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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Fouad Shaker Tahir
University of technology

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Face recognition using enhancement discrete wavelet transform based on MATLAB Asma Abdulelah Abdulrahman; Fouad Shaker Tahir
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1128-1136

Abstract

In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.
Distinguishing license plate numbers using discrete wavelet transform technology based deep learning Asma Abdulelah Abdulrahman; Fouad Shaker Tahir
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1771-1776

Abstract

Cars that violate the red light, and to increase the huge number of cars in violation, it is necessary to discover a system for identifying car plate numbers with the intervention of a computer, computer vision and neural networks segment and detail the number plates by designing regular algorithms to identify the number of license plates in violation. In this work, interest is in identifying the Iraqi car plate in order to know the place where the vehicle papers and the letters on which the vehicle depends and to know the location of the car were completed. The technique that was carried out in this work is to build new wavelets from polynomials by mathematical methods and discover a new algorithm using the MATLAB program to identify each number in the vehicle plate with a specific color by training a convolutional neural network (CNN) after analyzing the image using the new wavelets to identify the contents of the plate and good results have been reached. The accuracy level was reached with good values of up to 95%.
The effectiveness of the Hermite wavelet discrete filter technique in modify a convolutional neural network for person identification Fouad Shaker Tahir; Asma Abdulelah Abdulrahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp290-298

Abstract

Classification is of great importance in the field of image processing, and convolutional neural networks (CNNs) have achieved great success in this field. Although CNN has proven to be a powerful technology for image recognition problems, it has failed in complex situations involving many realworld applications (for example, visual monitoring and automated driver assistance). Where it is difficult to detect a human in a series of images for various reasons. One of these reasons is the difference in the size of the human body, the height of the platform to which the camera is attached during the task of capturing accurate images, and the short training time in using the cameras, all of which are important factors to consider for the robustness and effectiveness of the human classification system. In this paper, a new deep CNN-based learning model is designed based on a new discrete waveform transformation (DWT) derived from discrete Hermit wavelet transform (DHWT) instead of modular wavelet, and the second stage is to train the convolutional neural network Hermit wavelets (HWCNN) is the most accurate and efficient deep learning.
Kidney stones detection based on deep learning and discrete wavelet transform Fouad Shaker Tahir; Asma Abdulelah Abdulrahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1829-1838

Abstract

The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).
Co-Authors Asma Abdulelah Abdulrahman