire valuable insights from this investigation, which will ultimately improve patient treatment.


A Comparative Study of VGG16 and VGG19 for Oil Spill Detection in Coastal Imagery
Iman A. Al-Sudani and Ghaida A. Al-Suhail
IEEE
Oil spills, caused by various human activities, harm the environment and lead to extensive pollution and considerable economic costs, adversely affecting marine life and coastal regions. Therefore, it is essential to enhance oil spill detection strategies in water bodies by utilizing advanced technologies beyond conventional methods. This paper investigates the classification of oil spills in aquatic environment using convolutional neural networks (CNNs) and image data. Three subsets of a 1,267-image dataset were collected from the Kaggle platform: a training set, a validation set, and a testing set. The paper evaluates the performance of two CNN models using transfer learning: VGG16 and VGG19. The findings indicate that the VGG16 model is faster and less complex. Nevertheless, VGG19 reveals lower accuracy in feature extraction, achieving an accuracy of 94.49%, a precision of 96%, a recall of 97%, and an F1 score of 97%, compared to VGG16. For simplicity and speed, VGG16 achieves accuracy, precision, recall, and F1 scores of 96.46%, 98%, 97%, and 98%, respectively, making it more suitable for scenarios with limited resources. Notably, the VGG19 model performs better when resources are available, providing more precise and detailed information.


Exploring Transfer Learning Techniques for Brain Tumor Diagnosis in MRI Data
Hiba A. Alahmed and Ghaida A. Al-Suhail
IEEE
Deep learning has revolutionized computer-aided diagnosis, improving image analysis, segmentation, detection, and classification. By accurately identifying and classifying abnormalities in X-rays, MRI, and CT scans, it can develop personalized treatment plans. However, building deep learning models requires a significant investment of time, money, and large datasets. This paper aims to establish a deep learning framework that can identify brain lesions using transfer learning techniques. Magnetic resonance imaging (MRI) is still effective, but the diagnosis of malignant brain tumors remains challenging due to the complex structure of the brain. Using four architectures VGG16, VGG19, Xception, and DenseNet121 for brain tumor diagnosis, the effectiveness of deep transfer learning architectures was evaluated. With a batch size of 32, the following models are trained for 50 epochs. VGG16 had an accuracy of 97.6% on training data and 94% on validation data, VGG19 had an accuracy of 96.5% on training data and 89.2% on validation data, and Dense121 Net showed an accuracy of 100% on training data and 94% on validation data. In comparison, Xception showed an accuracy of 94.3% on validation data and 100% on the training set. The accuracy on test data for VGG16, VGG19, Xception, and DenseNet121 was 93.4%, 92.1%, 93.4%, and 94.3%, respectively.


AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)
Raneen I. Al-Essa and Ghaida A. Al-Suhail
MDPI AG
In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET.


A Service of RSU Communication in Internet of Vehicles (IoV) in Urban Environment
Raneen I. Al-Essa and Ghaida A. Al-Suhail
Springer International Publishing



EEIT2-F: Energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks




Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform
Mariam H. Abd, Ghaida A. Al-Suhail, Fadhil R. Tahir, Ahmed M. Ali Ali, Hamza A. Abbood, Kia Dashtipour, Sajjad Shaukat Jamal, and Jawad Ahmad
MDPI AG
There is no doubt that chaotic systems are still attractive issues in various radar applications and communication systems. In this paper, we present a new 0.3 GHz mono-static microwave chaotic radar. It includes a chaotic system based on a time-delay to generate and process frequency modulated (FM) waveforms. Such a radar is designed to extract high-resolution information from the targets. To generate a continuous FM signal, the chaotic signal is first modulated using the voltage control oscillator (VCO). Next, the correct value for the loop gain (G) is carefully set when utilizing the Phase-Locked Loop (PLL) at the receiver, so that the instantaneous frequency that reflects a chaotic state variable can be reliably recovered. In this system, the PLL synchronization and radar correlation are enough to recover the echo signal and detect the target. The finding indicates that the system can be implemented with no need to use the complete self-synchronization or complex projective synchronization schemes as compared to the existing chaotic radar systems. The simulation results show that the short-time cross-correlation of the transmitted and reconstructed waveforms is good and satisfactory to detect the target under various signal-to-noise ratio (SNR) levels and with less complexity in the design.


