Faculty of Computing

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Author: search.filters.author.Kumari, S

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    E-Learning Assistive System for Deaf and Mute Students
    (IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, S
    E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
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    Peer Learning – An Interactive and Collaborative E-Learning Application for College Students
    (IEEE, 2022-12-26) Sirithunga, H. A. P. M.; Deshan, B. G. S; Sigera, P. H. D; Udagedara, P.Y; Samarakoon, U; Kumari, S
    Since the start of the COVID-19 epidemic in 2020, the entire educational system has been challenging and Sri Lanka economic crisis, but this is especially effect for students who are now enrolled. This developmental milestone is reached when adolescents begin to assume responsibilities and acquire leadership skills through participation in a range of team activities. It is easiest to gain experience working in a group setting while still in school. Nevertheless, given the current stage of the Sri Lanka economic crisis, students will face a range of challenges. They are incapable of participating in group activities that are relevant to the subjects they teach, and, as previously indicated, enhancing their leadership skills, which is particularly problematic when working with students. The “Peer Learning” solution is a web-based application that supports students in enhancing their collaborative learning skills. Through the system, students have the opportunity to study a variety of collaborative tasks, which improves their educational and interpersonal abilities. In addition, professors can share their knowledge with students by personalizing questions, posting films, and demonstrating figures. Students can easily comprehend the system’s operation due to its user-friendly design, which enables advanced technological methods for monitoring and guiding students’ activities simultaneously.
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    PublicationOpen Access
    Tievs: Classified Advertising Enhanced Using Machine Learning Techniques
    (IEEE, 2021-12-06) Ranawake, D; Bandaranayake, S; Jayasekara, R; Madhushani, I; Gamage, G; Kumari, S
    The scarce use of tangible periodicals led to a consistently soaring popularity of online classified advertising. Nevertheless, existing platforms retain complications. Most recommendation systems are built with conventional technologies that are less scalable, less accurate, and having high latency processes. Moreover, customers find it tiring when clarifying a reliable, precise price value for items they are trying to sell through the classified advertising system. Additionally, strict validation techniques to identify and prevent fraudulent content or images from being published in the advertising portals have been neglected. Therefore, authors have inaugurated a superior classified advertising system, Tievs, as a solution, by appraising said predicaments. It wields a flexible, process-simplifying, concurrency-induced recommendation breakthrough implemented from Universal Sentence Encoding incurred Natural Language Processing and Deep Learning routines. Furthermore, an innovative price prediction system having a supervised regression-based ensemble model forged ensuing a comparative study, having excellent accuracy in proactively predicting item prices as to cater hassles faced by customers, was satisfied. Light Gradient Boosting classifier-driven fake description analysis and a Convolution Neural Network powered figure deception recognition system were introduced, which gained prodigious precision with moral clarity in fraud detection and prevention. Hence, the proposed solution's objective of surpassing former classified advertising systems in delivering customers' necessities, using the most lucrative, time-saving, human-centric, and error-preventive approaches, was accomplished. It was affirmative by the positively responded questionnaire regulated among prospective users by the authors.
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    Diagnostic Intervention for Mental Disorder
    (IEEE, 2021-12-01) Senanayake, S; Karunanayaka, C; Dananjaya, L; Chamodya, L; Kumari, S; Chandrasiri, S
    Mental health is one of the essential factors in the topic of healthcare and wellbeing. However, mental health disorders could cause severe damage, even loss of life to the person or the surroundings, if mental health disorders were not identified and appropriately cured. Unfortunately, though there is good help there, some people have a hard time detecting whether they are suffering from mental health disorders or not. In this study, the team proposes a system to detect mental health issues using facial emotion recognition (FER), sleeping patterns, social media web scraping, and heart rate. The intention is to give an accurate prediction of the mental health status of a person using these three nodes.
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    Automated Diabetic Retinopathy Screening With Montage Fundus Images
    (IEEE, 2020-12-10) Kumari, S; Padmakumara, N; Palangoda, W; Balagalla, C; Samarasingha, P; Fernando, A; Pemadasa, N
    Diabetic retinopathy (DR), also known as diabetic eye disease is one of the major causes of blindness in the active population. The longer a person has diabetes, higher the chances of developing DR. This research paper is an attempt towards finding an automatic way to staging DR using montage eye images through artificial intelligence (AI). Convolutional neural networks (CNNs) play a big role in DR detection. Using transfer learning and hyper-parameter tuning DR staging is analyzed through different models. VGG16 gave the highest classification accuracies for the stages Proliferative DR (PDR) & Non-proliferative DR (NPDR). The highest level of NPDR is severe DR which achieved 94.9% classification accuracy (CA) & special features like cotton wool & laser treatment performed at 83.3% CA for each. Moreover, by using patient's history data such as age, right eye & left eye value accuracies & diabetic diagnosed year, system can predict the DR stages. That predictive model has achieved the best CA of 94 % by using Xgboost classifier. Overall, a fully functional app has been developed to detect DR stages with Montage Fundus images using AI.