Mohamed Elshaikh Elobaid Said Ahmed
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Mohamed Elshaikh Elobaid Said Ahmed
Official Name
Mohamed Elshaikh Elobaid , Said Ahmed
Alternative Name
Ahmed, Mohamed Elshaikh Elobaid Said
Said Ahmed, Mohamed Elshaikh Elobaid
Elobaid, Mohamed Elshaikh
Main Affiliation
Scopus Author ID
57190012447
Researcher ID
R-7502-2019

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  • Publication
    High-performance data throughput analysis in wireless ad hoc networks for smart vehicle interconnection
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025)
    Alaa Kamal Yousif Dafhalla
    ;
    Amira Elsir Tayfour Ahmed
    ;
    Nada Mohamed Osman Sid Ahmed
    ;
    Ameni Filali
    ;
    Lutfieh S. Alhomed
    ;
    Fawzia Awad Elhassan Ali
    ;
    Asma Ibrahim Gamar Eldeen
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Tijjani Adam
    Vehicular Ad Hoc Networks play a crucial role in enabling Smart City applications by facilitating seamless communication between vehicles and infrastructure. This study evaluates the throughput performance of different routing protocols, specifically AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM, under various city and highway scenarios in complex networks. The analysis covers key parameters including traffic generation, packet sizes, mobility speeds, and pause times. Results indicate that TOM and DEM profiles significantly improve throughput compared to traditional AODV and GPSR protocols. GPSR:TOM achieves the highest throughput across most scenarios, making it a promising solution for high-performance data transmission in Smart Cities. For instance, GPSR:TOM achieves an average throughput of 3.2 Mbps in city scenarios compared to 2.8 Mbps for GPSR, while in highway scenarios, the throughput increases to 3.6 Mbps. Additionally, AODV:DEM records a throughput of 3.4 Mbps for high traffic generation, outperforming AODV:TOM at 3.1 Mbps and baseline AODV at 2.7 Mbps. The findings highlight the importance of optimizing data throughput to ensure reliability and efficiency in complex vehicle interconnection systems, which are critical for traffic management, accident prevention, and real-time communication in smart urban environments
  • Publication
    AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus
    (Elsevier, 2025)
    Amira Elsir Tayfour Ahmed
    ;
    Th.S. Dhahi
    ;
    Tahani A. Attia
    ;
    Fawzia Awad Elhassan Ali
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Tijjani Adam
    ;
    Subash Chandra Bose Gopinath
    AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes.
  • Publication
    Computer-aided efficient routing and reliable protocol optimization for autonomous vehicle communication networks
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-01)
    Alaa Kamal Yousif Dafhalla
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Amira Elsir Tayfour Ahmed
    ;
    Ameni Filali
    ;
    Nada Mohamed Osman SidAhmed
    ;
    Tahani A. Attia
    ;
    Badria Abaker Ibrahim Mohajir
    ;
    Jawaher Suliman Altamimi
    ;
    Tijjani Adam
    The rise of autonomous vehicles necessitates advanced communication networks for effective data exchange. The routing protocols Ad hoc On-Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR) are vital in mobile networks (MANETs) and vehicular ad hoc networks (VANETs). However, their performance is affected by changing network conditions. This study examines key routing parameters—MaxJitter, Hello/Beacon Interval, and route validity time—and their impact on AODV and GPSR performance in urban and highway scenarios. The simulation results reveal that increasing MaxJitter enhances AODV throughput by 12% in cities but decreases it by 8% on highways, while GPSR throughput declines by 15% in cities and 10% on highways. Longer Hello intervals improve AODV performance by 10% in urban settings but reduce it by 6% on highways. Extending route validity time increases GPSR’s Packet Delivery Ratio (PDR) by 10% in cities, underscoring the need to optimize routing parameters for enhanced VANET performance.
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