Graduation Projects 2017-2018

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Spring Term 2018

  • RecycleMe:

Description:This project implements a mobile app that converts the mobile phone into a scanning device to detect if the scanned material is recyclable or not using machine learning. This project helps household residents and business owners perform accurate and efficient sorting at source, thereby significantly reducing the overhead in recycling plants and contributes to the waste management crisis in Lebanon.
Team: Mohammad Hallak, Haifa Naim, Khaldoun Noureddine​ 
Advisor: Prof. Ahmad Dhaini
​​Poster | ​Video 

  • TBTest.me:

Description:This project is in collaboration with the faculty of medicine at AUB; it designs and implements a mobile application (both on Android and iOS using ReactNative) that determines by visual scan if a tuberculosis (TB) is positive or negative based on the size, color, and height of the bump, using machine learning and image analysis..
Team: Fatima Bdeir, Hamza Mogharbel, Estephan Rustom​ 
Advisor: Prof. Ahmad Dhaini 
Poster | ​Video 

  • MyOCT:

Description:This project implements a portal for manipulating optical coherence tomography (OCT) images to be used by ophthalmologists at AUBMC. The new portal interacts with existing back-end software that performs medical image analysis using machine learning. The web portal includes a user-friendly graphical user-interface (GUI) that allows the physician to create a project/file per patient, generate a report (in PDF format) that includes corneal haze statistics, toggle/change some back-end parameters to get a customized view of the image, visually highlight/color-code some regions for analysis, and manually perform measurements using a digital caliper.
Team: Fares Tabet, Jad Charrouf, Jack Azadian​​ 
Advisor: Prof. Ahmad Dhaini 
​​Poster | ​Video ​

  • Malware Classification Using Deep Learning:

Description:A malicious malware is a hostile and intruding software, with the intention of either harming or retrieving data, perform actions that the user does not want to happen, or even spy on the user. Ever since malicious malware was created, many computer scientists have been trying to find ways of combating them. For many years, the main source of malware classification and prevention is signature based, which uses previously known viruses to detect malware. This project explains the weaknesses of the signature-based approach, how deep learning is used to identify malware, and extend one of the proposed works in deep learning to classify the nine family of malwares.
Team: Mohamad Alkadri , Jad Kechichian, Samer Takieddine​ 
Advisor: Prof. Haidar Safa 

  • SOS: a mobile application for protecting individuals in danger:

Description:The fear of being subjected to a crime made it necessary to find a way that can aid in saving the community and ourselves from being added to the innocent victims. Therefore, there is a need for an application for protecting the individuals, mainly women, in addition to tracking under age children and elders. This project developed “SOS” which is an android mobile application that can be used by females subjected to abuse, family members with a certain medical condition (such as Alzheimer) and under age children to track their locations.
Team: Sara Barakat,  Christian Roustom,  Dania Othman​ 
Advisor: Prof. Haidar Safa​ 
Poster | ​Video 

  • Collaborative Programming:

Description:Collaborative Programing is an online collaborative web-platform aimed at teaching students programming in a collectivized environment. This platform offers users a hands-on experience with design patterns and object oriented programming.
Team: Osama El Hariri, Liwae Lamaa, Mohamad Hassan​ 
Advisor: Prof. Haidar Safa 
Poster | ​Video 

  • Syria Migration Analysis:

Description:This project aims to look for correlations between casualties through violent events in Syria, their possible effects on the migration o​f Syrian refugees and the demand of medical services for refugees in medical centers across Lebanon. Understanding these patterns of migrations and the reasons behind them can help us predict the demand of medical care in certain medical centers by incoming refugees.
Team: Ali Fawaz, Karim El Laham, Hussein Harakeh​ 
Advisor: Prof. Fatima Abu Salem 

  • Syria Refugees Demand on Healthcare Forecasting​:

Description:The project aims to forecast demand by Syrian Refugees and Lebanese citizens on Primary Health care centers in Lebanon on data provider by the ministry of Public Health. The developed website allows the employee at the Ministry, for instance, to create new records per patient (age, id, gender, district, governance and department). The user is then directed to a new page where they can analyze and predict the future records with the new record added​.
Team: Yara Rammal, Dana Ali Ahmad, Yehya Obeid​ 
Advisor: Prof. Fatima Abu Salem 
 ​​​Poster | ​Video1​  | ​Video2 ​