A QoS Evaluation of AODV Topology-Based Routing Protocol in VANETs
Israa A. Aljabry and Ghaida A. Al-Suhail
IEEE
Over recent years, a new technology named VANET (Vehicular Ad-hoc Networks) is highly recommended in a smart cities and especially in Intelligent Transportation Systems (ITS). VANET technology relies on the nodes acting like cars without the necessity for any controller or central base station by creating a wireless link among them. It enables cars to send and receive information between themselves and their environment. There are many network simulators that can support VANET environments such as NS-2, NS-3, OMNeT++, OPNET, and Qualnet. In this paper, we investigate the performance of a common reactive routing protocol; named (AODV) Ad-hoc On-demand Distance Vector routing where two scenarios are considered. The first scenario is a comparison between Vehicle to Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication modes. The second one is made between two maps Basrah city and Manhattan grid in Vehicle to Vehicle (V2V) mode; through the real-time interaction between OMNeT++ and SUMO. The implementation of AODV reveals a comparative results analysis using Quality of Service (QoS) parameters, such as packet delivery ratio, packet drop rate, and network throughput. The simulation results in a helpful guideline for researchers to improve and develop this protocol as compared to other existing works.


An Efficacy of Transmission Power on DYMO Routing Protocol in VANET
Raneen I. Al-Essa and Ghaida A. Al-Suhail
IEEE
A Vehicular Ad hoc Network (VANET) is made up of groups of moving or stationary vehicles that are linked by a wireless network. It arose from the concept of creating an automobiles network to achieve a specific need or situation, such as Intelligent Transport System (ITS) and smart cities. VANET are now recognized as reliable network that vehicles are used for communication on highways and in cities. Basically, when the vehicles communicate with one another directly is known as Vehicle to Vehicle (V2V), or it can communicate with Infrastructure or a Road-side Unit (RSU so it is called Vehicle to Infrastructure (V2I) and Vehicle to Everything (V2X). This paper presents the evaluation of Dynamic MANET On-Demand (DYMO) routing protocol for a vehicular network with a single RSU in order to elaborate the influence of different transmission power values on QoS parameters. The simulation results are obtained using OMNeT++ with INET and Veins frameworks communicate with a SUMO traffic road simulator to implement DYMO. The findings depict a good foundation for researchers in evaluating the efficacy of transmission power of the vehicles on the QoS metrics of DYMO routing protocol in terms of packet delivery ratio, normalized routing load, throughput and packet loss ratio in a VANET environment.


Mobility and Transmission Power of AODV Routing Protocol in MANET
Raneen I. Al-Essa and Ghaida A. Al-Suhail
IEEE
A Mobile Ad hoc Network (MANET) is made up of multiple nodes connected by routing protocols that do not require any infrastructure or centralized access point. Mobility and transmission power are two important parameters that influence the activity of MANET routing protocols. This study investigates the network performance of the reactive protocol AODV under different transmission power conditions in three mobility models of Random waypoint, Mass mobility and Linear mobility. Also, this study evaluates the impact of network size, mobility speed and simulation area on the network performance in terms of different QoS parameters. The simulation results are obtained using OMNeT++ and INET framework for AODV routing protocol. The findings indicate a good and helpful baseline for the researchers in evaluating some of the QoS metrics such as the packet delivery ratio, end to end delay and throughput under various scenarios in MANET environment.


