Ahmed Sattar Alhuseiny

@uowasit.edu.iq

Faculty member/ Biomedical engineering
Wasit University/ College of Engineering

Ahmed Sattar Alhuseiny


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https://researchid.co/aalhuseiny

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Engineering, Computer Engineering
5

Scopus Publications

214

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • DETECTION OF EPILEPTIC SEIZURES IN EEG BY USING MACHINE LEARNING TECHNIQUES
    Muayed AL-Huseiny, Ahmed Sajit
    Diagnostyka, 2023
    In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity.
  • BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI
    Muayed S AL-HUSEINY, Ahmed S SAJIT
    Applied Computer Science, 2022
    Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.
  • Transfer learning with GoogLeNet for detection of lung cancer
    Muayed S AL-Huseiny, Ahmed Sattar Sajit
    Indonesian Journal of Electrical Engineering and Computer Science, 2021
    <p class="p1">The use of computer algorithms has gained momentum in filling/assisting roles of specialists especially in early diagnosis scenarios. This paper proposes the employment of deep neural networks (DNN) to detect images with malignant nodules of lung computed tomography (CT). The method includes subjecting input images to a simple and fast pre-processing which isolates regions of interest (ROI), that’s the lungs dominated area, ridding the images of other surrounding tissues and artefacts. Centered and size normalized images are then fed to a deep neural network for training and validation. In this work transfer learning is used to readjust GoogLeNet DNN to learn this medical data. This includes allowing final layers of the DNN to evolve while restricting deep layers. In this setting, a rough, unprocessed dataset, the IQ-OTH/NCCD lung cancer dataset was used to train/validate the proposed algorithm. Experimental results show that this algorithm scores 94.38% accuracy, which outperforms benchmark method previously used with this dataset.</p>
  • Diagnosis of arrhythmia based on ECG analysis using CNN
    Muayed S. AL-Huseiny, Noor Khudhair Abbas, Ahmed S. Sajit
    Bulletin of Electrical Engineering and Informatics, 2020
    Arrhythmia is the prime indicator of serious heart issues, and, hence, it is essential to be detected properly for early phase treatment. This article presents an approach for the diagnosis of cardiac disorders via the recognition of 17 types of arrhythmia. The proposed approach includes building a convolution neural network (2D-CNN) which is trained by using images of Electrocardiograph (ECG) signals collected from the MIH-BIH database. The ECGs are first converted into images. This step serves twofold: first, CNN is best suited for classifying image data and thus reduces preprocessing, and second, most ECG recordings are still being produced on thermal paper which can then be captured as image. Next, 2D-CNN is trained and validated. Test results show that the proposed method achieves classification accuracy of 96.67% and error of 0.004%. in addition to the superior accuracy achieved by this method compared to the previous literature, this approach enjoys reduced processing time and complexity apart from the training phase, also by dealing with images this method offers high degree of versatility and can be integrated as utility within other applications or wearables.
  • SEU tolerance of FinFET 6T SRAM, 8T SRAM and DICE memory cells
    Ahmed S. Sajit, Michael A. Turi
    2017 IEEE 7th Annual Computing and Communication Workshop and Conference Ccwc 2017, 2017
    FinFET 6T, 8T, Dual Interlocked Storage Cell (DICE) SRAM cells and Single Events Upsets (SEUs) implementations are presented in this paper. Technology scaling has faced many challenges such as higher sensitivity to SEUs, short channel effects, dielectric leakage, and more. SEUs play a vital role in memory system stability. Memory systems with lower sensitivity to SEUs offer better stability and reliability. SEUs arise in smaller technology sizes because of reduced circuit node capacitance, which makes the node more prone to SEUs. SEUs might lead to a catastrophic situation in fields such as medicine, aerospace, etc. Generally, FinFETs have shown a great reduction in leakage current for all cells. The 8T SRAM LP_INV cell has shown the best overall performance among cells of interest in non-radiated environment. However, while 6T and 8T cells were able to resist SEUs of different amplitudes from a few picoseconds to a few tenths of a nanosecond during the hold state, they are more susceptible to SEUs during the read operation. On the other hand, the LP DICE cell with 1 fin per each pass transistor and 2 fins per each inverter transistor has shown great performance and immunity against SEUs—resistance of several nanoseconds during the hold state and for the whole duration of read state. This is because the structure of this cell provides a backup cell which can recover the cell to its steady state even if an SEU hits the cell; this makes the LP DICE cell a good candidate for use in a radiated environment.

