Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Silva, C.Has files: Yes

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    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.
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    Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students
    (2021 3rd International Conference on Advancements in Computing (ICAC) -SLIIT, 2021-12-09) Gamage, M.A.; Matara Arachchi, R.; Naotunna, S.; Rubasinghe, T.; Silva, C.; Siriwardana, S.
    Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works.