Towards Energy Savings in Cluster-Based Routing for Wireless Sensor Networks
Enaam A. Al-Hussain and Ghaida A. Al-Suhail
Springer International Publishing



A Fuzzy Based Clustering Approach to Prolong the Network Lifetime in Wireless Sensor Networks
Enaam A. Al-Hussain and Ghaida A. Al-Suhail
Springer International Publishing



Improving the Route Selection for Geographic Routing Using Fuzzy-Logic in VANET
Israa A. Aljabry and Ghaida A. Al-Suhail
Springer International Publishing



A Simulation of AODV and GPSR Routing Protocols in VANET Based on Multimetrices
Israa Aljabry and Ghaida Al-Suhail
University of Basrah - College of Engineering
Vehicular Ad hoc Networks (VANETs), a subsection of Mobile Ad hoc Networks (MANETs), have strong future application prospects. Because topology structures are rapidly changing, determining a route that can guarantee a good Quality of Service (QoS) is a critical issue in VANETs. Routing is a critical component that must be addressed in order to utilize effective communication among vehicles. The purpose obtained from this study is to compare the AODV and GPSR performance in terms of Packet Delivery Ratio, Packet Drop Ratio, Throughput, and End-to-End Delay by applying three scenarios, the first scenario focuses on studying these protocols in terms of QoS while changing the number of vehicles at a constant speed of 40Km/h, and for the second scenario changing the speed value while keeping a constant number of vehicles which is 100, the third involves changing the communication range at a constant speed and vehicle number. This study represents a foundation for researchers to help elaborate on the strength and weaknesses of these two protocols. OMNeT++ in conjunction with SUMO is used for simulation.


E-FLEACH: An Improved Fuzzy Based Clustering Protocol for Wireless Sensor Network
Enaam Al-Husain and Ghaida Al-Suhail
University of Basrah - College of Engineering
Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node’s centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.


A Fuzzy GPSR Route Selection Based on Link Quality and Neighbor Node in VANET
Israa A. Aljabry, Ghaida A. Al-Suhail, and Waheb A. Jabbar
IEEE
Over recent years, a new technology named VANET (Vehicular Ad-hoc Networks) is highly recommended in smart cities and especially in Intelligent Transportation Systems (ITS). The VANET technology relies on the nodes acting like cars without the necessity for any controller or central base station by creating a wireless link among them. It enables cars to send and receive information between themselves and their environment. most VANETs utilize position-based routing protocols because they contain a GPS device. To deal with VANET problems, one solution is Geographic Perimeter Stateless Routing (GPSR) which has been broadly implemented. This paper suggests an effective intelligent fuzzy logic control system; called the FL-QN GPSR routing protocol. The proposed routing protocol incorporates two metrics link quality, and neighbor node to detect the best next-hop node for packet forwarding also updates the format of the Hello message by adding the direction field to be more suitable to our simulation. The OMNeT++ and SUMO simulation tools are both used in parallel to examine the VANET environment. The obtained results of the four simulation experiments in urban environments indicate substantial improvements in the network performance compared to the traditional GPSR and AODV concerning the QoS parameters.


A dynamic behavior of hyperglycemia model based on reaction-diffusion cellular nonlinear networks (RD-CNN)
Ahmed M. Ali Ali, Fadhil R. Tahir, Ghaida A. Alsuhail, and Hamzah Abdulkareem Abbood
Elsevier BV



An adaptive fuzzy based FEC algorithm for robust video transmission over wireless networks
Ghaida A. Al-Suhail, Salah Al-Majeed, Sarah H. Subber, and A. Sheikh-Akbari
Deanship of Scientific Research
Forward Error Correction (FEC) is a commonly adopted mechanism to mitigate packet loss/bit error during real-time communication. An adaptive, Fuzzy based FEC algorithm to provide a robust video quality metric for multimedia transmission over wireless networks has been proposed to optimize the redundancy of the generated code words from a Reed-Solomon encoder and to save the bandwidth of the network channel. The scheme is based on probability estimations derived from the data loss rates related to the recovery mechanism at the client end. By applying the adaptive FEC, the server uses the reports to predict the next network loss rate using a curve-fitting technique to generate the optimized number of redundant packets to meet specific residual error rates at the client end. Simulation results in the cellular system show that the video quality is massively adapted to the optimized FEC codes based on the probability of packet loss and packet correlation in a wireless environment.


