moh
The student Shilan Waa'd Idwan obtained her master's degree from the Department of Computer Sciences / University of Technology for her thesis entitled "Multi Robot Path Planning in Cluttered Environment Using Evolution Computation Techniques" The thesis is proposal of the multi-robot path planning system in a two-dimensional environment to solve the problem of finding a road in Traffic jam. The modified algorithm was used with the approximate bats algorithm. The direction of the fast-tracked random tree detection algorithm was first modified and redirected by replacing stored storage accounts with a proposed new calculation method that reduces execution time. Secondly, the algorithm of the fast-tracked, fast-tracked random tree has been modified to operate in a moving environment by modifying the evaluation equation with a proposed one based on the bat's algorithm. Thirdly, enable the rapid-oriented, rapid-motion-guided random-motion detection algorithm to plan the path of multiple robots in a parallel, dynamic and real-time environment by employing the swarm principle on the bat-swarm algorithm that has been combined with the improved modified rapid-guided random tree algorithm. Object detection algorithm and improved tracking algorithms. The development of algorithms for estimating traffic parameters such as displacement, speed, direction and positioning are generated through the development of the results of detection and tracking algorithms.
This was on Sunday 10/6/2018 and at the discussion hall in the annex building of the department. The discussion committee consisted of Dr. Nada Flieh Hassan of the University of Technology / Department of Computer Sciences and Dr. Bilal Khalil Ismail, from Al Anbar University, Computer Science Department, and Dr. Abbas Fadhel Mohammed Ali, from the University of Information and Communication Technology, in the presence of the student 's supervisor (Dr. Aliaa' Karim Abdel Hassan).
|
Source: Department's Media |
The student (Nabaa' Basil Abid) received her master's degree from the Department of Computer Sciences / University
of Technology for her thesis (Autonomous Car Control based on Pattern Recognition Algorithms) The objective of
self-driving cars is Reduce risks, problems and costs that arise from human participation. Auto-driving is designed
for roaming between places of destination without the human operator. So one of the main functions of self-driving
cars is auto detection moving in front of it using computer vision techniques. Detecting and tracking a mobile body
is still an open research issue, though research for years. Even today, it remains a major challenge to achieve a
rigorous, robust and high-performance approach. How one can define an object to be detected and tracked is the
level of difficulty of this problem, which helps to make a decision to determine the path of independent cars
avoiding accidents. Object detection of a series of images is a preparatory step but a critical task for computer
vision applications in extracting the information. The main objective of this work is to develop object detection
algorithm and improve trace algorithms. Development of algorithms traffic parameters such as displacement, speed,
direction and positioning are estimated by developing the results of detection and tracking algorithms.
This was on Thursday 24/5/2018 and at the discussion hall in the annex building of the department. The discussion committee was composed of (Dr. Matheel Emad El-Din Abdel Moneim, Dr. Ekhlas Khalaf Kabbashi (from the University of Technology / Department of Computer Sciences and (Dr Ibrahim Nazir Ibrahim from Al Mustansiriya University / Faculty of Basic Education / Department of Computer Science in the presence of the student's supervisor (Prof. Dr. Abdel Munem Saleh Rahma).
|
Source: Department's Media |
The student (Sura Rahim Eidan) obtained her master's degree from the Department of Computer Sciences / University
of Technology for her thesis (Face Recognition Technology To Monitor Class Attendance). In this study, biometrics
literature was presented in general and its characteristics, applications, Work in general, and compare the
different types of measurements, because the facial recognition system, is one of these types of measurements. In
addition to identifying the structure of the system, the methods used in each step starting from the detection
stage, draw characteristics down to the recognition process, the challenges faced by each stage and common
techniques. Building an automated attendance management system by identifying and identifying people based on the
face recognition technique, this system has advantages that surpass traditional systems and some automated systems
based on other biometrics in attendance management. More than one person can be detected Identify it at the same
time and record attendance, and report it.
The discussion was on Wednesday, 16/5/2018, at the discussion hall in the annex building of the department. The discussion committee was composed of Dr. Abdul Amir Abdullah Karim, (Computer Science) and Dr. Sawsan Abdel Hadi Mahmoud of Al Mustansiriya University / Faculty of Education / Computer Science Department in the presence of the student supervisor (Dr. Rehab Fleih Hassan).
|
Source: Department's Media |
Saif Al Din Salem Ahmed received his master's degree from the Department of Computer Sciences / University of
Technology for his thesis entitled (Development of the model of detecting anomalies in the e-commerce database
system). This thesis proposes designing a system for detecting anomalies for e-commerce sites. This proposed system
consists of the first two phases of the consolidation phase and the second the classification phase. In the first
stage, the modified PMK-MEMS algorithm was proposed as a first step for data collection and the SVM algorithm as a
second stage of data classification to solve the problems and abnormal movements of e-commerce data. The proposed
amendment will be applied as a pre-processing of the PMK-MEMS algorithm. The main objective is to generate
aggregates of e-commerce transactions for the anomalous data set and then apply the SVM algorithm to classify the
data set and detect the anomalies of the data set.
The discussion was on Monday 14/5/2018 at the discussion hall in the annex of the department. The discussion committee was composed of Prof. Dr. Yousra Hussein Ali and Dr. Mohamed Natik Fadel from the University of Technology / Computer Sciences Department and (Dr. Inas Mohammed Hussein from Al Mustansiriya University / Faculty of Education / Department of Computer Science in the presence of the student supervisor (Dr. Abeer Tariq Mouloud).
|
Source: Department's Media |
