Faculty Of Engineering Graduation Project 2020- 2022

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Browsing Faculty Of Engineering Graduation Project 2020- 2022 by Subject "Artificial Intelligence"

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    Artificial Intelligence Based Smart Traffic Management System
    (MSA, 2022) Albeir Saber, Anthony; Khaled Mohamed Metwaly, Mohamed
    Due to increasing number of vehicles traffic jams are becoming a common scenario in the whole country as well as in the world. These frequent traffic jams at major junctions kill a lot of man hours. Thus, it creates a need for an efficient traffic management system. So here we are going to implement a smart traffic control system which is based on the measurement of traffic density using real time video processing technique. As the population of the modern cities is increasing day by day due to which vehicular travel is increasing which lead to congestion problem. We used video processing technique caused by using this we can easily calculated density of traffic present on road. The system will detect vehicles through images instead of using electronic sensors embedded in the pavement. A camera will be installed alongside the traffic light. It will capture image sequences. Image processing is a better technique to control the state change of the traffic light. In this proposed system aims to utilize live images from the cameras at traffic junctions for traffic density calculation using image processing and Artificial Intelligence (AI). The project will operate vehicle detection algorithm, speed estimation, and person detection operation. Moreover, the system will investigate the region of interest that the vehicle and person intersect in the movement. It also focuses on the algorithm for switching the traffic lights based on the vehicle density to reduce congestion, thereby providing faster transit to people and reducing pollution.
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    Collision Avoidance System by using Artificial Intelligence for Ground Support Equipment in Airport
    (MSA, 2022) Zenhom, Kareem Gamal; Mohamed, Mohamed Alaa
    In order to lower the ramp risk and improve the aircraft ground handling efficiency, we aim to: (a) Track ground support equipment (GSE) in a real-time and high-accuracy manner so that we can not only conveniently obtain the positions and velocities of them but also reliably report latent collisions among aircraft and GSE. As a result, corresponding ramp risks could be detected and handled in advance; (b) Schedule the GSE in an optimal manner based on the real-time data gathered in advance to make efficient use of GSE so that we can smoothly serve the annually increasing air traffic while controlling the ramp area congestion and GSE overheads This project express to provide a solution for such a problem we are going to proposed an (Artificial Intelligence) AI system which instantly notifies the Ground Support Equipment while moving the airport whenever an GSE in airport could make accident takes place and pinpoints its probability to take place. When an accident could take place, an ultrasonic sensor is used to get the surrounding GSE distances and camera sensor will be used to identify and recognize the surrounding GSE. Then, an algorithm is applied to process the sensor signal and alarm the driver using buzzer and change the text o GSE vehicle recognized will be speeches for the GSE driver. The GSE training for 17 equipment (aero plane, stairs, catering, services, tugs, medical lift, rescue, gpu, cargo loader, belt loader, starter unit, tractor, towing bar, conditioning unit, dolly, apron, and refueler) is achieved. The results for the project is achieved using video and camera live stream for detecting the GSE vehicles with average 89% accuracy and 39 milliseconds detection time
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    Early Detection of Brain Cancer Based on Artificial Intelligence
    (MSA, 2022) Zaki, Micheal Nabil Salama; Abofarw, Saad Mohamed Saad
    More than 150 types of brain tumor have been documented on the basis of histopathologic characteristics. Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this project, a proposed method is used to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Convolution Neural Network algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study will be carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. The project will be able to detect the brain tumor size and location using the auto encoder technique artificial intelligence image processing for the MRI. Also, the system will classify the brain tumor type from three types (Glioma, Meningioma, and Pituitary). The training dataset is achieved after data collection for 1000 sample for each type for brain tumor. The detection and recognition for the brain tumor is achieved using OpenCV, and Tensorflow software tools. Moreover, the project will be delivered using compact hardware (Raspberry pi 4) as independent device. The results show a detection and recognition accuracy for the human brain tumor with 92% because it yields to a better performance than the traditional ones
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    Intelligent Chest Diagnostics Framework Based on IOT and Artificial Intelligence
    (MSA, 2022) Zahra, Ahmed Yosry Ibrahim Abdo Mohamed