Research Papers - Dept of Computer Systems Engineering

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    iMask: An IoT-based Intelligent Mask to Identify and Track COVID-19 Suspects
    (IEEE, 2022-09-08) Yamasinghe, N; Ranasinghe, Y; Dissanayake, Y; Wijekoon, J.L; Panchendrarajan, R
    COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately.
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    Document Reader for Vision Impaired Elementary School Children to Identify Printed Images
    (IEEE, 2019-12-05) Gamage, N. D. U; Jayadewa, K. W. C; Jayakody, J. A. D. C. A
    Vision Impairment is a severe reduction of one or more functions of the eye. The print disability prevents a person from gaining information from printed material in the standard way and requires them to utilize alternative methods to access the information. World Health Organization estimated that nineteen (19) million children are visually impaired worldwide. As they are the future of the world it is necessary to eradicate barriers to the journey of gaining knowledge. Hence, this paper presents a mobile-based application targeting elementary school students to read textual documents, which contains a graphical image. The mobile application provides audio assistance to navigate through a mobile application, autofocused image capturing of printed papers, store captured images, classify selected text, images, and read-aloud generated digitized text. Therefore, “Schmoozer” would allow visually impaired individuals to read unbraided documents without others' interaction. Furthermore, this paper discusses the test results and evaluations to justify the feasibility of the proposed solution.
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    Mobile-based Assistive Tool to Identify & Learn Medicinal Herbs
    (IEEE, 2020-12-10) Senevirathne, L. P. D. S; Pathirana, D. P. D. S; Silva, A. L; Dissanayaka, M. G. S. R; Nawinna, D. P; Ganegoda, D
    Sri Lanka is recognized and valued globally due to its rich heritage of tropical plants, herbs and trees. A need for the valuation of valuable herbs are identified among both Sri Lankans as well as tourists. This paper brings forth a solution in distinguishing medicinal herbs through leaves and flowers using deep learning and image processing algorithms via a mobile application. The proposed mobile application identifies a flower and leaf by its morphological features, such as shape, color, texture. The perspective is to achieve highest accuracy for plant identification using image processing. The proposed model revealed an accuracy of 92.5% in the classification of leaves and flowers. Accuracy of 6 different plants are identified using this method. This application also provides Sinhala virtual assistant which enables user to search herbs using the name, which is popular among people, to obtain information about herbs. The main outcome of the virtual assistant of the research is to develop an information retrieval method on medicinal herbs in a more accurate, easy and efficient way. In addition. this application also provides 3D structure of the selected medicinal herb in augmented reality (AR).