Dr. Marwan Kadhim Mohammed AL-shammari

@uob.edu.iq

Computer Engineering
Northeastern University, China

Dr. Marwan Kadhim Mohammed AL-shammari


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https://researchid.co/malishan
Marwan Kadhim Mohammed was born in Iraq in 1977, PhD, in Northeastern University. He received the B.S. in Computer Engineering from Baghdad University, Iraq in 2000, the M.S. in Computer Engineering from UTeM University, Malaysia, in 2014, CISCO American institute instructor from 2007. He joined Baghdad University in 2006 as a lecturer of Department of computer engineering. He was director for research & developing division and training & continues learning division respectively. He was Core member in advisory office of Baghdad University. He has been a team leader at South Korean and Canada with Coicka and ED companies respectively. He is team leader for many projects in the field of Java, , visuals, IOS, OS, CG, Media, Networking, DB, Embedded Systems, Embedded Software, VR, EEG, Robotic surgery, Networking. He was external lecturer for postgraduate student in Northeastern University. 5 papers published, chapter in Springer book series 2018.

RESEARCH INTERESTS

UAV, Robotics, VR, EEG, cybersecurity, Embedded system, Embedded software, Telemedicine, and robotic surgery, deep-learning, neural network, SWARM, 5G wireless mesh network.
11

