M.Sc

Sep 1, 2004 - Sep 1, 2006

Master Degree in Computer Engineering
Special Major: Neural Networking

B.Sc

Jan 9, 1998 - Jul 1, 2002

Computer Engineering

Teaching Microprocessor1, Advanced Computer Technique, Digital Signal Processing, Optoelectronics, Engineering Analysis, Distributive Systems, English [Lecturer]

Jan 28, 2023 - Present

-Teaching theoretical and practical subjects for the third and fourth grades since 2006 -and till now.
-supervising on graduations projects 2008 - Present
- Member of Computer Engineering Dept. Scientific Committee 2013 - 2020
- Member of Preparing Questions for the Competition Examination for Higher Studies Applicants Committee 2007 - 2019
-Being a member of the graduation projects discussion committee 2017- Present
-Being a member of summer training discussion committee 2008 - Present
- Being a member of the Educational Guidance for the Fourth-grade students 2024- Present

Speech recognition and retrieving using fuzzy logic system

Nov 11, 2009

Journal Tikrit Journal of Pure Science

publisher Tikrit University

DOI Tikrit Journal of Pure Science

Issue 3

Volume 15

Speech Analysis is one of the most interesting fields in Digital Signal Processing in which many researches have been done on it based on different materials tools and scientific programs to produce an analysis that start from speech production, processing, coding and recognition, Chester, FJ. Taylor and M. Doyle were the first to apply the analysis of speech signal [1]. In this research, females and males speech samples of the word,'Close', were used to build a system in Neural Network and Fuzzy Logic to recognize the male from female speech voice and compared between the results of the two systems, then the system of the fuzzy logic was developed based on three features of the speaker voice which are energy value of the signal, power spectrum of the signal and vowel sound “O” in the word close in the speech samples to increase its ability in recognizing an individual speaker and to increase system security against intruders by making the system recognizes the speech of a one person giving a voice acceptance authority to that person and make an access denied to others to prevent accessing the system. The system shows good results during testing operation using samples of one person against others female's and male's samples.

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SIGNATURE RECOGNITION USING SPECIAL GENETIC NEURAL NETWORKS

Feb 10, 2010

Journal Gulf University Journal

publisher Gulf University -Kingdom of Bahrain

DOI 10.13140/RG.2.2.26158.77127

Issue 1

Volume 2

This paper aims to combine the statistical techniques and the Neural Networks (NNs) to map the image of the signature with that stored in the architecture of the networks. The procedure suggested here is not to enter the signature image data directly to update the NN, but to perform some statistical operations on the signature image data before feeding them to the multi-layer Neural Network (NN). These statistical operations include the normalization, distance determination between predivided objects, linkage between these objects, and finally hierarchical image data clustering operation. Hence, a multi-layer is used to represent these statistical data as a model. This procedure is repeated for different target signature images that positioned at different rotating angles. Moreover, new Feed Forward Neural Network (FFNN) structures that have mutually connected hidden nodes are proposed, and were presented also

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Extraction and Recognition of Color Feature in true Color Images Using Neural Network Based on Colored Histogram Technique

Dec 1, 2012

Journal AL-Rafidain Journal of Computer Sciences and Mathematics

publisher Mosul University

DOI 10.33899/csmj.2012.163709

Issue 2

Volume 9

In this research, a neural network using backpropagation (BPNN) algorithm was trained and learned to work as the cone cells in human eyes to recognize the three fundamental cells’ colors and hues, as the neural network showed good results in training and testing the color feature it was trained and learned again to recognize two nature scenes images; Red sunset and Blue sky images where both scenes images contain color interaction and different hues such as red-orange and blue-violet. The recognition process was based on color histogram technique in colored images which is a representation of the distribution of colors in an image by counting the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space, all possible colors in the image. The importance of this research is based on developing the ability of (BPNN) in images ‘objects recognition based on color feature that is very important feature in artificial intelligence and colored image processing fields from developing the systems of alarms robots in fire recognition, medical digenesis of tumors, certain pattern’s recognition in different segments of an image, face and eyes’ iris recognition as a part of security systems, it helps solve the problem of limitation of recognition process in neural networks in many fields.

