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 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, DIn 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.Publication Embargo Machine Learning to Aid in the Process of Disease Detection and Management in Soilless Farming(IEEE, 2022-07-18) Fernando, S. D; Gamage, A; De Silva, D. HThis research aims at enhancing the methods and techniques that are being used in disease detection when it comes to soilless farming. Soilless farming is quite famous among the Sri Lankan farmers farming in urban areas. A mobile application is launched by us and this application is capable of identifying diseases in plants, therefore, farmers do not have to rely on their years of experience to identify the diseases. A novice farmer may struggle to say what is wrong with their plants, while another farmer with many years of experience may say what the disease is with no hesitation. Both those types of farmers benefit from our mobile application equally. The said mobile application consists of four components and each of them focuses on a different service. One of those components is to detect and manage diseases in plant leaves and that component is what this research paper showcases. This particular component allows the user to capture live-images of plant leaves. Then the application processes the captured image to identify if the plant is suffering from a disease. After that, it generates a report with a set of treatments. It further analyses and alerts the user if this disease detected is going to affect the harvest.Publication Embargo Efficient Agricultural Sensor Network with Disease Detection(IEEE, 2019-12-05) Gunathilaka, M. D. N; Lokuliyana, S; Udurawana, A. W. G. C; Dissanayaka, D. M. A. S; Jayakody, AThe smart Agriculture concept is a new trending topic in making traditional agriculture task automation to make them more effective and efficient to suit current human requirements. With machine learning and image processing technologies those tasks are made more robust and accurate while maintaining the low cost made this research inspired to adopt Sri Lankan farmers to develop a real-time disease detection monitoring system with wireless sensor node for crops, so that would be able to harvest and store energy for battery-free operation using supercapacitors and technologies such as Maximum Power Point Tracking. The main outcomes of this nodes are to monitor the growth environment and also the crop for diseases by using image processing and machine learning techniques in order to cultivate a better fruit overall. The wireless sensor node can be adapted to be used on multiple types of remote farms. Pineapple (Ananas comosus) was selected as the test crop for the research which is a fruit grown widely in tropical countries in large fields. The texture, shape of the fruit and the taste of pineapple changes due to various conditions. The final system makes monitoring the crop for diseases a lot effective while making monitoring the growth conditions more efficient compared with what's available on the market.