Scopus Publications

40

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Efficiency-Oriented Mutual Authentication Protocol for MCC Using Elliptic Curve Cryptography
    Marwan Kadhim Mohammed AL-shammari, Suaad Ali Abead, Halah Hasan Mahmoud
    Journal of Internet Services and Information Security, 2026
    The blistering growth of the Mobile Cloud Computing (MCC) has enabled the smooth usage of omnipresent services, but it poses a great security issue because of the intrinsic resource limitations of mobile devices. Conventional authentication systems based on either large key sizes or heavy modular exponentiation are expensive in terms of computation and energy usage for a mobile device. In this paper, a lightweight mutual authentication protocol that can be applied in a mobile cloud environment is proposed, based on Elliptic Curve Cryptography (ECC) and 1-way hash functions that can be used to provide high levels of security at minimum overhead. In order to determine the effectiveness of the proposed scheme, an extensive statistical and performance study was done. Comparative results indicate that the protocol reduces computational latency by approximately 35-40% compared to standard RSA-based frameworks. In particular, processing time on the mobile client side will be kept at less than 15ms, keeping battery life intact. From a communication perspective, the protocol minimizes the exchange to three messages, reducing total bit-overhead by 28%. Performance evaluations using the BETH and Multi-Cloud Kaggle datasets show that the proposed protocol decreases computational delay by more than 90% compared to traditional RSA-2048 frameworks, while achieving a 98.5% throughput success rate even under high traffic conditions. The protocol is also checked through formal security verification with BAN Logic and AVISPA simulation tools and found to be resistant to common attack vectors, such as Man-in-the-Middle (MitM), replay attacks, and impersonation. More statistical testing additionally shows that the keys created by the generated session have high levels of entropy and guarantee Perfect Forward Secrecy (PFS). The results indicate that the suggested protocol offers the best tradeoff ratio between the security level and operation efficiency and is, therefore, very appropriate in real-time mobile cloud applications in which the latency and the life of the device matter.
  • Enhancing Cognitive Performance of College Students Based on Adam Optimization Assisted by Brain Biofeedback
    Elaf Ayed Jebur
    Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2025
    College students often face obstacles in achieving optimal cognitive performance due to the demands of academic stress during workload and the side effects of cutting-edge media that destroy the students’ attention and cognitive skills. This study aimed to investigate the effectiveness of the adaptive moment estimation (Adam), an optimization algorithm designed for training deep learning (DL) models, assisted with brain biofeedback to enhance students’ brain synapse transportation by boosting the cognitive performance of college students. The contribution of the study is to improve the academic cognitive skills of college students by processing input data (text, and videos). The study used an electroencephalogram (EEG) headset to read attention signals as brain biofeedback combined with the Adam optimization algorithm that trained on the student attention dataset to improve cognitive performance parameters. Adam’s optimization algorithm represents the suggested deep learning model. Results improved the attention accuracy (97.51%), mental fatigue and drowsiness (3%), and precision at predicting learning performance improved by (20%) for college students trained on the Adam optimization algorithm-assisted brain biofeedback system compared with state-of-the-art models concerning the same attention dataset. The study provides evidence that combining Adam and brain biofeedback effectively boosts college students’ performance, cognitive skills and synapse transportation system.
  • A Novel Hybrid CNN-PCA Model to Improve Security in Chaotic IoT Environments
    Marwan Kadhim Mohammed AL-Shammari
    Journal of Internet Services and Information Security, 2025
    Chaotic Internet of Things (IoT) has grown quickly in the digital world. With the emergence of IoT, many security obstacles have been appearing. The study suggests an efficient intrusion detection and protection strategy utilizing hybrid artificial intelligence (AI) methods to protect IoT networks from botnet/DDoS and IP/DNS spoofing attacks. In this study, the convolutional neural network (CNN) has been proposed to detect and prevent large-scale attacks as a first shield to protect network traffic from Distributed Denial of Services (DDoS) attacks, while principal component analysis (PCA) has been proposed to detect and protect the network from malicious attacks that come from IP/DNS spoofing attacks. The results exposed 98.36% detection to protect against intrusion in doorbell IoT devices, 98.62% detection to protect against intrusion in thermostat IoT devices, and 98.81% detection to protect against intrusion in security camera IoT devices, concerning accuracy benchmarks. The CNN-PCA hybrid model was efficacious in detecting malicious and botnet attacks for numerous IoT devices with optimal security. The study compares accuracy, precision, recall, and F1-core metrics with state-of-the-art security models.
