Researcher Image
رشا ماجد حسون سعيد - Rasha Majid Hassoon
MSc - assistant lecturer
College of Physical Education and Sports Sciences for Girls , Theoretical sciences
[email protected]
Research Interests

Computer Science

Artificial Intelligence

Security

Website Security

Networking

Academic Area

Computer Science

Artificial Intelligence

Security

Website Security

Networking

Teaching materials
Material
College
Department
Stage
Download
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
حاسوب
كلية التربية البدنية وعلوم الرياضة للبنات
العلوم النظرية
Stage 1
Teaching

Computer

Publication Date
Tue Jun 18 2024
Journal Name
Al-bahir
Organization of Traffic on the Main Streets Microcontroller Traffic Lights
...Show More Authors

View Publication Preview PDF
Crossref
Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Advances In Scientific Research And Engineering
Yolo Versions Architecture: Review
...Show More Authors

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Sun Sep 01 2024
Journal Name
Baghdad Science Journal
Hetero-associative Memory Based New Iraqi License Plate Recognition
...Show More Authors

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول

... Show More
View Publication Preview PDF
Scopus Clarivate Crossref
Publication Date
Sun Feb 25 2024
Journal Name
Tikrit Journal Of Pure Science
Optical Mark Recognition using Modify Bi-directional Associative Memory
...Show More Authors

Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using M

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Tue Mar 01 2016
Journal Name
International Journal Of Engineering Research And Advanced Technology (ijerat)
Speeding Up Back-Propagation Learning (SUBPL) Algorithm: A New Modified Back_Propagation Algorithm
...Show More Authors

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

View Publication
Publication Date
Thu Oct 01 2015
Journal Name
International Journal Of Modern Trends In Engineering And Research (ijmter)
Effect of changing hidden neurons and activation function on Back Propagation (BP) Speed
...Show More Authors

The Back-Propagation (BP) is the best known and widely used learning algorithm in training multiple neural network. A vast variety of improvements to BP algorithm have been proposed since ninety’s. in this paper, the effects of changing the number of hidden neurons and activation equation are investigated. According to the simulation results, the convergence speed have been improved and become much faster by the previous two modifications on the BP algorithm.

Publication Date
Wed Oct 21 2015
Journal Name
Integrated Journal Of Engineering Research And Technology
A HYBRID CUCKOO SEARCH AND BACK-PROPAGATION ALGORITHMS WITH DYNAMIC LEARNING RATE TO SPEED UP THE CONVERGENCE (SUBPL) ALGORITHM
...Show More Authors

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.