Alleviation of nonlinear impact using PAPR hybrid technique in CO-OFDM systems
Liqaa A. Al-Hashime, Sinan M. Abdul Satar, Ghaida A. Al-Suhail, and Osama Saied
ASTES Journal



An ICI Reduction Based on PAPR Clipping in Coherent Optical OFDM System
Liqaa AbdulSattar AL-Hashemi, Ghaida A. Al-Suhail, Sinan M. Abdul Satar, Ali N. Kareem, and Mohammed A. Hussein
IEEE
The Inter-Carrier Interference (ICI) compensation for Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) system has been studied in this paper. The purpose behind is to investigate the presence of ICI due to the impact of Laser Phase Noise (LPN) and Fiber Non-Linearity (FNL). Thereby, we propose a simple clipping scheme which represents an effective distortion algorithm to decrease the Peak to Average Power Ratio (PAPR) for 4QAM system. The method exhibits a significant process on ICI cancellation in CO-OFDM system. The OFDM signal is basically transmitted along 550km distance rated at 10Gbps single mode fiber for the coherent optical mode. The new findings show that the receiver sensitivity is highly improved below 10−3 FEC for laser power 5dBm; and archives about 1dBm to 2.4dBm when laser power becomes 8dBm at a typical clipping ratio of 0.6. In particular, the system exhibits a good performance over a 385km transmission distance in comparison to the conventional CO-OFDM. As a result, the proposed clipping shows that the system can enhance its the performance by reducing ICI in the CO-OFDM system; in addition to present a high robustness in BER metric against FNL by a clear reduction in PAPR.


An Effective Combining Transformer to RelieveFiber Nonlinearity in CO-OFDM System
Liqaa A. AL-Hashemi, Sinan M. Abdul Satar, and Ghaida A. Al-Suhail
IEEE
This paper mainly concerns on reducing the impact of chromatic dispersion (CD) and the nonlinear(NL) impairments in coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. In effect, the system suffers from a severe problem due to a high peak to average power ratio (PAPR); and this consequently degrades the system performance. In this paper, a new method is proposed to mitigate the PAPR problem and thereby reduce the impact of nonlinearity on the performance of the CO-OFDM system with quadrature amplitude modulation QAM mapping. The recommended new technique is based on combining two effective ways of clipping method integrated with the L2-by3 method. The simulation results indicate that the proposed method was effectively able to improve system performance by enhancing system performance via bit error rate (BER), quality factor (QF), error vector magnitude (EVM) beside the Optical Signal-to-Noise Ratio (OSNR) without the need to add any digital processing at the receiver; and eventually decrease the receiver complexity. The proposed method reduced the peak to average power ratio (PAPR) about 3.8 dB and helped to increase transmission distance by approximately 200 km.


Hybrid technique based PAPR reduction in CO-OFDM system

RECENT SCHOLAR PUBLICATIONS

AlzONet: a deep learning optimized framework for multiclass Alzheimer’s disease diagnosis using MRI brain imaging
HA Alahmed, GA Al-Suhail
The Journal of Supercomputing 81 (2), 423 2025



Energy-Aware Clustering Using Intelligent Scheme for Heterogeneous Wireless Sensor Networks
EA Al-Hussain, GA Al-Suhail
Karbala International Journal of Modern Science 11 (2), 3 2025



A Comparative Study of VGG16 and VGG19 for Oil Spill Detection in Coastal Imagery
IA Al-Sudani, GA Al-Suhail
2024 1st International Conference on Emerging Technologies for Dependable 2024



Exploring Transfer Learning Techniques for Brain Tumor Diagnosis in MRI Data
HA Alahmed, GA Al-Suhail
2024 1st International Conference on Emerging Technologies for Dependable 2024



