Bachelor

Oct 1, 1990 - Jul 14, 1994

Electrical and Electronic Engineering

Master

Oct 1, 1999 - Jul 9, 2003

Communication Engineering

Ph.D

Oct 1, 2010 - Jun 8, 2016

Computer Networks Engineering

computer [Head of Computer Techniques Engineering Department]

Sep 9, 2017 - Nov 3, 2019

computer [Head of Computer Techniques Engineering]

Mar 21, 2021 - Sep 1, 2023

computer [Head of Computer Techniques Engineering]

Jul 12, 2023 - Jul 18, 2024

computer [Head of Computer Techniques Engineering]

Apr 30, 2025 - Present

Detection and Classification of the Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)

Apr 23, 2022

Journal Iraqi Journal of Science

publisher University of Baghdad

DOI DOI: 10.24996/ijs.2022.63.11.40

Issue 11

Volume 63

Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA using transfer learning with fine-tuning. This paper proposed three versions of pre-trained networks (VGG16, VGG19, and ResNet50) for handling the classification task. According to the classification results, The proposed model ResNet50 outperformed the other models a validation accuracy of 91.51% has been obtained.

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Recognition of multifont English electronic prescribing based on convolution neural network algorithm

Sep 14, 2020

Journal Bio-Algorithms and Med-Systems

publisher De Gruyter

DOI https://doi.org/10.1515/bams-2020-0021

Issue 3

Volume 16

The printed character recognition is an efficient and automatic method for inputting information to a computer nowadays that is used to translate the printed or handwritten images into an editable and readable text file. This paper aims to recognize a multifont and multisize of the English language printed word for a smart pharmacy purpose. The recognition system has been based on a convolution neural network (CNN) approach where line, word, and character are separately corrected, and then each of the separated characters is fed into the CNN algorithm for recognition purposes. The OpenCV open-source library has been used for preprocessing, which can segment English characters accurately and efficiently, and for recognition, the Keras library with the backend of TensorFlow has been used. The training and testing data sets have been designed to include 23 different fonts with six different sizes. The CNN algorithm achieves the highest accuracy of 96.6% comparing to the other state-of-the-art machine learning methods. The higher classification accuracy of the CNN approach shows that this type of algorithm is ideal for the English language printed word recognition. The highest error rate after testing the system using English electronic prescribing written with all proposed font-types is 0.23% in Georgia font.

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Design and implementation of single bit error correction linear block code system based on FPGA

Aug 1, 2019

Journal TELKOMNIKA

publisher TELKOMNIKA

DOI DOI: 10.12928/TELKOMNIKA.v17i4.12033

Issue 4

Volume 17

Linear block code (LBC) is an error detection and correction code that is widely used in communication systems. In this paper a special type of LBC called Hamming code was implemented and debugged using FPGA kit with integrated software environments ISE for simulation and tests the results of the hardware system. The implemented system has the ability to correct single bit error and detect two bits error. The data segments length was considered to give high reliability to the system and make an aggregation between the speed of processing and the hardware ability to be implemented. An adaptive length of input data has been consider, up to 248 bits of information can be handled using Spartan 3E500 with 43% as a maximum slices utilization. Input/output data buses in FPGA have been customized to meet the requirements where 34% of input/output resources have been used as maximum ratio. The overall hardware design can be considerable to give an optimum hardware size for the suitable information rate.

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Biometric Based Chaotic Cryptography for Multiuser Security System

Sep 12, 2024 - Sep 13, 2024

Publisher Springer

DOI https://doi.org/10.1007/978-3-032-01948-6_8

Country Turkey

Location Ankara

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Multibiometric System for Iris Recognition Based Convolutional Neural Network and Transfer Learning

Dec 21, 2020 - Dec 22, 2020

Publisher OIP

DOI doi:10.1088/1757-899X/1105/1/012032

Country Iraq

Location Baghdad

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