Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

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Subject: search.filters.subject.Management Platform

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    Decentralized Property Registration and Management Platform
    (IEEE, 2022-12-09) Yasas, R. M; Bandara, M. H. M. N. D.; Praveena, T.; Abeywardena, K.; Kasthurirathna, D.
    The existing property registry management does not have a well-defined protocol for verifying and validating transactions that occur within the domain. These transactions rely on handwritten signatures, an unreliable methodology for determining an asset’s ownership. The legal system governs this process. However, several disputes have occurred due to improper validation and verification when registering properties, changing custody, and maintaining the chain of ownership. Trades have been made by including a lower value than the actual asset value, which will reduce the tax owed to the government and will lead to the failure of these departments. There are no appropriate mechanisms to resolve common disputes that arise within the domain. The courts must resolve these disputes using the same recurring traditional procedure, which will take years or decades to conclude. The main objective of this research is to develop a secure property registration mechanism by creating a digital protocol using a decentralized blockchain network. In addition, the research will focus on developing a minimum asset value calculator using machine learning and geographic information system, verifying the authenticity of the generated digital documents, and creating digital deeds for new and old paper-based records.
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    Arogya-An Intelligent Ayurvedic Herb Management Platform
    (IEEE, 2020-10-15) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, I
    Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.
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    Invasive Plant Detection and Management Platform
    (IEEE, 2020-06-09) Kasthurirathna, D; Lokuge, K; Mendis, R; Galagedara, L; Vijekumar, K
    Invasive plants are a threat to natural ecosystems. Sri Lanka's rich biodiversity is severely affected by the uncontrollable spread of this foreign flora. The authorities involved in invasive plant management face various challenges when identifying, monitoring, and as well as forecasting the spread of invasive plants. Another challenge faced by the field members is the difficulty in communication during the eradication process. The proposed solution is a software platform that aims to solve the above problems using mobile and web-based technologies. The mobile application is capable of identifying invasive plants, mapping their geographical location, and off-the-grid communication. The web application provides services to monitor and forecast the potential spread of harmful non-native plant species.