Symposia and Conferences
http://repository.kln.ac.lk/handle/123456789/15606
2024-03-29T15:19:15ZUse of a ChatBot-Based Advising System for the Higher-Education System
http://repository.kln.ac.lk/handle/123456789/27852
Use of a ChatBot-Based Advising System for the Higher-Education System
Radhakrishnan, Hansi K; Dias, N. G. J.
The educational advising process most often consists of repeated queries related to institutional policies, academic progression, career pathways, and industry placements. In the higher education system, this procedure is usually initiated by various learners directing the same questions toward a limited number of advisors, which results in the advising process being reliant on the availability of the individuals and their hectic work schedules. Hence, this study introduces a feasible mechanism of a chatbot-based advising system to bridge this identified gap between learner requirements and resource availability by automating the prescriptive advising process beyond the traditionally available methods. Most existing systems provide a rule-based approach with limited pre-defined intent-response structures, resulting in several identified usability shortcomings. In response, this study utilizes an opensource Large Language Model (LLM) combined with a custom knowledge base to address critical aspects needed for a chatbot-based advising system, such as personalization, conversational memory, and ease of maintenance. The system is built around three major components: an admin panel for advisors, a conversational user interface (CUI) for learners, and an easy-to-maintain custom knowledge base. It uses the traditional form of information distributed to students through handbooks, guidelines, and course outlines to create a custom knowledge base which is then utilized to answer the user's queries through a semantic similarity algorithm. This work contributes (1) a prototype of a chatbot-based advising system for higher educational institutes in Sri Lanka, (2) the application of Large Language Models, vector databases, and semantic similarity in the design of the system, and (3) the results of evaluating the system's functionality and performance metrics through comprehensive test cases and a comparative analysis against the existing approaches. As identified, the proposed system showcased a response accuracy rate of 89% proving that this novel approach of a component-based architecture excels in performance when compared to similar approaches.
2023-01-01T00:00:00ZUsability of the Fuzzy Logic-based Visual Impairment Level Identification System for Preschoolers and Toddlers
http://repository.kln.ac.lk/handle/123456789/27851
Usability of the Fuzzy Logic-based Visual Impairment Level Identification System for Preschoolers and Toddlers
Sammani, MHK; Perera, MPL; Vidanagama, DU
Nearsightedness, or myopia, and Colorblindness, the two common eye diseases, can affect preschoolers and toddlers. This research is to provide parents with a method for testing the two eye impairments listed above in children who are illiterate in both letters and numbers. Using the knowledge offered by ophthalmologists, comments from parents with young children of survey findings, and pertinent literature, this is to create a mobile gaming application based on Fuzzy Logic, that could evaluate the level of children's Colorblindness and Nearsightedness. The "Ishihara test" and "Hue test," which are still widely used today, can be used to identify color blindness by selecting hues from a color palette that have a similar color intensity, and by allowing children to choose images that range in size from large to small (follow the Snellen Chart), and Preferential Looking Test concept that parents can determine whether their child has nearsightedness based on the child's outward behavior. Also, a usability test has also been done at the current level of development of this app. This mobile gaming application roughly identifies the level of the above two eye defects in young children and refers to medical advice if there is a certain risk level. This research paper mainly considers the current development of this mobile gaming app and its usability.
2023-01-01T00:00:00ZReview of Uncommon Power Generation Methods
http://repository.kln.ac.lk/handle/123456789/27850
Review of Uncommon Power Generation Methods
Perera, Poornima L; Edirisinghe, Nishad M; Hettige, Budditha
Fossil fuel shortage is a common problem in every country. Day by day it going to discreet also fuel prices going to increase. When burning fossil fuels have made a few problems. The main problem is environmental pollution. Considering the current rate of usage of fossil fuels will let its life up to the next five decades only. Most countries referred to the use of renewable energy. Wind power, Solar power, sea wave power, Hydropower, and Geothermal heat are a few of the renewable energy they have used. Also, few countries have used nuclear power plants, charcoal power plants, and diesel power plants. But when using charcoal or diesel power plants, it has to spend a lot of money on one unit of electricity. Nuclear power plants are not suitable for small countries like Sri Lanka. In history, the most powerful kingdoms had used uncommon and hidden power for their living area. The main objective is this review paper is to identify uncommon power generation methods and weaknesses of the existing renewable power generation system and To what extent are these suitable for Sri Lanka. Performance analysis has been carried out in this study along with the literature review.
2023-01-01T00:00:00ZPredictive Analysis on Social Media Content to Become Viral
http://repository.kln.ac.lk/handle/123456789/27849
Predictive Analysis on Social Media Content to Become Viral
Silva, Singhe; Rajapaksha, Rasika
In the continuously changing world of social media, Instagram has taken a prominent place by becoming one of the most popular social media platforms. Instagram has not only the biggest organic reach but also the highest organic engagement rate. Above all, understanding and predicting what makes posts go viral is an uneasy yet significant challenge. This study focuses on Instagram and presents a fresh approach to discovering the key factors contributing to post virality, specifically on image posts. In this study, a metric named ‘Virality Rate’ is defined to predict the likelihood of going viral. It is calculated by dividing the sum of number of likes and comments by the number of followers. There were studies on Instagram post popularity prediction based on various features and datasets. But with a focus on public image-based posts from influencers worldwide, this research delves into sentiment analysis, image processing for technical features and content, hashtag assessment, user history and user features to forecast the potential virality of a post. This research trained and compared several regression models to predict the Viral Rate and employed Faster R-CNN and OpenCV to detect objects and help extract essential technical details. Through rigorous model training and evaluation, our results highlight the Random Forest Regression model as the most effective predictor. It boasts an impressive Mean Absolute Percentage Error (MAPE) of 0.15, which implies an accuracy of 85% and a notable R-squared (R2) value of 0.924 which is significant compared to previous studies. It was found that the User History Features, sentiment score, technical features and posting time have a high impact on Virality Rate. In conclusion, this research aims to advance social media analytics by offering actionable insights for content creators, influencers, marketers and regular users.
2023-01-01T00:00:00Z