Image-Based Oil Spill Detection using Deep Learning Techniques: A Review
IA Al-Sudani, GA Al-Suhail
2024 5th International Conference on Communications, Information, Electronic 2024



Lung Cancer Detection in Radiological Imaging using Deep Learning: A Review
NS Jozi, GA Al-Suhail
2024 5th International Conference on Communications, Information, Electronic 2024



Design and FPGA Implementation of a Hyper-Chaotic System for Real-time Secure Image Transmission
ABA Al-Hussein, FR Tahir, GA Al-Suhail
Iraqi Journal for Electrical and Electronic Engineering 21 (1) 2024



Medical image classification using deep learning for brain tumors detection: An overview
HA Alahmad, GA Al-Suhail
International Conference on Intelligent Computing & Optimization, 194-207 2023



AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)
RI Al-Essa, GA Al-Suhail
Computation 11 (174), 27 2023



A Service of RSU Communication in Internet of Vehicles (IoV) in Urban Environment
RI Al-Essa, GA Al-Suhail
Computational Intelligence, Data Analytics and Applications, ICCIDA 2022 2023



A QoS Evaluation of AODV Topology-Based Routing Protocol in VANETs
IA Aljabry, GA Al-Suhail
2022 International Conference on Engineering & MIS (ICEMIS), 1-6 2022



An Efficacy of Transmission Power on DYMO Routing Protocol in VANET
RI Al-Essa, GA Al-Suhail
2022 International Conference on Engineering & MIS (ICEMIS), 1-6 2022



Mobility and Transmission Power of AODV Routing Protocol in MANET
RI Al-Essa, GA Al-Suhail
2nd International Conference on Computing and Machine Intelligence (ICMI 2022



EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks.
EA Al-Hussain, GA Al-Suhail
International Journal of Electrical & Computer Engineering (2088-8708) 12 (3) 2022



Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform
MH Abd, GA Al-Suhail, FR Tahir, AMA Ali, HA Abbood, K Dashtipour, ...
Remote Sensing 14 (9), 1984 2022



Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform. Remote Sens. 2022, 14, 1984
MH Abd, GA Al-Suhail, FR Tahir, AM Ali Ali, HA Abbood, K Dashtipour, ...
s Note: MDPI stays neutral with regard to jurisdictional claims in published 2022



A Fuzzy Based Clustering Approach to Prolong the Network Lifetime in Wireless Sensor Networks
EA Al-Hussain, GA Al-Suhail
Intelligent Computing & Optimization: Proceedings of the 4th International 2022



Improving the Route Selection for Geographic Routing Using Fuzzy-Logic in VANET
IA Aljabry, GA Al-Suhail
Intelligent Computing & Optimization: Proceedings of the 4th International 2022



Towards energy savings in cluster-based routing for wireless sensor networks
EA Al-Hussain, GA Al-Suhail
Intelligent Computing & Optimization: Proceedings of the 4th International 2022



A Fuzzy Based Clustering Approach to Prolong the Network Lifetime in Wireless
EA Al-Hussain, GA Al-Suhail
Intelligent Computing & Optimization: Proceedings of the 4th International 2021

MOST CITED SCHOLAR PUBLICATIONS

An adaptive observer synchronization using chaotic time-delay system for secure communication
MH Abd, FR Tahir, GA Al-Suhail, VT Pham
Nonlinear Dynamics 90, 2583-2598 2017
Citations: 58



A Survey on Network Simulators for Vehicular Ad-hoc Networks (VANETS)
IA Aljabry, GA Al-Suhail
International Journal of Computer Applications 174, 8887 2021
Citations: 45



Modelling of Long-Wave Chaotic Radar System for Anti-Stealth Applications
GA Al-Suhail, FR Tahir, MH Abd, VT Pham, L Fortuna
Communications in Nonlinear Science and Numerical Simulation (CNSNS), 2018 2017
Citations: 25



