Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo 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, RCOVID-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.Publication Embargo 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. AVision 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.Publication Embargo 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, DSri 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).Publication Embargo A Mobile-Based Screening and Refinement System to Identify the Risk of Dyscalculia and Dysgraphia Learning Disabilities in Primary School Students(IEEE, 2021-08-11) Hewapathirana, C; Abeysinghe, K; Maheshani, P; Liyanage, P; Krishara, J; Thelijjagoda, SLearning Disability is a condition that has a direct effect on the brain and there is no cure or any identified medical treatments. Most of these cases remain undiagnosed due to the lack of awareness from their parents and teachers in underdeveloped countries like Sri Lanka. Mobile application-based solution ‘Nana Shilpa’ was developed for the screening and intervention processes for the specific Learning Disabilities which are Verbal and Lexical Dyscalculia, Operational and Practognostic Dyscalculia, Letter Level Dysgraphia and Numeric Dysgraphia. Deep Learning with Machine Learning techniques is used in the screening process to provide a better solution. To detect the written letters/numbers, trained Convolutional Neural Networks (CNN) achieved the accuracy of 92%, 99%, 99% for Verbal and Lexical Dyscalculia, Letter Level Dysgraphia and Number Dysgraphia respectively. The Machine Learning algorithms used for screening processes are Support Vector Machine (SVM) and Random Forest (RF). In the machine learning models, it is achieved the accuracy of 98%, 97% for Operational and Practognostic Dyscalculia and Number Dysgraphia respectively. In Sri Lanka, this has been recognized as an acceptable solution for screening and intervention via a mobile-based application for above mentioned four variants of learning disability conditions which are developed based on the gaming environment.