Research Papers - Dept of Information Technology

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Author: search.filters.author.Chandrasiri, SSubject: search.filters.subject.Augmented

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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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    Kidland: An Augmented Reality-based approach for Smart Ordering for Toy Store
    (IEEE, 2021-12-01) Wijayalath, W. M. C. D; Ranasinghe, R. M. T. T; Thennakoon, M. T. H; Vithanage, H. D; Chandrasiri, S
    Augmented reality (AR) is an iconic topic that can be applied in different domains in modern world technology. With the rapid development of technologies, eCommerce (Online Shopping) has become closer to human life. As a result, AR was started implemented with eCommerce platforms by the developers. With the busy lives and the pandemic situation, people are limited to visiting toy stores while providing a solution. An AR-based virtual toy store is proposed with 3D Toy generation for visualizing selected toys, a Virtual tour for enhancing the remote virtual shopping experience, and an Indoor navigation system visualizing the path within large scale shopping malls are new features of the proposed system. The majority of the existing eCommerce platforms are missing image search features. As a solution, “KidLand” has implemented an image search engine, suggesting add-on-related items and nearest branches using machine learning algorithms. An intelligent chatbot uses a reinforcement learning algorithm and Natural Language Understanding (NLU) to give possible solutions regarding the toy store. As a solution to the language literacy problem, developed a chatbot that can chat both English and Sinhala languages. “Kidland” was developed to provide the users the next level of shopping experience with attractive features of AR technology with marketing and use advanced technologies overcoming the issues of ordinary eCommerce platforms. In Sri Lanka, this system has been identified as a solution for the issues with ordinary shopping platforms.
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    Smart Pest Management: An Augmented Reality-Based Approach for an Organic Cultivation
    (IEEE, 2021-01-17) Mahenthiran, N; Sittampalam, H; Yogarajah, S; Jeyarajah, S; Chandrasiri, S
    The agricultural world faces more difficulties due to crop pests that damage or infliction cultivated plants. The main challenges to those interested in cultivation are pest attack and disease. Pests spread the disease, and the yield is decreased. However, it is possible to control pest attacks and infections in the early attack stage to reduce pesticide use and keep the farm safe. Mobile applications can provide accurate identification rather than manual detection. Mobile applications and technologies are created when considering the solution. The importance of the proposed solution is to increase the rate of the plant product and achieve high revenue without any cost. One of the main components used in this system is the image processing technique. The pest images will be taken, and they will be subjected to various preprocessing for noise reduction and enhancement of the pictures. Using image processing, the user can determine the pest's life cycle stage. The user can identify the stage of the damaged plant by applying the classification algorithm. The content analysis is based on the machine learning process, especially using a Convolutional Neural Network. Hence, the proposed system will help to get knowledge of organic pest prevention methods. In the system, we determined the type of pest with 90% accuracy by submitting a damaged leaf and a pest image. The pest's lifecycle stage and stage of the affected plant also can be identified in our system with high accuracy. Moreover, it shows the organic prevention methods.