Research Papers - Dept of Information Technology

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Author: search.filters.author.Perera, D

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    Optimum Music: Gesture Controlled, Personalized Music Recommendation System
    (IEEE, 2021-12-09) Wijekoon, R; Ekanayaka, D; Wijekoon, M; Perera, D; Samarasinghe, P; Seneweera, O; Peiris, A
    Music plays an important role in everyone’s life since it helps to relax the mind when appropriate music is played. This paper presents a music recommendation system based on the user’s current emotions, activities as well as demographic information such as age, gender, and ethnicity. In addition, the system can be controlled by hand gestures and vocal commands. Unsupervised learning methods in were used to recommend music according to the demographic data and emotions of the user. Finally, the important idea is to recommend music based on all of the user’s data, such as demographics, emotions, and activities. The overall system performance was manually tested and evaluated with a group of individuals, yielding a 70% satisfaction rate for the recommendation; additionally, supporting models such as demographic identification, emotion identification, and hand gesture identification have received a higher proportion of accuracies, contributing to the research’s success. Unlike other systems, ours utilizes all of the user’s information while making music recommendations.
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    Contact Tracing Of Covid-19 Patients Using Tweets
    (IEEE, 2022-02-23) Perera, D; Bamunusinghe, J
    Covid-19 has had an impact on everyone’s lives in the recent past. Presently the field of healthcare uses social media as a tool for professional education and communication. The benefits and drawbacks of these networks have been widely discussed in different research papers. Due to the Covid-19 pandemic, the biggest problem that the government is facing is identifying the close contacts of the Covid-19 patients. Most of the people avoid revealing the truth about the places they visit and people they met in the recent past to the Covid-19 controlling bodies. But people use social media in their day to day life to post/ share their life experiences. Some people use twitter to share their experiences related to Covid-19. In this research paper we focus on tracing the Covid-19 close contacts using tweets. The proposed approach creates a dataset using a twitter API and filters the covid-19 positive users using sentimental analysis. After filtered positive users from the dataset, we have used a set of keywords to filter individual users’ tweets and then we have applied name entity recognition to identify the connected people and places. After gathering each tweet user’s information, we have visualized the relevant relationships of each close contact in a network diagram. Our proposed model indicates 75% accuracy by tracing down Covid-19 positive users and close contacts.
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    COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period
    (IEEE, 2022-02-23) Wickramarathne, J; Perera, D; Kanchana, B. C; Peiris, R
    This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months.
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    Individualized Edutainment and Parent Supportive Tool for ADHD Children
    (IEEE, 2020-12-10) Thennakoon, A; Perera, D; Sugathapala, S; Weerasingha, S; Samarasinghe, p; Dahanayake, D; Piyawardana, V
    Attention-Deficit/Hyperactivity Disorder (ADHD) is a comorbid disorder that can impact a child and his/her family. ADHD children have considerable obstacles in managing time, understanding instructions, and paying attention to the activities. To address these perplexities, this research has designed a mobile application to help parents to have better interaction with the children and for the children to enjoy their learning activities. The specialty of this application is the models are trained on individual child skills and needs. Issues with time management are handled by the Scheduler component while the Instruction Predictor module supports the parent in recognizing the child's understandability level. Furthermore, the children are provided with edutainment activities based on their attention and ability levels. Different models have been used in predicting the results through these modules and the prediction result accuracy exceeds 90% in most of the cases. Out of the many models, The Random Forest model resulted in the best overall performance. The application was tried by many parents and health professionals and received satisfactory and commendable reviews.