A Weighted Voting of K-Nearest Neighbor Algorithm for Diabetes Mellitus
AH Khaleel, GA Al-Suhail, BM Hussan
International Journal of Computer Science and Mobile Computing (IJCSMC) 6 (1 2017
Citations: 23



A Simulation of AODV and GPSR Routing Protocols in VANET Based on Multimetrices
IA Aljabry, GA Al-Suhail
Iraqi Journal for Electrical and Electronic Engineering (IJEEE) 17 (2), 66-72 2021
Citations: 19



Energy efficiency analysis of adaptive error correction in wireless sensor networks
GA Al-Suhail, KW Louis, TY Abdallah
International Journal of Computer Science Issues (IJCSI) 9 (4), 79 2012
Citations: 17



Synchronization of Monostatic Radar Using a Time-Delayed Chaos-Based FM Waveform
MH Abd, GA Al-Suhail, FR Tahir, AMA Ali, HA Abbood, K Dashtipour, ...
Remote Sensing 14 (9), 1984 2022
Citations: 12



Error-resilience of TCP-friendly video transmission over wireless channel
GA Al-Suhail, N Wakamiya, RS Fyath
2006 9th International Conference on Control, Automation, Robotics and 2006
Citations: 12



E-FLEACH: An Improved Fuzzy Based Clustering Protocol for Wireless Sensor Network
EA Al-Husain, GA Al-Suhail
Iraqi Journal for Electrical and Electronic Engineering (IJEEE) 17 (2), 190-197 2021
Citations: 11



AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)
RI Al-Essa, GA Al-Suhail
Computation 11 (174), 27 2023
Citations: 10



Client-server based wireless networked control system
MJ Marie, GA Al-Suhail, S Al-Majeed
2016 IEEE East-West Design & Test Symposium (EWDTS), 1-7 2016
Citations: 10



A QoS Evaluation of AODV Topology-Based Routing Protocol in VANETs
IA Aljabry, GA Al-Suhail
2022 International Conference on Engineering & MIS (ICEMIS), 1-6 2022
Citations: 7



Towards ubiquitous human gestures recognition using wireless networks
MS Aljumaily, GA Al-Suhail
International Journal of Pervasive Computing and Communications 13 (4), 408-418 2017
Citations: 7



Improving the QoS of Wireless Video Transmissions via Packet-Level FEC
GA AL-Suhail, L Tan
International Conference on Signal Processing and Communication Systems 2007
Citations: 7



Mobility and Transmission Power of AODV Routing Protocol in MANET
RI Al-Essa, GA Al-Suhail
2nd International Conference on Computing and Machine Intelligence (ICMI 2022
Citations: 6



A fuzzy GPSR route selection based on link quality and neighbor node in VANET
IA Aljabry, GA Al-Suhail, WA Jabbar
2021 International Conference on Intelligent Technology, System and Service 2021
Citations: 6



An ICI reduction based on PAPR clipping in coherent optical OFDM system
LAS AL-Hashemi, GA Al-Suhail, SMA Satar, AN Kareem, MA Hussein
2018 International conference on advanced science and engineering (ICOASE 2018
Citations: 6



A practical survey on wireless sensor network platforms
GA Al-Suhail, J Mehdi, G Nikolakopoulos
Journal of Communications Technology, Electronics and Computer Science 13, 23-30 2017
Citations: 6



Application Tool based on C4.5 Decision Tree for Diagnosing Diabetes Infection Symptoms
AH Khaleel, G Al-Suhail, BM Hussan
Journal of Communications Technology, Electronics and Computer Science 2019
Citations: 5



Performance Improvement Of OOFDM Systems Based On Advanced Logarithmic Companding Technique
AN Kareem, SMA Satar, MA Husein, LA Al-Hashime, GA Al-Suhail
Transactions on Networks and Communications 6 (5), 121 2018
Citations: 5