Browsing by Author "El Masry, Noha"

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    Amelio-rater: Detection and Classification of Driving Abnormal Behaviours for Automated Ratings and Real-Time Monitoring
    (IEEE, 2018) El Masry, Noha; El-Dorry, Passant; El Ashram, Mariam; Atia, Ayman; Tanaka, Jiro
    Real-time monitoring of the drivers may be a factor that would force them to drive safely. In this paper, we introduce a system named 'Amelio-Rater", that focuses on detection and classification of abnormal driving behaviours for automatically generating driver ratings and real-time monitoring. To reduce malicious ratings, the Amelio-rater introduces an automatic rating system which is calculated purely based on the driver's driving behaviours only. Each driver will be given his own Amelio-rater rate and a manual user rate. There are multiple types of driving abnormal behaviours monitored by the proposed system such as meandering, single weaves, sudden changing of lanes and speeding. The classification results achieved showed that the Amelio-rater reached an accuracy of 95%. Our experiments showed that the manual user rates given for the driving behaviour are not far from the rates given by Amelio-rater. Amelio-rater rates were very close to the actual rates given by the users
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    Detecting Abnormal Fish Behavior Using Motion Trajectories In Ubiquitous Environments
    (Elsevier Ltd, 2020-01) Anas, Omar; Wageeh, Youssef; Mohamed, Hussam El-Din; Fadl, Ali; El Masry, Noha; Nabil, Ayman; Atia, Ayman
    Monitoring fish farms as controlling water quality and abnormal fish behaviors inside fish pond are one of the most costly and difficult task to do for fish farmers. Fish farmers normally do these tasks manually, which requires them to dedicate lots of time and money. Way for detecting fish behaviors is presented in this paper by identifying the fish and analyzing their trajectories in a difficult water environment. First of all, we used an image enhancement algorithm to color-enhance water pictures and to enhance fish detection. We then used an algorithm for object detection to identify fish. Finally, we used a classification algorithm to detect fish abnormal behavior. Our aim is making an automated system that monitors the fish farm to reduce costs and time for the fish farmers and provide them with more efficient and easy ways to perform their operations