  • Improve Industrial Automation Through the Fusion of Cognitive Manufacturing and Echo State Wireless Sensor Network
    Ali Saifaldeen Alkhafaji, Ibtihaal M. Hameed, Marwan Kadhim Mohammed AL-Shammari
    Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 2025
    Cognitive manufacturing faces major challenges due to the increasing industrial competition. Cognitive manufacturing has been directed toward agile strategies that harness artificial intelligence, machine learning, and computer vision approaches to overcome the obstacles of four-generation manufacturing. The integration of these strategies represents an important research direction in the engineering research society. This paper develops a new Automated Defect Detection System (ADDS) utilizing the Echo State Wireless Sensor Network (ES-WSN) as a random shallow technique to detect defects in products through image analysis and training. The system employs artificial intelligence and real-world factory datasets to predict defects. Benchmarks, for instance, Accuracy, precision, recall, and F1, were used to evaluate the performance of the suggested model. The results indicate good performance compared with state-of-the-art methods. The accuracy detected at peak iteration (3000 iterations) is not less than 99.78 percent with an Average Relative Error (ARE) equal to 2.7 percent and a processing time equal to 80 milliseconds. ES-WSN improved product quality assurance to 98 percent. The research focused on using shallow learning as a computer vision technique on real-time images of specific products to develop an ADDS and improve product quality in cognitive manufacturing.
  • Design and Development of Powerful Neuroevolution Based Optimized GNN-BiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks
    International Journal of Intelligent Engineering and Systems, 2024
    The exponential growth of online information has necessitated effective solutions to combat information overload and optimize network resources.Recommender systems (RS) have emerged as critical tools in this context, opening new avenues for research.However, RS encounters formidable challenges in understanding user behavior and preferences, reducing redundancy in recommendations within social networks (SN), and ensuring scalability and accuracy.To address these issues, this study introduces a novel approach that harnesses the power of two neural networks: Bidirectional long short-term memory (BILSTM) for SN behavior analysis and graph neural network (GNN) for modelling consumer behaviour, both represent a powerful Neuroevolution network.The proposed RS, tailored for SNs, demonstrates significant performance enhancements when compared to traditional deep learning and deep reinforcement learning algorithms.The methodology involves a rigorous training process with a 70% training set and 10% validation set to mitigate overfitting, with final evaluation on a previously unseen 20% testing set.Optimization techniques, including momentum and adaptive learning rates, are applied to GNN-BiLSTM, ensuring computational efficiency.The results unequivocally showcase the effectiveness of this approach in generating more precise and contextually relevant recommendations.By leveraging BILSTM and GNN, the RS gains a deeper understanding of user preferences and item relationships, resulting in superior recommendation quality.Performance metrics such as root mean squared error (RMSE) and mean absolute error (MAE) unequivocally demonstrate the superiority of the proposed model over traditional deep learning and deep reinforcement learning algorithms.In conclusion, the integration of BILSTM and GNN in RS offers a promising solution to the pressing challenges faced by existing systems.This hybrid approach significantly elevates the accuracy and efficiency of recommendations in SNs, paving the way for valuable insights and potential enhancements in future recommendation systems which depends on Neuroevolution approach.
  • Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach
    International Journal of Intelligent Engineering and Systems, 2024
    Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing.However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes.Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern.This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment.The core objective is to ensure sustainable intelligent computing through a colony strategy.The research primarily focusses on the applying sigmoid fish swarm optimization (SiFSO) for early compromised device detection and subsequently alerting other network nodes.Additionally, our data center implements an innovative ant skyscape architecture (ASA) cooling mechanism, departing from traditional, unsustainable cooling strategies that harm the environment.To validate the effectiveness of these approaches, extensive simulations were conducted.The evaluations primarily revolved around the fish colony's ability to detect compromised devices, focusing on source tracing, realistic modelling, and an impressive 98% detection accuracy rate under ASA cooling solution with 0.16 ºC within 1,300 second.Compromised devices pose a substantial risk to green data centers, as attackers could manipulate and disrupt network equipment.Therefore, incorporating cyber enhancements into the green data center concept is imperative to foster more adaptable and efficient smart networks.
  • Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles
