Assistant Lecturer

Associate Dean

Technical College of Health/Shekhan

Duhok Polytechnic University

To coordinate the work of the administrative and technical staff in all the college’s divisions according to the work requirements of the college and distribute them among those divisions in a manner that guarantees the college’s interest and need.

Rapporteur of Department Medical Lab. Technology department

Technical College of Health/Shekhan

Duhok Polytechnic University

• I became member in the Graduate Studies Committee in the Technical College of Health/ Shekhan. • I became member in the undergraduate Studies Committee in the Technical College of Health/ Shekhan. • I became head in the undergraduate Studies Committee in the Technical College of Health/ Shekhan.

Rapporteur of Department of Computer System and Information Technology

Shekhan Technical Institute

Duhok Polytechnic University

• I worked as coordinator in the department with the quality assurance • I worked as a member of the Committee zankoline to record students in universities and institutes in the Kurdistan Region for two years.

Computer center unit manager

Shekhan Technical Institute

Duhok Polytechnic University

Responsible for computer center unit to prepare and organize laboratories in department, also all computer in units of institute.

Excellent use of computer application and Microsoft Office. Programming the micro-controller chip.

Computer programming language such Visual basic, C++, C#, Matlab, and python

Regularly play with friends and in competitions

I like traveling and long walks on the river banks.

Based on each one’s specialization (general or specialty). E.g. my current research my current research around signal and image processing, IoT, and artificial intelligent.

Kurdistan Engineering Union

Tech. Computer Eng.

Duhok

Iraqi Engineering Union

Tech. Computer Eng.

Mosul

MODIFY CONVOLUTIONAL NEURAL NETWORK MODEL FOR THE DIAGNOSIS OF MULTI-CLASSES LUNG DISEASES COVID-19 AND PNEUMONIA BASED ON X-RAY IMAGES

Journal of Duhok University (Issue: 1) (Volume: 25)

COVID-19 is a new virus able to infect both the upper and lower respiratory lobes of ling. There is a daily increase in cases and deaths in the global epidemic. A number of the test kits now in use are sluggish and in short supply; hence RT-PCR testing is the most appropriate option. To avoid a potentially fatal outcome, early detection of COVID-19 is essential. According to numerous studies, visual markers (abnormalities) on a patient's Chest X-Ray imaging can be a valuable characteristic of a COVID-19 patient, which can be exploited to discover the virus. In this research, Convolutional Neural Networks (CNNs) are being proposed to detect the Covid-19 disease based on X-rays images. The suggested model is based on modified VGG16 architecture for deep feature extraction. The fine-tuning approach with end-to-end training is also utilised in the aforementioned deep CNN models. The suggested model has been trained and evaluated on the dataset contains 7,245 X-ray images, comprising 1,420 Covid-19 cases, 4,167 bacterial cases of Pneumonia, and 1,658 normal cases. The model is evaluated using metrics such as accuracy, precision, recall, and f1-score. The proposed model enhanced the accuracy by using less trainable parameters (weights) than the Vgg16 model. Thus, the time needed for training and testing will be less. In addition, it achieved a multiclass micro-average of 97% precision, 97% recall, 97% f1-score, and 97% classification accuracy. The findings obtained show that the proposed strategy outperforms several currently used methods. This model appears to be convenient and forceful for multiclass classification.

Classification of COVID-19 Cases from X-Ray Images Based on a Modified VGG-16 Model.

Traitement du Signal (Issue: 1) (Volume: 19)

COVID-19 is considered one of the most deadly pandemics by the World Health Organization and has claimed the lives of millions around the world. Mechanisms for early diagnosis and detection of this rapidly spreading disease are necessary to save lives. However, the increase in COVID-19 cases requires not relying on traditional means of detecting diseases due to these tests’ limitations and high costs. One diagnostic technique for COVID-19 is X-rays and CT scans. For accurate and highly efficient diagnosis, computer-aided diagnosis is required. In this research, we suggest a convolutional neural network for chest x-ray images categorisation into two classes of infection: COVID-19 and normal. The suggested model uses an upgraded model based on the VGG-16 architecture that has been trained end-to-end on a dataset composed of X-ray images obtained from two different public data repositories, which include 1,320 and 1,578 cases in the COVID-19 and normal classes, respectively. This suggested model was trained and evaluated on the provided dataset and showed that our proposed model showed improved performance in the matter of overall accuracy, recall, precision, and F1-score at 99.54%, 99.5%, 99.5%, and 99.5%, respectively. The system’s significance is supported because it has greater accuracy than other contemporary deep learning methods in the literature on COVID-19 identification.

