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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mohammad Tariq Yaseen
University of Mosul
Computer Science & IT

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Aluminum based nanostructures for energy applications Mohammad Tariq Yaseen; Abdalem A. Rasheed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18146

Abstract

The plasmonic material properties of aluminum allow active plasmon resonances extending from the blue color in the visible range to the ultraviolet (UV) region of the spectrum. Whereas Al is usually avoided for applications of plasmonics due to its losses in the infrared spectrum region. In this work, the study of the scatter and absorption of disk nanoantennas (DNAs) using various types of materials Au, Ag, and Al is accomplished by using the CST microwave studio suite simulation. The results showed that Al can offer good plasmonic properties when DNA radius is 25 nm to 125 nm at 20 nm height and working wavelengths longer than 800 nm in the near-infrared (NIR) region. Al produces negative plasmonic features around 800 nm wavelength due to the interband transition in the imaginary part of epsilon. For Au and Ag, the plasmonic characteristics rapidly decayed when the DNA radius was higher than 60 nm, but in contrast, Al offers good plasmonic features at these large dimensions of DNAs. This extended response of Al in UV, visible, and NIR, incorporated with its low cost, natural abundance, low native oxide, and amenability to industrial processes, could make Al an extremely promising plasmonic metal candidate for energy applications.
Neural network-based diagnosis of type 2 diabetes using an iridology approach Alaa Abdulkareem Ahmed; Mohammad Tariq Yaseen
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1226-1237

Abstract

The growing global occurrence of type 2 diabetes requires the development of non-invasive and effective diagnostic methods. This work proposes a novel approach to detecting type 2 diabetes using iridology and machine learning (ML) techniques. By analyzing the iris of the right eye, a single region of interest (ROI) corresponding to the head of the pancreas is recognized for feature extraction. A total of 112 statistical and texture features are extracted using gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. Five neural network (NN) models, narrow, medium, wide, bi-layered, and tri-layered are deployed to classify healthy and diabetic people. The models are trained and assessed using a range of k-fold values (2 to 20) to optimize performance. The highest classification accuracy of 83.2% was reached using the narrow neural network (NNN) model at 7-fold cross-validation. This work exhibts the potential of iridology-based ML approaches for non-invasive diabetes diagnosis, providing a promising substitute to traditional blood tests.
Co-Authors Abdalem A. Rasheed Alaa Abdulkareem Ahmed