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Difficulties Facing the Students of the Third Grade of Computer Engineering Technology in the Subject of Engineering Analysis

Sep 10, 2019

Journal College Of Basic Education Researches Journal

publisher Mosul University

DOI https://www.iasj.net/iasj/article/175551

Issue 4

Volume 15

The current research aims to identify the difficulties that face the third class’s students at computer engineering department studying the course of engineering analysis considering certain variables. The sample that was taken consisted of (31) third class’s students at the technical engineering college-Mosul/computer engineering department for the studying year (2018-2019). To achieve that aim, the two researchers had built a scale to identify those difficulties which consisted from 20-paragraphs according to three-fields considering (the curriculum and syllabus of the material, difficulties related to lecturer, difficulties related to students) taking into consideration ten paragraphs for the first field, five paragraphs for the two and the third fields followed by three alternatives that marked with consistency and integrity. The two researchers applied their scale on the main research’s sample and after collecting the data and analyzed it statistically, the results were as the following: Most of the students have difficulty in studying the engineering analysis course and the syllabus and its use in different fields. Variation in the point of views between the third class’s students of the computer engineering department in facing those difficulties considering sex variable (males and females).

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A review on the relationship between computer engineering, discrete-math and graph theory

Oct 25, 2020

Journal Tikrit Journal of Engineering Sciences

publisher Mosul University

DOI 10.25130/tjes.27.3.05

Issue 3

Volume 27

This review is based on understanding the main concept between computer engineering and mathematics based on two of their most important fields, the discrete-math and graph theory. and answering the question that was asked by many students over the years of working in the university, about the necessity of studying mathematics while majoring computer engineering. Most of the students face the same problem over years for not having the vision to connect between studying materials of their specialization and general ones, in particular between studying discrete-math engineering as in Engineering analysis, and discrete-math as in the Digital signal Processing (DSP), and between algebraic mathematics. Moreover, they do not understand the main idea of the transition between different time or frequency domains, by converting the work in real-time domain systems to work in discrete–time or frequency domain systems. And they do ignore the importance of studying graph theory, in which recent researches have proved the powerful of using graphs in learning tasks, developing an important field of computer engineering, the machine learning, where the standard neural networks (SNNs) have been developed to graph neural networks (GNNs). A figure was concluded at the end of the review to brief the importance of discrete-math developing the relationship between computer engineering in general and graph theory’s role in developing machine learning in particular.

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Enhancing drivers’ attention by a smart binary matching machine to avoid accidents

Sep 1, 2023

Journal Al-Rafidain Engineering Journal

publisher Mosul University

DOI 10.33899/rengj.2023.138781.1240

Issue 2

Volume 28

Stress and sudden difficult situations have raised the risks of accidents down the roads. The drivers’ attention might be distracted out in seconds under unexpected circumstances, which could take place due to bad weather, vision problems, fatigue for long driving hours, damaged or broken Traffic light , and even children's noise inside the car. In this paper, I proposed to develop a special colourful Deep Back Propagation Neural Network to enhance drivers’ attention by observing different traffic light cases using a suggested smart binary matching machine system in Python. The smart machine system will analyse and identify the real Traffic light from art signs, broken or damaged ones; in addition to pedestrian signs based on a Database symbols for each case, which have taken the basic Traffic light and signs, and developed them to damaged cases or unreal one, before making the right decision by the learned network, then send an enhanced feedback signal to the driver. The algorithm consisted of accurate image processing steps, with two long stages of full contents features extraction vectors to be handled by Red-Yellow-Green Shallow and Deep Back Propagation Neural Networks (SBPNN) and (DBPNN) for each complex case. As a result, the algorithm rated a high accuracy of 100%, which is the most important factor to maintain safety, recoding the true label output as 1-value, with a predicated tested ouput 1.0-value. The suggested system does not replace the driver's one decision, yet it is an enhancing backup classification and recognition system before things move out of control. The feedback signal calculated based on reducing …

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