Browsing by Author "Joseph, J. K"

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Amazon Biology: An Augmented Reality-Based E-Book for Biology
(IEEE, 2020-12-10) Somakeerthi, D. C. S; De Silva, G. W. I. U; De Silva, L. D. T; Chandrasiri, S; Joseph, J. K
Biology is a conventionally struggling subject to learn from both high school and college students due to its complexity. Students are used to learning Biology from various methods such as reading textbooks, attending lectures. Biology is based on more practical and most of the schools not available proper lab facilities, anatomic structures, and resources to learn the module easily. And teachers who teach the module face a considerable number of issues when delivering the concepts. Some of them face unavailability of teaching aids, time-consuming, lack of lecture materials. Apart from that, the nature of the topic and the teaching style are the main learning problems faced by the students. Therefore, students do not learn the concepts perfectly and interest in the module has been reduced day by day. To overcome these difficulties “Amazon Biology,” mobile application has been proposed. The application consists of three major modules including image processing for the plant classification, augmented reality for human anatomy, and gamification. The proposed application has used the techniques in augmented reality and game-based learning. The developed system delivers nearly 85% level of accuracy and provides more advantages for students. They are effective and efficient learning, teaching via visual materials, and practical.
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Evolutionary Algorithm for Sinhala to English Translation
(IEEE, 2019-10-08) Nugaliyadde, A; Joseph, J. K; Chathurika, W. M. T; Mallawarachchi, Y
Machine Translation (MT) is an area in natural language processing, which focuses on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules, and therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it into English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.