Bachelor of Computer Science and Mathematics

Sep 1, 2006 - Jun 30, 2010

Bachelor of Computer Science and Mathematics from Mosul University / Iraq

Master of Computer Science and Mathematics

Sep 1, 2010 - Feb 18, 2013

Master of Computer Science and Mathematics from Mosul university / Iraq

PhD in Computer Science and Mathematics

Sep 2, 2018 - Sep 19, 2023

PhD in Computer Science and Mathematics from Mosul University / Iraq

شؤون المواطنين [وحدة شؤون المواطنين]

Dec 28, 2016 - Jan 27, 2017

قسم الشؤون العلمية [مسؤول شعبة الدراسات العليا]

Feb 2, 2017 - Apr 12, 2017

مسؤول شعبة الدراسات العليا / قسم الشؤون العلمية

قسم الاعلام والعلاقات العامة [مسؤول شعبة العلاقات العامة]

Apr 13, 2017 - Oct 18, 2018

مركز الحاسبة الالكترونية [شعبة صناعة البرمجيات]

Oct 19, 2018 - Oct 9, 2022

قسم الدراسات والتخطيط [مدير قسم الدراسات والتخطيط]

Oct 10, 2022 - Dec 31, 2023

مركز الحاسبة الالكترونية [شعبة صناعة البرمجيات]

Jan 2, 2024 - Oct 2, 2024

الكلية التقنية الزراعية الموصل [معاون العميد للشؤون العلمية والطلبة]

Oct 10, 2024 - Present

Computation Offloading in the Internet of Connected Vehicles: A Systematic Literature Survey

Mar 8, 2021

Journal Journal of Physics: Conference Series

publisher IOP Publishing Ltd.

DOI 10.1088/1742-6596/1818/1/012122

Issue 1

Volume 1818

Nowadays, there is a rapid development in vehicles world. Vehicles are equipped with smart systems as well as infotainment applications. But such systems consume vehicles' computation or storage capacity. However, when the vehicle encounters a computation and/storage hungery applications or near real time applications that need high Quality of experience (QoE), it must offload it, either partially or entirely, to a more powerful and resourceful entity. At the beginnings this entity was a remote cloud. Although clouds are powerful in terms of computation and storage capacities, the process of task offloading to a remote cloud consumes the network bandwidth, which is not suitable to delay sensitive applications. As a solution, researchers propose to use cloudlets as third entity closer to the network edge. This will make the offloading much faster, but unfortunately due to the fact that cloudlets less computation and storage capacity than clouds, offloading will cause resource starvation. These factors motivate the appearance of Vehicular Cloud Computing (VCC). VCC proposes collecting the on-board units of multiple vehicles to form an on-ground cloud. This allows vehicles to offload their computational task to other vehicles in the vicinity. In this paper, we first provide a summery on concepts that are related to edge computing and task offloading process, and then we review a set of papers that use different approaches to execute computation offloading and scheduling. © Published under licence by IOP Publishing Ltd.

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Biometric identity Authentication System Using Hand Geometry Measurements

Mar 2, 2021

Journal Journal of Physics: Conference Series

publisher IOP Publishing Ltd.

DOI 10.1088/1742-6596/1804/1/012144

Issue 1

Volume 1804

In recent years hand geometric dependent biometric system has shown to be the quite acceptable biometric trait and suitable for security applications. It has been recognized as an effective means of authenticating identity in a variety of commercial applications as a result of better hardware and improved algorithms. This paper purpose a hand recognition system that extract 21 features for the right hand to identify and authorize persons. The system has two main parts, the first contain the data collection, explains the basic pre-processing required and how hand geometry characteristics like fingers length, width, coordinates of the base of the fingers, and palm width are extracted to derive the features used for discrimination, While the second part include the training and testing of three artificial neural networks to perform the recognition. After features extraction, the system uses three kinds of artificial neural networks in performing the recognition process, which are feed forward back propagation NN, Elman NN, and the cascade forward neural network NN. The proposed system shows that the Recognition Rate RR for the neural networks after testing were 95%, 92%, 88% respectively. © Published under licence by IOP Publishing Ltd.

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Improving face recognition by artificial neural network using principal component analysis

Dec 12, 2020

Journal Telkomnika (Telecommunication Computing Electronics and Control)

publisher Universitas Ahmad Dahlan

DOI 10.12928/TELKOMNIKA.v18i6.16335

Issue 6

Volume 18

The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person’s faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third and fourth models were trained using the same features but using Elman neural network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33, 98.80) with (40, 50) features respectively, while Elman gives (98.33, 95.14) for with (40, 50) features respectively. © 2020. All rights reserved.

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