    Halah Hasan Mahmoud, Marwan Kadhim Mohammed Al-Shammari, Gehad Abdullah Amran, Elsayed Tag eldin, Ala R. Alareqi, Nivin A. Ghamry, Ehaa ALnajjar, Esmail Almosharea
    Computers Materials and Continua, 2023
    Utilizing artificial intelligence (AI) to protect smart coastal cities has become a novel vision for scientific and industrial institutions. One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles (UVs) for anti-submarine attacks. This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy. The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach, and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution. The research’s noteworthy findings demonstrate UVs’ logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information. The results suggest that detecting the submarine early increases the likelihood of averting a collision. The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency. Securing communication between Unmanned Aerial Vehicles (UAVs) improves the level of secrecy necessary for the task. The swarm navigation is based on a peer-to-peer system, which allows each UAV to access information from its peers. This, in turn, helps the UAVs to determine the best route to take and to avoid collisions with other UAVs. The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.
  • IoT-Based Motorbike Ambulance: Secure and Efficient Transportation
    Halah Hasan Mahmoud, Abed Saif Alghawli, Marwan Kadhim Mohammed Al-shammari, Gehad Abdullah Amran, Khaled H. Mutmbak, Khaled H. Al-harbi, Mohammed A. A. Al-qaness
    Electronics Switzerland, 2022
    The predilection for 5G telemedicine networks has piqued the interest of industry researchers and academics. The most significant barrier to global telemedicine adoption is to achieve a secure and efficient transport of patients, which has two critical responsibilities. The first is to get the patient to the nearest hospital as quickly as possible, and the second is to keep the connection secure while traveling to the hospital. As a result, a new network scheme has been suggested to expand the medical delivery system, which is an agile network scheme to securely redirect ambulance motorbikes to the nearest hospital in emergency cases. This research provides a secured and efficient telemedicine transport strategy compatible with the vehicle social network (VSN). The proposed telemedicine method should find the best ambulance motorbike route for getting patients to the hospital as quickly as possible. This approach also enables the secure exchange of information between ambulance motorbikes and hospitals. Ant colony optimization (ACO) is utilized as a SWARM technique to expand the capabilities of 5G-wireless mesh networks to determine the best path. To secure communication, the secure socket layer (SSL), which is boosted once by the advanced encryption standard (AES), has achieved a new suggested scheme as a cybersecurity approach. According to the performance evaluation, this approach will determine the optimal route for motorbike ambulances. Additionally, this technique establishes a secure connection between ambulance motorbikes and the hospital. The study enhances telemedicine transportation.
  • Attention enhancement system for college students with brain biofeedback signals based on virtual reality
    Marwan Kadhim Mohammed Al-shammari, TianHan Gao, Rana Kadhim Mohammed, Song Zhou
    Multimedia Tools and Applications, 2022
    Attention Deficit Hyperactivity Disorder (ADHD) is a common and heritable disease that has an environmental influence on brain function. The diseases affects multiple aspects of the lives of college students, not only on their study but also on the relationships with other people. The problem with ADHD attention involves short term memory. The purpose of this paper is to investigate the capability of improving short term working memory for ADHD patients by the aid of technology a proper VR environment is built for ADHD, who are isolated from the real circumference. Electroencephalography (EEG) is taken as biofeedback to read the brain signal from the patient. A deep learning approach and an artificial neural network method, are employed to efficiently and accurately process EEG. The findings of the trial indicate that the virtual reality recommended system will play a greater role in improving the attention to the ADHD patient.
  • Improve memory for alzheimer patient by employing mind wave on virtual reality with deep learning
    Marwan Kadhim Mohammed Al-shammari, Gao Tian Han
    Advances in Intelligent Systems and Computing, 2019
    Alzheimer disease is associated with many risks, including the destruction of family morale and the loss of experience of many scientists in different areas. However, little research depending on computer science has been conducted to explore this disease. The purpose of this study is trying to find the possibility of using computer techniques to improve the therapeutic methods of Alzheimer disease. This paper elaborates the approach of using EEG signals on virtual reality environment and introducing them as a patient’s therapeutic program to improve temporary memory. The patient’s memory is rearranging based on a suitable brain signal through the theory of artificial neural network and deep learning technique so that the memory is able to be gradually improved.
  • Secure transition for robotic surgery with elliptic curve diffie hellman
    Wang Bei Lei, Marwan Kadhim Mohammed Al-shammari, HuiMing Xiao
    ACM International Conference Proceeding Series, 2018

