Research Papers - Dept of Software Engineering

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

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    Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis
    (IEEE, 2022-12-09) Kothalawala, M.U.; Gaveshith, M.G. K; Tharaka, A.H.D.H.; Punchihewa, I.A; Sriyaratna, D
    Banana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.
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    Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies
    (IEEE, 2022-08-29) Senanayaka, S.A.M.A.S; Perera, R.A.D.B.S; Rankothge, W.; Usgalhewa, S.S.; Hettihewa, H.D
    Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API.
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    Detection and Protection Related to Data Sharing Technologies
    (IEEE, 2018-10-28) Harshanath, S. M. Buddhika
    Digital transformation enables development and the testing of available applications for customer use, done in a smart way. It is also clear that the vast amounts of available data related to business cannot be easily handled in a day without the use of these mobile resources. Yet, privacy and security factors affect the release of more data for publishing or for exchanging purposes. Open data is ruled out due to insufficient available data for research as related to mobile devices where, there is a need for real data sets for testing purposes. The Internet of Things demands stronger security mechanisms to keep attacks at bay over buildings and automobiles. However, it must not be ignored that rules and regulations require considerable time to come into effect whereas, self-control imposed by a user made aware of the risks, takes little or no time at all.