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

Asian Journal of Research in Computer Science (Issue: 2) (Volume: 11)

Prediction is vital in our daily lives, as it is used in various ways, such as learning, adapting, predicting, and classifying. The prediction of parameters capacity of RNNs is very high; it provides more accurate results than the conventional statistical methods for prediction. The impact of a hierarchy of recurrent neural networks on Predicting process is studied in this paper. A recurrent network takes the hidden state of the previous layer as input and generates as output the hidden state of the current layer. Some of deep Learning algorithms can be utilized in as prediction tools in video analysis, musical information retrieval and time series applications. Recurrent networks may process examples simultaneously, maintaining a state or memory that recreates an arbitrarily long background window. Long Short-Term Memory (LSTM) and Bidirectional RNN (BRNN) are examples of recurrent networks. This paper aims to give a comprehensive assessment of predictions based on RNN. Additionally, each paper presents all relevant facts, such as dataset, method, architecture, and the accuracy of the predictions they deliver.

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Asian Journal of Research in Computer Science (Issue: 2) (Volume: 10)

A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

Swarm Intelligence-Based Feature Selection for Multi-Label Classification: A Review

Asian Journal of Research in Computer Science (Issue: 4) (Volume: 9)

Multi-label classification is the process of specifying more than one class label for each instance. The high-dimensional data in various multi-label classification tasks have a direct impact on reducing the e􀀀ciency of traditional multi-label classifiers. To tackle this problem, feature selection is used as an effective approach to retain relevant features and eliminating redundant ones to reduce dimensionality. Multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining and bioinformatics. Moreover, in recent years researchers have focused on applying swarm intelligence methods in selecting prominent features of multi-label data. After reviewing various researches, it seems there are no researches that provide a review of swarm intelligence-based methods for multi-label feature selection. Thus, in this study, a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multilabel feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works and at the end of this survey, some challenges, issues and open problems of this field are introduced to be considered by researchers in future.

COVID-19 Diagnosis from Chest X-ray Images Using Deep Learning Approach

IEEE Xplore

Coronavirus (COVID-19) disease is an infectious disease caused by the newly and deadly pneumonia type identified Coronavirus2 (SARS-CoV-2). A real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the main method and has been regarded as the gold standard for diagnosing the COVID-19. Strict requirements and the limited supply of RT-PCR kits for the laboratory environment leads to delay in the accurate diagnosis of patients in addition to the test takes 4-6 hours to obtain the results. To tackle this problem, radiological images such as chest X-rays and CT scan could be the answer to test the COVID-19 infection rapidly and more efficiently. In this paper, an efficient proposed Convolution Neural Network (CNN) architecture model for COVID-19 detection based on chest X-ray images is presented. The proposed model is developed to provide accurate detection for binary classification (Normal vs. COVID-19), three class classification (Normal vs. COVID-19 vs. Pneumonia), and four class classification (Normal vs. COVID-19 vs. Pneumonia vs. Tuberculosis (TB)). Our proposed model produced an overall testing accuracy of 99.7%, 95.02%, and 94.53% for binary, three, and four class classifications, respectively. A comparison is made between this work and others shows the superior of this work over the others.

Skin Lesions Classification Using Deep Learning Techniques: Review

Asian Journal of Research in Computer Science (Issue: 1) (Volume: 9)

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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

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

The software development in different fields leads to increase the requirements for effective, efficient and complicated software. Due to the huge amounts of software requirements, it is possible some errors to occur in the certain part 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 case 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.

