Research Papers - Dept of Computer Systems Engineering

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Subject: search.filters.subject.Monitoring System

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Now showing 1 - 4 of 4
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    PublicationEmbargo
    IoT-based Monitoring System for Oyster Mushroom Farming
    (IEEE, 2021-12-09) Surige, Y. D; Perera, W. S; Gunarathna, P. K; Ariyarathna, K. P; Gamage, N; Nawinna, D. P
    Agriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.
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    PublicationOpen Access
    IOT-based Monitoring System for Oyster Mushroom Farms in Sri Lanka
    (KDU IRC, 2022-01-10) Surige, Y. D; Perera, W. S. M; Gunarathna, P. K. M; Ariyarathna, K. P. W; Gamage, N. D. U; Nawinna, D. P
    Oyster Mushrooms are a type of a fungus which is very sensitive to the environmental factors and vulnerable to diseases and pest attacks which directly effects local trade and export strength. Mushroom is a climacteric type of food which continues its cycle even after harvesting. The mushroom farming process still uses manual mode such as the identification of diseases uses a farmers eye visually, harvesting of mushrooms are decided based on the visual appearance while the environmental factors are decided based on gut feelings. These methods has its limitations which requires more potential to improve both the quality and capacity of mushroom production. With the advancements of technology, this farming process can be performed with the aid of an IoT device and deep learning model. This research applies Convolutional Neural Networks (CNN) with Mobile Net V2 model to detect mushroom harvest time and any disease spread with an accuracy of 92% and 99% respectively. Long Short-Term memory (LSTM) to analyze the detected environmental factors with an accuracy of 89% and this system predicts the yield of mushroom production with the support of LSTM model with an accuracy of 97%. This developed system which aids mushroom farming activities is connected with the farmers through s mobile application
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    PublicationEmbargo
    AI Based Monitoring System for Social Engineering
    (IEEE, 2021-12-09) Abeywardana, K. Y; Udara, S. W. I; Wijayawardane, U. P. B; Kularatne, K. N. P; Navaratne, N. M. P. P; Dharmaphriya, W. G. V. U
    Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.
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    PublicationEmbargo
    DiabiTech-Non-Invasive Blood Glucose Monitoring System
    (IEEE, 2019-12-05) Udara, S. S. W. I; De Alwis, A. K; Silva, K. M. W. K; Ananda, U. V. D. M. A; Kahandawaarachchi, K. A. D. C. P
    Diabetes is one of the largest chronic diseases threatening 84 million patients in South East Asia alone. By the year 2045, this is expected to rise to 156 million. Lack of a permanent cure and impractical, expensive, painful measuring techniques are among the reasons behind these alarming statistics. Almost 58% of diabetes patients are not diagnosed in this South Asian region, mainly due to lack of motivation caused by the above issues. This research focuses on finding a solution, which is non-invasive, portable, practical, accurate, and cost-effective with the help of sensors and accessible technology platforms. The research started upon discovering the medical classifications, parameters, contributing factors, and external dynamics that could affect the result. Upon research, an algorithm was developed to calibrate and measure the blood glucose level, which is compatible with dynamic factors of the patients. Later on, the hardware device was built using NIR and red LEDs accompanied by a user-friendly mobile application, which can be accessible by patients of all ages. The results obtained were validated by using statistical techniques. The analysis showed a strong linear correlation between the voltage output and blood glucose level. The overall accuracy of the system accounts for above 90%. Since this solution is noninvasive, it provides a reusable and portable platform which can constantly monitor blood glucose levels conveniently in a painless manner, without any repetitive costs. Further, this solution will help patients to adjust medication based on their current blood glucose levels to reduce both the unnecessary damage of organs and additional costs being incurred.