Hasan abdulellah abdulla alsarraf
Research Interestsartificial intelligence
deep learning
machine learning
python
recognition
security
quantum computing
| Gender | MALE |
|---|---|
| Place of Work | Technical Engineering College/ Mosul |
| Position | Statistics unit official |
| Qualification | Master |
| Speciality | Computer engineering |
| hasan.alsarraf@ntu.edu.iq | |
| Phone | 009647729681096 |
| Address | alqadsya zhour, ninva, Mosul, Iraq |
Hasan serves as an Assistant Lecturer in the Department of Applied Mechanical Engineering Technologies at Technical college of Mousl at Northern Technical University (NTU). He is also involved in administrative work as the Head of the Statistics Unit in the Department of Studies and Planning. Hasan is a member of the teaching staff at the College of Technical Engineering in Mosul.
In addition, Hasan Abdulelah Abdullah alsarraf is a PhD student at University Sains Malaysia (USM), specializing in artificial intelligence and machine learning. His current research focuses on developing a hybrid AI model for drug-target interaction prediction, contributing to advancements in computational drug discovery.
Hasan earned his Master’s degree in Computer Engineering from Northern Technical University, College of Technical Engineering, Mosul, in 2021. He also holds a Bachelor’s degree in Computer Engineering Technologies from Northern Technical University, College of Technical Engineering, Mosul, in 2007. His academic journey has equipped him with expertise in AI, deep learning, and security applications.
An active researcher, Hasan has published 4 scientific papers in various fields within his area of expertise, contributing valuable insights to the scientific community. His work extends to recognition systems, machine learning, and quantum computing, with a focus on medical image analysis and AI-driven security solutions. Also participated in numerous conferences and has reviewed 124 scientific papers within prestigious global research fields. His involvement in academic review and research continues to make significant contributions to the advancement of AI and related technologies.
124 +
Reviewer
Skills
Artificial intelligence (90%)
Python (85%)
Deep Learning (85%)
Image Processing (80%)
Quantum Computing (70%)
Security (80%)
Academic Qualification
Master Degree
Sep 1, 2019 - Oct 24, 2021M.Sc. research focused on CNC (Plotter Prototype ) Using Arduino Microcontroller
B.Sc in Computer Engineering- Northern Technical University
Sep 1, 2003 - Jul 1, 2007Working Experience
Artificial Intelligence, Machine Learning, Deep Learning, Drug target interaction prediction, [present]
Jan 4, 2025 - PresentAI Driven Based Model Web Security [present]
Feb 6, 2025 - PresentDIAGNOSIS-AWARE REAL-TIME VIDEO FACE RECOGNITION MODEL (DART-VFR) [present]
Jun 4, 2024 - Feb 11, 2025Publications
Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model
Apr 2, 2024Journal Bulletin of Electrical Engineering and Informatics
publisher Bulletin of Electrical Engineering and Informatics
DOI DOI: 10.11591/eei.v13i2.6198
Issue 2
Volume 13
One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
DNA recognition using Novel Deep Learning Model
Jan 9, 2024Journal INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS
publisher JET
Issue 2
Volume 70
—DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.
Human–machine interaction for motorized wheelchair based on single-channel electroencephalogram headband
Apr 4, 2023Journal Bulletin of Electrical Engineering and Informatics
publisher Bulletin of Electrical Engineering and Informatics
Issue 2
Volume 12
Human machine interaction (HMI) allows persons to control and interact with devices. Starting from elementary apparatus which acquires input biosignals to controlling various applications. Medical applications are amongst the very important applications of HMI. One of these medical applications is assisting fully/partially paralyzed patients to restore movements or freely move using exoskeletons or motorized wheelchairs. Helping patients with spinal cord injury or serious neurological diseases to restore their movements is a key role objective for most researchers in this field. In this paper, an EEG-based HMI system is proposed to assist patients with tetraplegia/quadriplegia to mentally control a motorized wheelchair so they can move freely and independently. EEG power spectrum (α, β, δ, θ, and γ) from the frontal lobe of brain is recorded, filtered and wirelessly sent to the wheelchair to control directions and engine status. Four different experiments were conducted using the proposed system in order to validate the performance. Two different GUIs scenarios (cross-shaped and horizontal bar) were used with the experiments. Results showed that the horizontal bar scenario considered more user friendly while the cross-shaped is the more suitable for navigation. The implemented system can be equipped with modules and sensors such as GPS, ultrasound and accelerometer that improve the system performance and reliability.
CNC Software Control System Using Visual Basic
Feb 4, 2020Journal IOP Conference Series: Materials Science and Engineering
publisher IOP Conference Series
DOI 10.1088/1757-899X/928/3/032069
Issue 2
Volume 032069
Computer Numerical Control (CNC) machines are having a great industrial role in the progress of humanity to furnish an exactly crafted pieces for different tools and instruments. In the last decade their use had become more popular due to the cheap microcontrollers that emerged and still emerging. The 3D printers' availability made manufacturing of CNC machines simpler. But the need for easing the means to program a CNC machine is still required. This paper gives a software which is intended to make dealing with G-code used with this type of machines easier. This software allows the user to execute manually any step he wants in predefined steps which can be changed on demand, return to origin of working palette, control the working head to any level demanded. Also, it can transform any G-code already prepared to the CNC machine. This software is built using the visual basic vb6.net. The microcontroller used is the Arduino uno, the mechanical set is locally prepared, and the presented application is for line drawing. CNC machines can do a variety of jobs including; drilling, plotting, engraving, laser cut, PCB drilling ... etc. This software had been tested successfully for drawing with ability to show the G-code for each manual movement. Applying it to other CNC applications demands changing the operation head and care for each condition application circumstances.