Research Papers - Dept of Information Technology

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    Graph Neural Network based Child Activity Recognition
    (IEEE, 2022-08-25) Mohottala, S; Samarasinghe, P; Kasthurirathna, D; Abhayaratne, C
    This paper presents an implementation on child activity recognition (CAR) with a graph convolution network (GCN) based deep learning model since prior implementations in this domain have been dominated by CNN, LSTM and other methods despite the superior performance of GCN. To the best of our knowledge, we are the first to use a GCN model in child activity recognition domain. In overcoming the challenges of having small size publicly available child action datasets, several learning methods such as feature extraction, fine-tuning and curriculum learning were implemented to improve the model performance. Inspired by the contradicting claims made on the use of transfer learning in CAR, we conducted a detailed implementation and analysis on transfer learning together with a study on negative transfer learning effect on CAR as it hasn’t been addressed previously. As the principal contribution, we were able to develop a ST-GCN based CAR model which, despite the small size of the dataset, obtained around 50% accuracy on vanilla implementations. With feature extraction and fine tuning methods, accuracy was improved by 20%-30% with the highest accuracy being 82.24%. Furthermore, the results provided on activity datasets empirically demonstrate that with careful selection of pre-train model datasets through methods such as curriculum learning could enhance the accuracy levels. Finally, we provide preliminary evidence on possible frame rate effect on the accuracy of CAR models, a direction future research can explore.
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    PublicationOpen Access
    Combined Approach of Supervised and Unsupervised learning for Dog Face Recognition
    (IEEE, 2021-04-02) Weerasekara, D. T; Gamage, A; Kulasooriya, K. S. A. F
    One would be surprised to hear the lost dog rates around the world. Even though it is something that one doesn't ponder a lot about, lost dogs are a problem that most dog owners fear. Dogs provide humans with companionship, protection, and unconditional love, and to the dogs; their whole world revolves around their owner and their family members. Therefore, when a pet dog goes missing, not only the dog owner but also the pet dog is affected. Unfortunately, in Sri Lanka, a lost dog being found is a very rare occurrence. A reason for this can be pointed out as the lack of an easily-accessible, public platform for lost dogs. In this research project, a solution to this problem has been implemented using image processing. This research study is about image classification and recognition using the Convolutional Neural Network (CNN) or also known as Shift Invariant or Space Invariant Artificial Neural Network (SIANN) by using TensorFlow framework as well as Keras library. The VGG16 model was customized for being used feature extraction. The implementation was a combination of both Machine Learning and Deep Learning. The platform to upload the found dog is also a continuous and inter-related subcomponent that provides a happy and healthy life for stray dogs too. That idea is providing them a higher chance to find a safe place to survive and also a home where they will be loved. The results are discussed in terms of the accuracy of the image recognition and classification in percentage. Each group of dogs get around 90% accuracy or above.
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    PublicationOpen Access
    Advance Technology for Kids to Improve Knowledge and Skills using Motion Gesture Recognition – Leap Mania
    (SLIIT, 2014-12-16) Nandasiri, K. G. M. P; Nawarathna, N. H. C. E. M; Mohamad, M. M. R; Herath, H. M. C. K; Kasthuriarachchi, K. T. S; Wijendra, D
    Leap mania is a gesture controlled e-leaning system which targets the nursery level kids to improve their knowledge and skills in a pleasurable learning environment. Game-based learning is becoming popular in the academic discussion of Learning Technologies. However, even though the educational potential of games has been thoroughly discussed in modern days, teaching to small kids became difficult due to the short attention spans of them. In addition to traditional methods of learning and teaching, such as reading books and newspapers, a huge variety of online educational resources are available to provide an atmosphere of fun and interactive designs to keep children engaged. However, there is no proper e-learning game tools with gesture control mechanism found among the tools and computer based applications for kids. This research focuses on building an enthusiastic and pleasurable learning environment to enhance the knowledge and skills of kids by implementing a game-based learning application using leap motion controller.