RECENT SCHOLAR PUBLICATIONS

  • Efficiency-Oriented Mutual Authentication Protocol for MCC Using Elliptic Curve Cryptography
    MKM AL-shammari, SA Abead, HH Mahmoud
    Journal of Internet Services and Information Security 16 (1), 595-611 , 2026
    2026
    Citations: 1
  • Semantic brain tumor segmentation from 3D MRI using u2-net with custom dilated and residual u-block
    H Akbar, NS Herman, MKM Al-Shammari
    International Journal of Industrial Optimization 7 (1), 15-26 , 2026
    2026
  • Enhancing Cognitive Performance of College Students Based on Adam Optimization Assisted by Brain Biofeedback
    EA Jebur, MR Jassim, MKM Al-shammari
    Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable … , 2025
    2025
  • A Novel Hybrid CNN-PCA Model to Improve Security in Chaotic IoT Environments
    MKM AL-Shammari
    Journal of Internet Services and Information Security 15 (4), 515-527 , 2025
    2025
  • Improve Industrial Automation Through the Fusion of Cognitive Manufacturing and Echo State Wireless Sensor Network
    AS Alkhafaji, IM Hameed, MKM AL-shammari
    281-297 :Journal of Wireless Mobile Networks, Ubiquitous Computing, and … , 2025
    2025
    Citations: 1
  • Sentiment analysis for e-commerce product reviews based on feature fusion and bidirectional long short-term memory
    H Akbar, D Aryani, MKM Al-shammari, MB Ulum
    Jurnal Teknik Informatika (Jutif) 5 (5), 1385-1391 , 2024
    2024
    Citations: 3
  • Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach.
    HH Mahmoud, MKM Al-Shammari, IM Hameed, II Al_Barazanchi, ...
    International Journal of Intelligent Engineering & Systems 17 (3) , 2024
    2024
    Citations: 7
  • Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks.
    MKM Al-Shammari, EA Jebur, HH Mahmoud, II Al_Barazanchi, R Sekhar, ...
    International Journal of Intelligent Engineering & Systems 17 (1) , 2024
    2024
    Citations: 3
  • Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles
    HH Mahmoud, MKM Al-Shammari, GA Amran
    Computers, Materials & Continua 76 (1), 573–589 , 2023
    2023
  • Iot-based motorbike ambulance: secure and efficient transportation
    HH Mahmoud, AS Alghawli, MKM Al-shammari, GA Amran, KH Mutmbak, ...
    Electronics 11 (18), 2878 , 2022
    2022
    Citations: 6
  • Attention enhancement system for college students with brain biofeedback signals based on virtual reality
    MKM Al-shammari, TH Gao, RK Mohammed, S Zhou
    Multimedia Tools and Applications 79 (47-48), 1182-1198 , 2021
    2021
    Citations: 15
  • Improve memory for alzheimer patient by employing mind wave on virtual reality with deep learning
    MKM Al-shammari, GT Han
    International Conference on Innovative Mobile and Internet Services in … , 2018
    2018
    Citations: 3
  • Secure Transition for Robotic Surgery With Elliptic Curve Diffie Hellman
    WB Lei, MKM Al-shammari, HM Xiao
    Proceedings of the 2018 4th International Conference on Mechatronics and … , 2018
    2018
  • A Secure and Efficient System for Ambulance Vehicular Social Network Based on Re-Fragmentation and Swarm
    T Gao, MKM Al-shammari
    IT CoNvergence PRActice INPRA 6 (3), 1-8 , 2018
    2018
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Attention enhancement system for college students with brain biofeedback signals based on virtual reality
    MKM Al-shammari, TH Gao, RK Mohammed, S Zhou
    Multimedia Tools and Applications 79 (47-48), 1182-1198 , 2021
    2021
    Citations: 15
  • Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach.
    HH Mahmoud, MKM Al-Shammari, IM Hameed, II Al_Barazanchi, ...
    International Journal of Intelligent Engineering & Systems 17 (3) , 2024
    2024
    Citations: 7
  • Iot-based motorbike ambulance: secure and efficient transportation
    HH Mahmoud, AS Alghawli, MKM Al-shammari, GA Amran, KH Mutmbak, ...
    Electronics 11 (18), 2878 , 2022
    2022
    Citations: 6
  • Sentiment analysis for e-commerce product reviews based on feature fusion and bidirectional long short-term memory
    H Akbar, D Aryani, MKM Al-shammari, MB Ulum
    Jurnal Teknik Informatika (Jutif) 5 (5), 1385-1391 , 2024
    2024
    Citations: 3
  • Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks.
    MKM Al-Shammari, EA Jebur, HH Mahmoud, II Al_Barazanchi, R Sekhar, ...
    International Journal of Intelligent Engineering & Systems 17 (1) , 2024
    2024
    Citations: 3
  • Improve memory for alzheimer patient by employing mind wave on virtual reality with deep learning
    MKM Al-shammari, GT Han
    International Conference on Innovative Mobile and Internet Services in … , 2018
    2018
    Citations: 3
  • Efficiency-Oriented Mutual Authentication Protocol for MCC Using Elliptic Curve Cryptography
    MKM AL-shammari, SA Abead, HH Mahmoud
    Journal of Internet Services and Information Security 16 (1), 595-611 , 2026
    2026
    Citations: 1
  • Improve Industrial Automation Through the Fusion of Cognitive Manufacturing and Echo State Wireless Sensor Network
    AS Alkhafaji, IM Hameed, MKM AL-shammari
    281-297 :Journal of Wireless Mobile Networks, Ubiquitous Computing, and … , 2025
    2025
    Citations: 1
  • A Secure and Efficient System for Ambulance Vehicular Social Network Based on Re-Fragmentation and Swarm
    T Gao, MKM Al-shammari
    IT CoNvergence PRActice INPRA 6 (3), 1-8 , 2018
    2018
    Citations: 1
  • Semantic brain tumor segmentation from 3D MRI using u2-net with custom dilated and residual u-block
    H Akbar, NS Herman, MKM Al-Shammari
    International Journal of Industrial Optimization 7 (1), 15-26 , 2026
    2026
  • Enhancing Cognitive Performance of College Students Based on Adam Optimization Assisted by Brain Biofeedback
    EA Jebur, MR Jassim, MKM Al-shammari
    Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable … , 2025
    2025
  • A Novel Hybrid CNN-PCA Model to Improve Security in Chaotic IoT Environments
    MKM AL-Shammari
    Journal of Internet Services and Information Security 15 (4), 515-527 , 2025
    2025
  • Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles
    HH Mahmoud, MKM Al-Shammari, GA Amran
    Computers, Materials & Continua 76 (1), 573–589 , 2023
    2023
  • Secure Transition for Robotic Surgery With Elliptic Curve Diffie Hellman
    WB Lei, MKM Al-shammari, HM Xiao
    Proceedings of the 2018 4th International Conference on Mechatronics and … , 2018
    2018