Phone : +964 (750) 459-8083

Birth Date: 1990-10-09

Nationality: Iraq

Address: Grebase

Salar Faisal Noori

Assistant Lecturer

Department of Computer Science

Programming & Software Engineering
Area Interest
Programming Language Java Application Computer Network Web Development Software Engineering Operating System Database System
Teaching Materials
Academic Year 2023 - 2024 First Semester Data Structure I Visual Programming I Web Development I Computer Skill I

Rapporteur of Computer Science Department & ICT Unit Director at Cihan University - Duhok, M.Sc. in Software Engineering



English (Proficient)


Arabic (Proficient)


Kurdish (Proficient)


Turksih (Intermediate)



Programming Language

Programming: C++, C#, OOP, HTML, CSS, PHP, JAVA Script, Java Application, Visual Basic, Form Application and MS-DOS Commands.


Computer Skill

Data Management, Software, Hardware Maintenance and Social Media.




Reading Books Related to Programming Language & Computer.



Playing Football is the best sport for me.



Too Discover new things.


2015 – 2017


Software Engineering

Near East University

2011 – 2014


Computer Science

Nawroz University

Academic Title


Assistant Lecturer



How to Make a Quiz for your students using Moodle Platform.

Peshmarga Hall

I Explain how to make a quiz and how to choose the questions like ... Essay, Multiple Choice, Short Answer, also how to reduce students cheating and etc. Using Moodle platform.



Quiz Sample in Moodle Platform

Peshmarga Hall

i have handled workshop on Moodle Paltform

Publication Journal


A framework enhancement method of deep web data extraction

Materials Today: Proceedings :

The solutions suggested for data extraction issue depends on the HTML DOM trees and response pages’ tags being analyzed. Although these solutions can achieve excellent outcomes, they are strongly dependent on HTML specifics. Therefore, to solve this issue this paper proposes a framework of two stages, for proficiently disclosure profound web data. The primary organizes, the proposed system performs “normal crawling” to get significant pages related to the user’s text query. To choose up significant web pages, a strategy is proposed based on the moved forward weighting work (ITF-IDF) is received by the crawler. In the second stage, “data region extraction “is performed to obtain data records. The proposed data extractor exploits the visual features of blocks to extract visual blocks. The strategy is proposed to cluster the visual blocks in a comparable format based on format tree and appearance likeness. Within the cluster with the most elevated weight, the visual blocks are chosen to be extricated as information records. The test comes about the outline that the system proposed is superior to past information extraction works.


Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation

Materials Today: Proceedings : (Issue : No.2) (Volume : No.1)

The occurrence of defect over the soft tissues and nervous system is gradually increasing where Magnetic Resonance Imaging (MRI) is the most preferred method for performing the examination. The brain tumor MR image segmentation performs functionalities like image reconstruction of affected (diseased tissues) and qualitative analysis of infected and normal tissues. The image segmentation accuracy with the physician’s perspective relies over the shape, size, and location of lesions tissues, appropriate diagnostic strategies, and disease determination. The outcomes of this investigation rely over Multi-Perspective Scaling Convolutional Neural Networks (MPS-CNN) model for segmenting brain tumors more effectually and accurately. The multi-scale inputs are given to the proposed CNN model to overcome the necessity to select the appropriate input scale based on the tumor size, neighborhood tumor analysis based on scaled images, and adoption towards various tumor sizes. Therefore, the segmentation accuracy can be increased based on the input multi-scale brain tumor images. Also, the faster segmentation with multi-scaling process accelerates the speed of ensuring real-time segmentation process. This scaling process can effectively segment the brain images in the MRI which enhances the generalization process. It is utilized for predicting the brain lesion tissue of MRI. The simulation is carried out in MATLAB environment. The anticipated MPS-CNN is compared with prevailing approaches like CNN, FCN, U-Net, SegNet, Deep V3, and Deep FCN. And the MPS-CNN shows better trade-off in contrary to other approaches.