RECENT SCHOLAR PUBLICATIONS

  • Generation of Different Laser Beam Profiles Based on Optical Diffractive Elements-Assisted Deep Learning
    A Alhuseiny
    Al-Nahrain University , 2025
    2025
  • TMJ Changes Before and After Implant of Posterior Partial Edentulous Patients Free of TMD Signs Under the Scope of Cadiax Compact II Device
    HJS Alhuseiny, FD Al-Aswad, BS Abdulhameed, THM Almayyahi, ...
    South Eastern European Journal of Public Health, 9-15 , 2024
    2024
  • Detection of epileptic seizures in EEG by using machine learning techniques
    MS AL-Huseiny, AS Sajit
    Diagnostyka 24 (1), 1-7 , 2023
    2023
    Citations: 6
  • Breast cancer cad system by using transfer learning and enhanced ROI
    MS Al-Huseiny, AS Sajit
    Applied Computer Science 18 (1), 99-111 , 2022
    2022
  • Transfer Learning with GoogLeNet for Detection of Lung Cancer
    MS AL-Huseiny, AS Sajit
    Indonesian Journal of Electrical Engineering and Computer Science 22 (2 … , 2021
    2021
    Citations: 180
  • Diagnosis of arrhythmia based on ECG analysis using CNN
    MS Al-Huseiny, NK Abbas, AS Sajit
    Bulletin of Electrical Engineering and Informatics 9 (3), 988-995 , 2020
    2020
    Citations: 18
  • SEU tolerance of FinFET 6T SRAM, 8T SRAM and DICE memory cells
    AS Sajit, MA Turi
    2017 IEEE 7th Annual Computing and Communication Workshop and Conference … , 2017
    2017
    Citations: 10
  • FinFET memory cell improvements for higher immunity against single event upsets
    AS Sajit
    California State University, Fullerton , 2016
    2016

MOST CITED SCHOLAR PUBLICATIONS

  • Transfer Learning with GoogLeNet for Detection of Lung Cancer
    MS AL-Huseiny, AS Sajit
    Indonesian Journal of Electrical Engineering and Computer Science 22 (2 … , 2021
    2021
    Citations: 180
  • Diagnosis of arrhythmia based on ECG analysis using CNN
    MS Al-Huseiny, NK Abbas, AS Sajit
    Bulletin of Electrical Engineering and Informatics 9 (3), 988-995 , 2020
    2020
    Citations: 18
  • SEU tolerance of FinFET 6T SRAM, 8T SRAM and DICE memory cells
    AS Sajit, MA Turi
    2017 IEEE 7th Annual Computing and Communication Workshop and Conference … , 2017
    2017
    Citations: 10
  • Detection of epileptic seizures in EEG by using machine learning techniques
    MS AL-Huseiny, AS Sajit
    Diagnostyka 24 (1), 1-7 , 2023
    2023
    Citations: 6
  • Generation of Different Laser Beam Profiles Based on Optical Diffractive Elements-Assisted Deep Learning
    A Alhuseiny
    Al-Nahrain University , 2025
    2025
  • TMJ Changes Before and After Implant of Posterior Partial Edentulous Patients Free of TMD Signs Under the Scope of Cadiax Compact II Device
    HJS Alhuseiny, FD Al-Aswad, BS Abdulhameed, THM Almayyahi, ...
    South Eastern European Journal of Public Health, 9-15 , 2024
    2024
  • Breast cancer cad system by using transfer learning and enhanced ROI
    MS Al-Huseiny, AS Sajit
    Applied Computer Science 18 (1), 99-111 , 2022
    2022
  • FinFET memory cell improvements for higher immunity against single event upsets
    AS Sajit
    California State University, Fullerton , 2016
    2016