Raid Rafi Omar Al-Nima

Northern Technical University

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Wai Lok Woo

Northumbria University

Musab T. S. Al-Kaltakchi

Northern Technical University

Mohammed A. M. Abdullah

University of Mosul

Jonathon A. Chambers

University of Leicester

Moatasem Yaseen Al-Ridha

Northern Technical University

S.S. Dlay

Newcastle University

Ammar Sameer Anaz

University of Mosul

Lubab H. Albak

Northern Technical University

S.A.M. Al-Sumaidaee

Mustansiriyah University

Hua Mao

Northumbria University

Cooperative Institutions

Northern Technical University

University of Mosul

Newcastle University

Mustansiriyah University

Ministry of Health

Birkbeck, University of London

Northumbria University

University of Lagos

Marche Polytechnic University

Universidad de Londres

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Expecting confirmed and death cases of covid-19 in Iraq by utilizing backpropagation neural network

Bulletin of Electrical Engineering and Informatics (2021)

Moatasem Yaseen Al-Ridha Ammar Sameer Anaz Raid Rafi Omar Al-Nima

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.

Backpropagation

Pandemic

Value (mathematics)

Coronavirus

10.11591/eei.v10i4.2876

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Citations (8)

Classifications of signatures by radial basis neural network

Bulletin of Electrical Engineering and Informatics (2022)

Musab T. S. Al-Kaltakchi S.A.M. Al-Sumaidaee Raid Rafi Omar Al-Nima

The personal signature can be considered one of the most common behavioral biometrics. In this study, signatures are classified according to their specifications. The statistical calculation is considered for the specifications of each signature. Then, a radial basis neural network (RBNN) is adapted to apply multiple classifications for the employed signatures. A big number of signatures are utilized; they are obtained from the database called biometric ideal test (BIT). The total number of collected signatures is equally divided between the testing and training phases, where it is partitioned into 50% for the training and 50% for the testing. The proposed technique could achieve attractive performance, where each of the mean square error (MSE) and mean absolute error (MAE) attained a small value of 0.028. In addition, the proposed approach using the RBNN is compared with the different neural networks of the state-of-the-art techniques in order to demonstrate that the outcomes are acceptable and successful.

Signature (topology)

Basis (linear algebra)

10.11591/eei.v11i6.3931

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Citations (3)

Predicting death and confirmed cases of coronavirus

Bulletin of Electrical Engineering and Informatics (2022)

Farqad H. Abdulraheem Moatasem Yaseen Al-Ridha Raid Rafi Omar Al-Nima

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.

Backpropagation

Coronavirus

Mean absolute error

10.11591/eei.v11i1.3133

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Citations (3)

Letters Recognition System by Deep Convolutional Neural Network

Duaa Mowafak Hameed Raid Rafi Omar Al-Nima Mustafa Ali Malla Khalil A. K. Al-Maqsood

10.1109/iccsce61582.2024.10696565

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Using Hand-Dorsal Images to Reproduce Face Images by Applying Back propagation and Cascade-Forward Neural Networks

Raid Rafi Omar Al-Nima Farqad H. Abdulraheem Moatasem Yaseen Al-Ridha

This paper concentrates on reproducing face images from hand-dorsal images. This idea is adopted to enhance the biometric system outcomes. That is, best identifications can be presented by providing the face images of people as this can lead to directly recognizing the individuals. Non-linear relationships between hand-dorsal images and face images are designed and implemented. The power of Cascade-Forward Neural Network (CFN) and Back Propagation Neural Network (BPN) are employed to reproduce all face details by utilizing a hand-dorsal image. Both networks recorded interesting results in reproducing the details faces. The CFN performance is equal to 2.8571% and the BPN performance is equal to 6.4286%. Furthermore, the Average Correlation (ACORR) for the BPN which achieved 0.9874, this is lower than the ACORR for the CFN obtained to 0.9940. These performances reported that the CFN has significant ability to recognize people according to their face images.

Backpropagation

10.1109/iceccpce46549.2019.203755

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Citations (13)

Explainable Deep Semantic Segmentation for Flood Inundation Mapping with Class Activation Mapping Techniques

Proceedings of the 14th International Conference on Agents and Artificial Intelligence (2024)

J.E. Sanderson Hua Mao Naruephorn Tengtrairat Raid Rafi Omar Al-Nima Wai Lok Woo

Semantic mapping

10.5220/0012432300003636

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High-Performance Character Recognition System Utilizing Deep Convolutional Neural Networks

NTU Journal of Engineering and Technology (2024)

Duaa Mowafak Hameed Raid Rafi Omar Al-Nima

Accessible reading is still a major challenge for those with visual impairments in our digitally-driven world, particularly for those who are born blind. The creation of a Letter Recognition System (LRS) for the blind is the novel solution to this problem that this research suggests. With the help of this device, blind people can access printed letters according to its reading solution. In order to obtain images of printed letters, this study presents practical hardware that uses a webcam and a manual printing machine. The paper can be moved to acquire the written letters on it. Hence, a large collection of data containing letters known as Printed English Letters-version 2 (PEL2) dataset was gathered for the printed English letters (A–Z). Following acquisition, the input images undergo preparation, segmentation, and resizing. After that, a Deep Convolutional Neural Network (DCNN) is used to recognize letters from them. Ultimately, it is recommended that the identified letter be transformed into letter-to-speech audio to enhance the system's efficacy in assisting blind or visually impaired individuals with reading. In this work, a very high accuracy of 99.70% for letter identification has been calculated and attained.

Identification

Optical character recognition

10.56286/ntujet.v3i4.1086

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Cross-Spectral Iris Matching for Surveillance Applications

Advanced sciences and technologies for security applications (2017)

Mohammed A. M. Abdullah Raid Rafi Omar Al-Nima Satnam Dlay Wai Lok Woo Jonathon A. Chambers

Iris Recognition

IRIS (biosensor)

Normalization

Gabor filter

10.1007/978-3-319-68533-5_5

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Citations (13)

Recognition of Eudiscoaster and Heliodiscoaster using SOM Neural Network

Maǧallaẗ al-rāfidayn li-ʿulūm al-ḥāsibāt wa-al-riyāḍiyyāẗ/Al-Rafidain journal for computer sciences and mathematics (2010)

Raid Rafi Omar Al-Nima

This research is aimed to design an Eudiscoaster and Heliodiscoaster recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the fossils picture for data acquisition. Second: applying neural networks techniques for recognition. The image processing techniques display the steps for getting a very clear image necessary for extracting data from the acquisition of image type (.jpg). This picture contains the fossils. The picture should be enhanced to bring out the pattern. The enhanced picture is segmented into 144 parts, then an average for every part can easily be computed. These values will be used in the neural network for the recognition. For neural network techniques, Self Organization Maps (SOM) neural network was used for clustering. The weights and output values will be stored to be used later in identification. The SOM network succeeded in identification and attained to (False Acceptance Rate = 15% - False Rejection Rate = 15%).

Identification

Neural gas

Self-organizing map

10.33899/csmj.2010.163918

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Interpreting arabic sign alphabet by using the deep learning

AIP conference proceedings (2023)

Ahmed Saeed Ibrahim Al-Obaidi Raid Rafi Omar Al-Nima Tingting Han

10.1063/5.0171428

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Citations (2)