Mahmood Hameed Qahtan
Research InterestsComputer Networks
IoT
AI
Image Processing
| Gender | MALE |
|---|---|
| Place of Work | Technical Engineering College for Computer and AI / Mosul |
| Department | Department of Artificial Intelligence Engineering Techniques |
| Position | Responsible of Computer and Network Maintenance Unit |
| Qualification | M.S. |
| Speciality | Computer Techniques Engineering |
| mahmood.hameed@ntu.edu.iq | |
| Phone | +9647701819267 |
| Address | iraq/mosul, Nineveh, Mosul, Iraq |
Skills
Computer Networks (95%)
Internet of Things (IoT) (90%)
Image Processing (85%)
Machine Learning (Supervised & Unsupervised) (80%)
Deep Learning and Neural Networks (80%)
Academic Qualification
M.Sc. in Computer Technology Engineering -Technical Engineering College/Northern Technical University/Mosul, Iraq.
Nov 1, 2020 - Nov 30, 2022B.Sc in Computer Technologe Engineering-Technical Engineering College/Mosul, Iraq.
Sep 1, 2008 - Jul 1, 2012Working Experience
College Network Management, Devices maintenance [Northern technical University, Technical Engineering College for Computer and AI / Mosul, Department of Artificial Intelligence Techniques Engineering[Responsible of Computer and Network Maintenance Unit]]
Oct 9, 2024 - PresentTeaching Practical and Theoretical Software Development, research papers, performing workshops [Northern technical University, Technical Engineering College for Computer and AI / Mosul, Department of Artificial Intelligence Techniques Engineering [ Asset Lecturer]]
Sep 23, 2024 - PresentPublications
Mahmood Hameed Qahtan Bone Fracture Detection Using Hybrid EfficientNet-B0 and ResNet50 with SVM: A Comparative Performance Analysis
Jul 31, 2025Journal Ingenierie des Systemes d'Information
publisher Scientific ReseaInternational Information and Engineering Technology Association (IIETA)rch Publishing
Issue 7
Volume 30
The accurate identification of osseous fractures is crucial for precise medical diagnoses and treatment planning. This study introduces a new hybrid classification approach, integrating EfficientNet-B0 and ResNet50 deep learning models with an SVM classifier, surpassing traditional versions. Leveraging pre-trained feature extractors for EfficientNet-B0 and ResNet50, the proposed method achieves a test accuracy of 98.01% and a recall of 0.99 for fractured cases with EfficientNet-B0+SVM, while reducing runtime to 20.44 minutes. ResNet50 + SVM also improved accuracy from 80.05% to 96.41% with a runtime of 38.47 minutes, compared to 83.96 minutes standalone. This hybrid approach demonstrates significant enhancements in accuracy and efficiency, positioning it as a promising tool for clinical bone fracture detection.
A Review of Artificial Intelligence Techniques for Medical Image Enhancement
Jun 15, 2025Journal International Journal of Computational and Electronic Aspects in Engineering
publisher Scientific Research PublishingInternational Journal of Computational and Electronic Aspects in Engineering
DOI https://doi.org/10.26706/ijceae.6.2.2 0250406
Issue Issue 2
Volume Volume 6
Medical imaging plays a crucial role in diagnosis, treatment planning, and monitoring of diseases. However, the quality of medical images is often compromised due to noise, low resolution, and artifacts. Recent advancements in Artificial Intelligence (AI), particularly deep learning techniques, have significantly improved image enhancement capabilities in the medical domain. This paper comprehensively reviews AI-based image enhancement methods applied to medical imaging. We discuss various enhancement techniques, including denoising, super resolution, contrast enhancement, and artifact removal. Additionally, we provide an overview of commonly used datasets, evaluation metrics, and recent developments in AI models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures. Finally, we highlight current challenges.
Internet of things based real-time electric vehicle and charging stations monitoring system
Sep 1, 2022Journal Indonesian Journal of Electrical Engineering and Computer Science
DOI 10.11591/ijeecs.v27.i3.pp1661-1669
Volume Vol. 27, No. 3,
Due to a shortage of fuel sources and the increment in environmental pollution, efficient techniques should be introduced. The best solution is to move to the use of electric vehicles. The article aims to develop a solution for electric vehicle (EV) charging station locations that utilize the internet of things (IoT) technology. The IoT is a paradigm that uses sensors and transmitting networks to provide current facilities with a real-time global communication perspective of the physical world. This paper proposes a real-time system to provide a real-time update to EV location and charging stations (CSs) location to reduce time lost by users searching CSs, and provides real-time charging station (CS) recommendations for EV users by displaying the nearest CS, provide estimation arrival time to the nearest CS, display distance between nearest CS and EV real-time updated. The work of the proposed system was tested, and the most significant error rate (17 meters) is represented by the difference in the distance obtained from the system and the distance obtained from Google Map. The total accuracy of the design for the tested case is (98.014%).
IoT-Based Electrical Vehicle’s Energy Management and Monitoring System
Jul 27, 2022Journal Open Access Library Journal
publisher Scientific Research Publishing
Volume Volume 9
It is necessary to move to the use of electric vehicles, as they represent the next generation of transportation. Electrical vehicle batteries may be damaged due to overcharging or over-discharging, so they need to precisely estimate the state of charge to extend their lifespan and protect the connected compo nents they power. This paper presents battery management and monitoring system of electric vehicles, low-cost and IoT-based, in real-time, and easily used to help users through an application supporting the Internet of Things technology to display the essential information required about the battery’s status as battery capacity and the charging and consuming current. This in formation is updated and displayed in real-time. The proposed system is im plemented using an ESP32 microcontroller, blynk mobile application, and Blynk IoT platform.
Charging Station of Electric Vehicle Based on IoT: A Review
Jun 1, 2022Journal Open Access Library Journal
publisher Scientific Research Publishing
Volume Volume 9
At present, humans face the problem of lack of fuel and environmental pollu tion to reduce pollution as well as fuel consumption. We have to use electric vehicles, but the spread of these vehicles is still low due to the lack of charging stations as well as their high prices. This paper reviews important research about charging stations with IoT and the charging type used in these stations, and it makes a comparison between them, as well as the sources for these sta tions, which may be renewable and non-renewable energy. Using IoT saves the time spent by the user looking for the stations’ location with the possibil ity of knowing the location of charging stations by using a mobile application, as well as the possibility of placing charging stations in public places and park ing stations, thus making it easier to move to the use of these new vehicles.