Real-Time Implementation of Greenhouse Monitoring System Based on Wireless Sensor Network

International Journal of Recent Technology and Engineering (IJRTE) (Issue: Issue-2S2) (Volume: Volume-8)

Abstract: The climate change has brought about unpredictable weather conditions that have resulted in the global food shortage being experienced. This issue can be solved by greenhouses, they play a main role in increasing the crop yield per unit area and represent the suitable environment for off-corps yields. Managing and continuous monitoring the green house environment can be done using a wired sensor network, but the high cost, wiring complexity, fixed sensor locations and the restricted distances are the big problems of this type of a networking. To solve these problems, we implemented a real time embedded system using Wireless Sensor Network (WSN) based on ZigBee technology to control and monitor the environmental of greenhouses. The WSN can be adopted as the best solution to apply in greenhouse because of its good properties, long distances, low-cost, low power consumption, high security and high reliability. The constructed system is implemented based on simple components, ATMEGA328P microcontroller and ZigBee are represented the kernel of sensor node, collect data from various sensors and present them to a coordinating station where data can be stored and processed, then actuators will be operate depending on the processed data. The captured data will be displayed for monitoring in a real time manner. The monitor system was developed using GSM technology. The simulation results show that the system is more efficient in the manpower saving and raising the economic value of products. Furthermore, the developed system is simple, and easily installable.

REAL-TIME POWER MEASUREMENTS IN SMART BUILDING MONITORING SYSTEM

Journal of University of Duhok (Issue: 2017: Pure and Engineering Sciences (Special Issue of Conference)) (Volume: 20)

The concept of a smart building or a home automation system is often characterized by the ability to control and monitor various household appliances to provide improved convenience, energy efficiency and security. In this paper a smart building real-time power monitoring system is proposed. The system targeted at enhanced safety and user awareness about power consumption with reduced cost. The consumed power can be instantaneously measured. The system can be programmed such in the case increasing power consumption, and start to turn off some insignificant appliances. Consequently, power consumption is reduced down and the cost is reduced. The general-purpose microcontroller (Arduino) available in market with low price. An interpolation technique is proposed to reduce the time of data acquisition. The experimental power load measurements achieved by proposed system are compared in the Lab with the precision and commercial power meters. The comparison shows good results, and the typical time required to measure the real power load and power factor is 42.8 ms for one cycle measurement with 192 samples of data acquisition. The maximum error in measurements are 6% W for real power and 4% for PF.

1st International Conference on Computer Science and Software Engineering (CSASE 2020)

Iraq, Duhok As Guest

first International Conference on Computer Science and Software Engineering (CSASE 2020). This conference is organized by the University of Duhok and technically is sponsored by IEEE, Iraq section.

3rd International Health and Medical Sciences Conference "Toward the Advanced Medical Research"

Iraq, Sulaimani As Guest

Participate in the 3rd International Health and Medical Sciences Conference "Toward the Advanced Medical Research", at Faruk Medical City, Sulaimani, Kurdistan Region of Iraq

2nd International Health and Medical Sciences Conference

Iraq, Sulaimani As Guest

Participate in the 2nd International Health and Medical Sciences Conference, The Faruk Medical City, Sulaimani: From Innovations to Clinical Trials.

2nd International Conference of the College of Engineering

Iraq, Duhok As Presenter

Participate in the 2nd International Conference of the College of Engineering, University of Duhok: Recent Innovations in Engineering.

Research Methodology course

Technical College Akre As Guest

Research Methodology course Technical College Akre Informatics

Discrete Wavelet Transform and application

Medical Laboratory Technology, Technical College of Health/Shekhan As Presenter

Multi Vitamin Supplementation During Pregnancy

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Taenia spp

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Normal microbial flora

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Infection control

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Polycythemia

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Learning in small groups in medical fields

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Generation of viral vaccine

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

miRNA. diagnostic and therapeutic potentials

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Typing Of Bacteria in the Genommic Era

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

Breast Cancer

Medical Laboratory Technology, Technical College of Health/Shekhan As Attend

IELTS Preparation

University of Duhok, National

IELTS Preparation College of Education, University of Duhok

Faculty Academic Capacity Building

University of Duhok, National

Faculty Academic Capacity Building College of Education, University of Duhok

English Course

University of Duhok, National

English Course for three months at the Cultural Center