Education
Oct, 2020
MSc

Information System Engineering

Erbil Polytechnic University

Jul, 2002
BSc

Computer Engineering Techniques

Northern Technical University

Title
Jun, 2021
Assistant Lecturer
Publication Journal
Aug, 2021
Security Approaches For Integrated Enterprise Systems Performance: A Review

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH (Issue: 12) (Volume: 8)

Advancements in Information and Communications Technology (ICT) have become the communication medium for virtually every small or large industry around the world. This technology changed the ways of doing businesses and has led to the invention of a new concept called Electronic Business (E-Business). E-Business involves various activities for any business such as ordering, transacting, customer servicing, delivering, and paying. This new type of business consists of many advantages comparing with the traditional form of business, but on the other hand, it faces real challenges related to the approaches used to keep the data secure. The sharing of sensitive data for any enterprise system requires designing a security management framework in order to enable controlling access to sensitive information. In this paper, the last efforts of researchers proposed in the security field of the E-Business were systematically reviewed. Furthermore, the security approaches, techniques, and security frameworks have been discussed.

May, 2021
Detection of Diabetic Retinopathy Based on Convolutional Neural Networks : A Review

Asian Journal of Research in Computer Science (Issue: 3) (Volume: 8)

A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, reviews the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which is highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.

May, 2021
COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

Qubahan Academic Journal (Issue: 2) (Volume: 1)

In December 2019, SARS-CoV-2 caused coronavirus disease (COVID-19) distributed to all countries, infecting thousands of people and causing deaths. COVID-19 induces mild sickness in most cases, although it may render some people very ill. Therefore, vaccines are in various phases of clinical progress, and some of them are being approved for national use. The current state reveals that there is a critical need for a quick and timely solution to the Covid-19 vaccine development. Non-clinical methods such as data mining and machine learning techniques may help do this. This study will focus on the COVID-19 World Vaccination Progress using Machine learning classification Algorithms. The findings of the paper show which algorithm is better for a given dataset. Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. The comparison is based on accuracy and performance period, and it was discovered that the Decision Tree outperforms other algorithms in terms of time and accuracy.

Feb, 2021
The Impact of Test Case Generation Methods on the Software Performance: A Review

International Journal of Science and Business (Issue: 6) (Volume: 5)

Software development in different fields leads to an increase in the requirements for effective, efficient, and complicated software. Due to the huge amounts of software requirements, it is possible for some errors to occur in certain parts of the programs and this means a real challenge for the software producer. The need for an effective test system is necessary for designing reliable programs and avoiding the errors that may appear during the software product. In this review, many techniques are discussed for the process of generating test cases which are a group of conditions that determine whether the designed programs are able to satisfy the user’s requirements or not. Fuzzy logic utilizes an operational profile in the process of allocating test cases to improve the software quality, as well as the design of fault propagation path to predicts the software defects during the test operation, also the automatic generation for PLC test cases that produce a new track through the program code in order to minimize the test cases needed for large size program.

Jul, 2020
Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application

JOURNAL OF APPLIED SCIENCE AND TECHNOLOGY TRENDS (Issue: 3) (Volume: 1)

Nowadays, heart diseases are considered to be the primary reason for unexpected deaths. Thus, various medical devices have been developed by engineers to diagnose and scrutinize various diseases. Healthcare has become one of the most substantial issues for both individuals and government due to brisk growth in the human population and medical expenditure. Many patients suffer from heart problems causing some critical threats to their life, therefore they need continuous monitoring by a traditional monitoring system such as Electrocardiographic (ECG) which is the most important technique used in measuring the electrical activity of the heart, this technique is available only in the hospital which is very costly and far for remote patients. The development of wireless technologies enables to build of a network of connected devices via the internet. The proposed ECG monitoring system consists of an AD8382 ECG sensor to read patient's data, Arduino Uno, ESP8266 Wi-Fi module, and IoT Blynk application. The implementation of the proposed ECG healthcare system enables the doctor to monitor the patient's remotely using the IoT Blynk application installed on his smartphone for processing and visualizing the patient's ECG signal. The monitoring process can be done at any time and anywhere without the need for the hospital.

May, 2020
A Modified Convolutional Neural Networks Model for Medical Image Segmentation

Test Engineering and Management (Issue: May - June 2020) (Volume: 83)

Medical image segmentation is a crucial step in developing computer-Aided Diagnosis (CAD), which supports the physician to adopt a suitable procedure for the clinical case. Lung cancer, Tuberculosis, and Pneumonia are the most dangerous threats that attack the human lungs and result in high global mortality. Precise lung segmentation from X-ray and Computed Tomography (CT) is a challenge due to the irregular shape and high ambiguity in lung edges with the background. This paper aims to develop a sufficient approach with robust lung segmentation, less time, and minimum processing cost. U-net is a deep convolutional neural network architecture; it is mostly designed for medical image segmentation. A standard kernels’ number at each convolutional layer in U-net is utilized to abstract the wealthy data from the medical images. In this paper, we proposed a modified U-net model based on the reduction of kernels’ numbers in each layer. The modification involves employing only 25% of the standard kernel’s number to extract ROI from the chest images. We compare the standard and modified U-net segmentation results using 263 X-ray images from the Shenzhen dataset and 269 CT images from the LUNA16 dataset. The experimental results indicate the contribution of our modified U-net to improve the global accuracy, Jaccard, and Dice metrics of the standard U-net. Besides, the modified U-net takes about 30% of the standard U-net time to learn the network and build the proposed segmentation model. The proposed U-net architecture with a minimized kernels’ number indicates the possibility to increase the lung segmentation precision in terms of some performance metrics and decrease the network learning time in terms of mean iteration time.