Research Publications Authored by SLIIT Staff

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Now showing 1 - 3 of 3

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Smart Attendance and Progress Management System
(Springer, Singapore, 2021) Krishnapillai, L; Veluppillai, S; Akilan, A; Saumika, V. N; De Silva, K. P; Gamage, M. P. A. W
Management of attendance may be a great burden on lecturers if done manually. This study focuses on finding an automated solution for taking attendance and keeping track of progress of a student in a smart way. The smart attendance system is generally using biometrics for identifying individuals. In this study, face recognition was considered for identification. The student's face is recognized and attendance is taken using face biometrics based on high-definition monitor camera. The images of the student are given as an input and image classification was done using CNN algorithm preventing duplicate entries for attendance. For tracking the progress of the student, the factors affecting the GPA are trained using Machine Learning algorithms. This research also aims to examine the effective progress of undergraduate students by taking past year records and find out the factors for their high and low output which will be helpful to improve their performance.
Embargo
Intelligent Trainer for Athletes using Machine Learning
(IEEE, 2019-09-27) Attigala, D. A; Weeraman, R; Fernando, W. S. S. W; Mahagedara, M. M. S. U; Gamage, M. P. A. W; Jayakodi, T
International professional athletes are looked after and trained by a team of professionals consisting of trainers and medical professionals among other. They make sure that the athlete is physically and mentally prepared to compete in a competition, and often train for years for the perfect results. Sri Lankan athletes however do not have the same luxury of being taken cared by a team of such professionals since they are young due to the lack of adequate resources in the country. `Optio' mobile application aims to provide a solution for this problem by creating a mobile application that the athlete constantly has access to, which will provide him/her with dietary, exercise and health related advice catered and customized to each individual athlete's needs. Consequently, this will provide a method which will let the athlete's trainer monitor their athletes easily as well as let them pick the most suitable athlete for a competition.
Open Access
Agro-Genius: Crop Prediction Using Machine Learning
(https://ijisrt.com/agrogenius-crop-prediction-using-machine-learning, 2019-10) Gamage, M. P. A. W; Kasthurirathna, D; Paresith, M. M; Thayakaran, S; Suganya, S; Puvipavan, P
This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.

Research Publications Authored by SLIIT Staff

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