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All Journal International Journal of Electrical and Computer Engineering
Hadeel K. Aljobouri
Al-Nahrain University
Computer Science & IT Electrical & Electronics Engineering

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Anti-resonant based nested terahertz fiber design for illicit drugs detection Shaymaa Riyadh Tahhan; Hadeel K. Aljobouri; Baraa Riyadh Altahan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1588-1598

Abstract

Anti-resonant hollow-core fibers (AR-HCFs) have gotten a lot of interest lately because of their potential uses in different medical sensing applications. In this work, an anti-resonant THz fiber (ATF) biosensor is implemented to check for illicit drugs and identify them at airport borders. Three different unlawful medicines have been chosen for the proposed design, Cocaine, Amphetamine, and Ketamine. A novel hollow-core anti-resonant fibers (HC-ARF) Matryoshka shape sensor has been designed for detecting the illegal drugs. The proposed design shows a robust sensitivity ranging from 99.8-99.9% and shallow confinement losses compared to other articles in the same field, as the higher losses are 9.3×10-4 dB/m with cocaine. Bending loss lessens as the bending radius rises while it is still below 1 dB/cm for radius more than 10 cm. The numerical simulation outcomes displayed that the designed HC-ARF has 0.0643±0.0238 ps/THz/cm flat dispersion at 0.6-2 THz. As the first application in this field, this work will be the first published in the literature.
Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features Noor Hamzah Alkurdy; Hadeel K. Aljobouri; Zainab Kassim Wadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3440-3448

Abstract

This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classifier performance is evaluated using its accuracy, recall, and F1 score. With an accuracy of 99.47%, CNN-XGBoost is the most accurate model.
Co-Authors Baraa Riyadh Altahan Noor Hamzah Alkurdy Shaymaa Riyadh Tahhan Zainab Kassim Wadi