Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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Author: search.filters.author.Krishara, JHas files: Yes

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    IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application
    (IEEE, 2024-04-05) Bodaragama, B.D.T; Miyurangana, E.H.A.D.M; Jayakod, Y.T.W.S.L; Vipulasiri, D.M.H.D; Rajapaksha, U. U. S; Krishara, J
    This research project seeks to enhance fish tank management and improve the well-being of aquatic life by leveraging modern technological solutions. It focuses on four key areas: monitoring water quality, detecting fish diseases, preventing algae growth, and developing an automatic fish feeder with remote control capabilities. The project’s first goal is to establish a comprehensive water quality monitoring and control system that predicts future water conditions, continuously assesses key parameters, and provides real-time data to users for proactive interventions. Additionally, the research project aims to develop an image-processing-based mobile application for early detection of fish diseases, eliminating the need for manual inspection and improving overall fish health management. The project also involves the creation of a mobile app to predict and prevent algae growth by analyzing factors like lighting, nutrient levels, and water flow, providing personalized recommendations for algae control. Lastly, an automatic fish feeder with remote control capabilities will be designed, allowing fish owners to schedule and adjust feeding times and portion sizes through a mobile app. This innovative approach ensures fish receive consistent and appropriate nutrition even when owners are away from home.
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    Face Skin Disease Detection and Community based Doctor Recommendation System
    (IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, D
    In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.