Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1628
Title: Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant
Authors: Jayalath, A.D.A.D.S.
Nadeeshan, P.V.D.
Amarawansh, T.G.A.G.D.
Jayasuriya, H.P.
Nawinna, D. P.
Keywords: Machine learning
Image Processing
Neural Network
Ayurveda
Sinhala
Plant identification
Virtual Assistant
Issue Date: 5-Dec-2019
Publisher: 2019 1st International Conference on Advancements in Computing (ICAC), SLIIT
Series/Report no.: Vol.1;
Abstract: Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don’t have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user’s questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.
Description: Date of Conference: 5-7 Dec. 2019 Date Added to IEEE Xplore: 29 May 2020
URI: http://rda.sliit.lk/handle/123456789/1628
ISSN: 978-1-7281-4170-1/19
Appears in Collections:1st International Conference on Advancements in Computing (ICAC) | 2019

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