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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Moatasem Yaseen Al-Ridha
Northern Technical University
Computer Science & IT Electrical & Electronics Engineering

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Regenerating face images from multi-spectral palm images using multiple fusion methods Raid Rafi Al-Nima; Moatasem Yaseen Al-Ridha; Farqad Hamid Abdulraheem
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.12857

Abstract

This paper established a relationship between multi-spectral palm images and a face image based on multiple fusion methods. The first fusion method to be considered is a feature extraction between different multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to be considered is a score fusion between two parts of an output face image. Our method suggests that both right and left hands are used, and that each hand aims to produce a significant part of a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion part to reconstruct the full-face image, in order to examine its appearance. This topology provided interesting results of Equal Error Rate (EER) equal to 1.99%.
Expecting confirmed and death cases of covid-19 in Iraq by utilizing backpropagation neural network Moatasem Yaseen Al-Ridha; Ammar Sameer Anaz; Raid Rafi Omar Al-Nima
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2876

Abstract

The world is currently facing a strong epidemic and pandemic of coronavirus. This motivates establishing our paper, where this virus pushes researchers to study, investigate, observe, analyse and try solving its related issues. In this work, an artificial neural network (ANN) model of backpropagation neural network (BNN) with two hidden layers is proposed for expecting confirmed cases and death cases of coronavirus disease 2019 (covid-19). As a field of study, Iraq country has been considered in this paper. Covid-19 dataset from our world in data (OWID) is used here. Promising result is achieved where a very small error value of 0.0035 is reported in overall the evaluations. This paper may implicate establishing further researches that consider other parameters and other countries over the world. It is worth mentioning that the suggested ANN model may help decision maker people in taking quarantine movements against the strong epidemic and pandemic of covid-19.
Predicting death and confirmed cases of coronavirus Farqad Hamid Abdulraheem; Moatasem Yaseen Al-Ridha; Raid Rafi Omar Al-Nima
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3133

Abstract

At the end of 2019, a new virus called coronavirus has globally spread causing severe effections. In this paper, an artificial intelligence (AI) method is proposed to predict numbers of death and confirmed coronavirus cases. Efficient machine learning (ML) network named the byesian regularization backpropagation (BRB) is employed. It can estimates numbers of death and confirmed cases from applied population density and date. So, the BRB uses the population density, month and day as inputs, and predicts the new cases per million and new deaths per million as outputs. The network was trained and assessed by using a daily coronavirus recorded dataset known as the our world in data (OWID). The considered dates here are from the 31st of December 2019 to the 13th of October 2020. Furthermore, recorded information from countries over all world are employed. The obtained results provided a good promising performance with a testing mean absolute error (MAE) equal to 0.0218.
Co-Authors Ammar Sameer Anaz Farqad Hamid Abdulraheem Raid Rafi Al-Nima Raid Rafi Omar Al-Nima