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Author: search.filters.author.Pemadasa, NSubject: search.filters.subject.Deep Learning

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    UveaTrack: Uveitis Eye Disease Prediction and Detection with Vision Function Calculation and Risk Analysis Publisher: IEEE Cite This PDF
    (Institute of Electrical and Electronics Engineers, 2022-10-15) Perera, B. D. K; Wickramarathna, W.A.A.I.; Chandrasiri, S; Wanniarachchi, W.A.P.W; Dilshani, S.H.N; Pemadasa, N
    Uveitis is an inflammatory infection that affects uvea tissue, the middle layer of the eyewall. It can result in swelling or damage to the eye and lead to vision impairments or blindness. Most Uveitis symptoms are associated with many other diseases localized to the eye. Thus, it is hard to determine the responsible symptoms for uveitis. Consequently, early detection of this disease can prevent a perilous situation in the future. The initial motivation behind the design of this mobile application is to help accurately diagnose uveitis with minimal time and effort and thereby minimize the shortage of human specialists in this field. The 'UveaTrack' is a hybrid mobile application that enables the keep tracking of uveitis eye illness and uses machine learning (ML) algorithms, deep learning (DL) architectures, and image processing techniques for developing the system. The 'UveaTrack' application could be able to achieve an average accuracy of more than 85% and had produced overall better results. Furthermore, the 'UveaTrack' application can use as a valuable instructional tool for freshly graduated clinicians, supporting their work with patients and assisting them in making diagnostics conclusions.
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    Deep learning based flood prediction and relief optimization
    (IEEE, 2019-12-05) Pathirana, D; Chandrasiri, L; Jayasekara, D; Dilmi, V; Samarasinghe, P; Pemadasa, N
    Flood is a major natural disaster that occurs recurrently in Sri Lanka. It is important to stay on alert and get early preparations to avoid unnecessary risks that cause damage to both life and property. This project developed a flood assistance application “DHARA” to support early flood preparation and flood recovery process. DHARA mobile application facilitates river water level prediction, safest evacuation route suggestion and provides relevant warnings and alert notifications and the web application provides affected area detection, victim and relief estimation to assist flood recovery management. This system is developed as a mobile application and a web application. A recurrent neural network architecture named Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), a path finding algorithm namely A star (A*) algorithm and a clustering technique named Fuzzy Clustering are used for the development of the system. The system is verified with sample data related to “Wellampitiya” and “Kaduwela” area based on river “Kelanl”. The river water level prediction model has successfully predicted the water level 4 hours in advance. The verification results of the river water level prediction showed a satisfactory agreement between the predicted and real records with 85.4% accuracy.