نجاة عبدالله الصيد رحيل
عضو هيئة تدريس
عضو هيئة تدريس قار
المؤهل العلمي: ماجستير
الدرجة العلمية: مساعد محاضر
التخصص: هندسة كمبيوتر - حاسب الي
قسم الحاسوب - كلية الآداب و العلوم - بدر
حول نجاة
CURRICULUM VITAE Objective; To work and study in competitive environment With utmost sincerity and Dedication. Educational qualifications; 1- Bachelor degree in computer science from The Bader College of Arts &Science -Al Zintan University, Libya (2006). 2- Master degree in computer engineering from The Cankaya University , Turky(2015). PERSONAL PROFILE Name ; NAGAT ABDALLA ESIAD Date of Birth ; 14-May-1985 Sex ; Female Marital Status ; Married Nationality ; Libyan Passport number ; C9P1722J E-Mail, I. D ; njatrhel@yahoo.com PHONE NO ; 00218 – 092 -7138647 ; 00218 – 094 –5448123 Languages known ; Arabic (Mother tongue) , English ( Good ), DETAILS OF EMPLOYMENT EXPERIENCE S NO NAME OF THE INSTITUTION DESIGNATION PERIOD 1 Computer Department-Bader College of Arts &Science -Al Zintan University Working in the lab as a demonstrator supervising the student in programming in C & C++ Language, Pascal Language and networks. (2007 -2012) 2 Computer Department-Bader College of Arts &Science -Al Zintan University Working as a lecture assistant giving the student lectures in lots of curses such as The basic of programming I-II by java ,C++ Language ,Mat lab Language ,Compiler Design ,Operating System , Electronic Education ,Data Security . (2015 -2022) Research papers UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results. published at ECCV 2020 European Conference on Computer Vision Declaration I hereby declare that all statements made here in above are true, complete and correct and are not false or misleading. I understand that in the event of any information so furnished being found false or incorrect or misleading, the organization/institution shall be at liberty to dismiss me from its services (if selected) besides proceeding against me for giving false and incorrect statements, under the appropriate law. Yours sincerely (NAGAT ABDALLA ESIAD) REFERENCES 1) DR.REBIE.M. JALASH ASSISTANT PROFESSOR & DEAN BADER COLLEGE OF ARTS &SCIENCE Zintan UNIVERSITY Al Zintan LIBYA. E-MAIL,I.D:Rebie63@hotmail.com PHONE NO ; 00218 91 4222563 2) NABIL.M.ELALGSHAT HEAD OF COMPUTER DEPARTMENT BADER COLLEGE OF ARTS &SCIENCE AL Zintan UNIVERSITY Al Zintan LIBYA. E-Mail, I. D :gashat@go.uoz.edu.ly PHONE NO ; 00218 91 7958203 3) Emre BAŞESKİ HEAD OF COMPUTER DEPARTMENT CANKAYA UNIVERCITY TURKY E-mail: emrebaseski@cankaya.edu.tr
المنشورات العلمية
Analyzing the Efficiency of a Data Mining Dataset in Weka Implementing an Automotive Dataset
Journal ArticleAbstract
The Car manufacturing sector represents a major focus in the development of the automotive industry. In this research paper, a proposed data mining application for the automotive manufacturing sector is explained and tested. The dataset was retrieved from the machine learning repository at the University of California, Irvine. This research paper aims to create a more reliable classifier for future object classification. Classification is an important technique in data mining. It is a supervised learning process that involves classifying an object into one of the predefined classes based on its attributes. In this paper, we use a large database containing 7 attributes and 1,728 instances. We compare the
results of a simple classification technique (using the J48 decision tree inference algorithm and MONK) with results based on different parameters using WEKA (Waikato Environment Knowledge Analysis), a data mining tool. The results of the experiment show a comparison between three algorithms to see which is the best and least error-prone algorithm. The physical characteristics of a car viz . Engine location ,price, how many doors, stroke, city fuel consumption, and other factors determine a vehicle's performance. Therefore, developing such a classification, although a huge undertaking, is absolutely essential in the car industry. Machine learning techniques can help integrate computer-based systems to predict vehicle quality and improve system efficiency. Classification models were trained using 214 datasets. The predicted values of the classifiers were evaluated using 10-fold cross-validation, and the results were compared.
Keywords: Data mining, Machine learning techniques, J48, decision trees, Car market, WEKA classification.
Adballa Nagat Esiad Rahel, (09-2025), International Science and Technology Journal المجلة الدولية للعلوم والتقنية: المجلة الدولية للعلوم والتقنية International Science and Technology Journal, 37
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
ChapterAbstract
This paper is the report of the first Under-Display Camera (UDC) image restoration challenge in conjunction with the RLQ workshop at ECCV 2020. The challenge is based on a newly-collected database of Under-Display Camera. The challenge tracks correspond to two types of display: a 4k Transparent OLED (T-OLED) and a phone Pentile OLED (P-OLED). Along with about 150 teams registered the challenge, eight and nine teams submitted the results during the testing phase for each track. The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration. Datasets and paper are available at https://yzhouas.github.io/projects/UDC/udc.html.
Keywords
Under-display camera Image restoration Denoising Debluring
Nagat Adballa Esiad Rahel, (01-2021), ECCV 2020: Computer Vision – ECCV 2020 Workshops pp 337-351: international conference,