Faculty Of Computer Science Graduation Project 2020 - 2022

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Browsing Faculty Of Computer Science Graduation Project 2020 - 2022 by Subject "Deep Learning"

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    Designing Potential Inhibitors For SARS-CoV-2 Main Protease Using Deep Learning
    (October University For Modern Sciences and Arts, 2022) Hassan, Adham Khaled
    in this work we are trying to speed up the process of finding a cure for SARS-CoV-2 since SARS-CoV-2 have impacted our society due to the global pandemic which have affected the education, economy, world heath care and deaths caused by the virus due to the long time taken by drug discovery pipeline which is between 10 to 12 yeas for a drug to be develop and the enormous cost of the drug discovery pipeline and the low success rate of drug passing the FDA approve in the have motivated us to design a deep learning solution for designing a drug in fraction of the time required and for the current event done by the SARS-CoV-2 the target for this proposed solution will be SARS-CoV-2 the proposed solution is consist of two model one for generative molecules and the other for predicting the affinity of the molecule toward SARS-CoV-2 the proposed solution achieved a generation of molecules with average affinity of -9.8 and a prediction of accuracy of 98.1625% toward SARS-CoV-2, the proposed solution could reduce the drug discovery pipeline which is between 10 to 12 yeas to only 1 to 3 yeas for any novel virus.
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    Football Match Events expressing for visually and hearing Impaired
    (October University For Modern Sciences and Arts, 2022) Badr Abdelqader, Mohammad
    Football is the most viewed sport in the world. Many visually and hearing impaired individuals deserve to keep up with matches like normal people, so a players classification model has been implemented to classify players and referees. As well as a ball detection model that would help to detect the ball positions on the field in addition to an action recognition algorithm that would help to get the current state of the ball in order to perform a simulation for the players and ball movement on a 2D pitch using perspective transformation. 9
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    Forensic Document Examination For Handwritten Signatures Using Deep Learning
    (October University For Modern Sciences and Arts, 2022) Tarek Ibrahim, Omar
    Forgery is a type of fraud de ned as the act of forging a copy or an imitation of a document, signature, or banknote which is considered a form of illegal criminal activity. In this project, we are focusing on developing an application that aims to help forensic examiners in the process of identi cation and detection of handwritten signature forgeries inside documents in addition to the identi cation of possible types of signature forgery methods. The proposed system uses contemporary methods that utilize a deep learning approach of CNNs (Convolutional Neural Networks) based on both binary, and categorical image classi cation methodologies to help forensic examiners measure the genuineness of handwritten signatures. For the image classi cations, we have considered using a number of ve di erent CNN models which are: VGG-16, ResNet50, Inception-v3, Xception, and our 2DCNN model. The purpose of using these di erent CNN models is to conduct a comparative study between each of the models to help determine which model is more e cient at identifying images containing text data of similar resemblances, as to the nature of handwritten signatures. Upon comparing these CNN models, we concluded that the ResNet50 model was able to reach the highest score at identifying handwritten signatures with an accuracy of 82.3% and 86% when tested on datasets of 300 images and 140 images respectively. The system aims to provide all functionalities which are required by current standards through the use of robust system architecture, design patterns, and various techniques to build a practical application that follows an expandable structure.