Faculty of Computing

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4202

Browse

Filters

20212

Advanced Search

Filter by

Settings

Author: search.filters.author.Kuruppu, T.A.Has files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    Automated Sinhala News Platform Based on Machine Learning and Deep Learning
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Malsha, N.P.G.A.; Heshani, K.D.; Ransara, R.K.; Kumari, P.k.S.; Kuruppu, T.A.
    Over the years, newspapers are taken as a major activity in citizens. Due to digitalization, online newspapers get famed, and eventually, it seems to be an ideal adaptation for their busy lifestyle. While there are several Sinhala News platforms, nonetheless, provide an advance and better user experience similar to English news platforms like google news. To fill this gap the proposed news platform, dispense personalized news recommendation based on user behaviors and user emotions. To replace the manual news categorization, the proposed system is armed with automatic news categorization. An automatically unnecessary comment removing feature was also added to the proposed system as an extra feature. The system implementation is based on deep learning, machine learning, NLP, sentimental analysis, and image processing techniques where it can provide a better user experience and a new experience.
  • Thumbnail Image
    PublicationEmbargo
    Auto Training an AI for Detecting Plant Disease Using Twitter Data Annexed With a Plant Anthology
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Vasanthan, N.; Shimran, Mohamed; Ahkam, A.; Ishak, I.; Silva, C.; Kuruppu, T.A.
    Agricultural productivity plays a vital role in contributing to a nation’s economy. Farmers nowadays are concerned due to disease persistence in crops and plants, and it also affects the economy indirectly, so it is important to come up with a solution to detect plant diseases and educate the farmers about the solutions to retaliate against the diseases. Proper care is mandatory to safeguard the quality of plants. The existing traditional methods consume a massive amount of time and resources hence, it’s costly. Due to the importance of continuous monitoring, it seems impractical for a farmer to implement the traditional methods on large scale. The Traditional systems which are used lack the ability to identify diseases out of their predefined scope. As a solution, we came up with an autolearning system that identifies new plant diseases and provides remedies. This paper showcases the image processing techniques to detect plant diseases, Auto ML techniques to create new models for plants and corresponding diseases, Diseases are identified using image processing, Remedies are extracted for the given plant diseases using unstructured data from web data crawling. The business intelligence model uses NLP to provide ideas about the trending plants and plantrelated diseases are also discussed in this paper.