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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences
Hadeel N. Abdullah
University of Technology
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Objects detection and tracking using fast principle component purist and kalman filter Hadeel N. Abdullah; Nuha H. Abdulghafoor
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (931.057 KB) | DOI: 10.11591/ijece.v10i2.pp1317-1326

Abstract

The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Region-convolution neural networks (Fast-RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.
A hybrid bacterial foraging and modified particle swarm optimization for model order reduction Hadeel N. Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (732.605 KB) | DOI: 10.11591/ijece.v9i2.pp1100-1109

Abstract

This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
A new generation of artificial intelligence contributing to improving the image quality Salwa A. Alagha; Hadeel N. Abdullah; Suad Khairi Mohammed
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp664-676

Abstract

High-resolution (HR) images provide inclusive and critical information, which is substantial for many implementations. Production operation for high-quality images from low-quality images can be costly and time consuming. The main advancement in this domain is produced by enhanced super-resolution generative adversarial network (ESRGAN); the ESRGAN and different deep learning (DL) models exhibit prominent advances in image super-quality. This research proposes introducing the discrete wavelet transform (DWT) as a multi-scale analysis stage that feeds into the network, whereby the frequencies are analyzed before being fed into the generative adversarial networks (GAN). The goal is to enhance the ability to recover edges and fine details, especially in low-resolution images. The performance of this proposed model is implemented, evaluated, and comparatively assessed. Key performance parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), are calculated, which compare the proposed model with other image-improving models (Bicubic, SRResNet, and ESRGAN). The experimental results indicate that the proposed method ESRGAN new yields a good result in image improvement, with a PSNR of (26.22, 26.00, 25.51, and 23.89) and an SSIM of (0.6638, 0.6255, 0.5882, and 0.6286) for four datasets, respectively.
Co-Authors Nuha H. Abdulghafoor Salwa A. Alagha Suad Khairi Mohammed