Browsing by Author "Rajendran, K"

Now showing 1 - 3 of 3

Embargo
An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry
(IEEE, 2020-12-10) Mahroof, A; Gamage, V; Rajendran, K; Rajkumar, S; Rajapaksha, S, K; Wijendra, D
Education is one of the fast-growing fields in the global perspective. Advancement of technology can be used in this sector to provide an effective and a valuable education system. In general, the students are more attracted to displays rather than the textbooks. In Sri Lanka, there is an inadequacy of resources and teachers cannot provide one on one attention to the students. Sri Lanka is not equipped with any platform to self-learn or self-evaluate their performance using an application either. Fortunately, “Edubot” acts as a solution for the stated research gap by providing a self-learning and self-evaluating AI based chatbot platform for Ordinary Level students in Chemistry domain. The self-learning component will provide the students a classroom environment by providing interactive tutorials. Explanatory responses would be given by Edubot by capturing doubts raised by the students and the self-evaluating component will provide an exam-based environment in which the Edubot auto generates the question and answers. The research finding shows that each component has an accuracy of more than 70 percent and helps to achieve the main goal of increasing the resources available to the ordinary level students in the Chemistry domain. This would then lead to an increase in the pass rate of the chemistry subject in the G.C.E Ordinary Level exam.
Embargo
Revolutionalize Your Learning Experience with EQU ACCESS
(IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, K
This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.
Embargo
Revolutionizing Tamil Language Analysis: A Natural Language Processing Model Development Approach
(IEEE, 2024-07-25) Ravichandira, G; Sivabaskaran, V; Uthayakumar, T; Vyravanathan, S; Krishara, J; Rajendran, K
This study proposes a web-based platform utilizing Natural Language Processing (NLP) techniques to identify and rectify spelling and grammar errors in Tamil, a language with intricate nuances. Users can input Tamil text, which undergoes automated scrutiny for linguistic inaccuracies. Additionally, the research delves into contextual text summarization and real-time transcription of spoken Tamil. The overarching aim is to devise a holistic solution amalgamating various components to facilitate the detection and rectification of Tamil spelling and grammatical errors. The envisioned subgoals encompass a spell-checking tool capable of identifying misspelled words and suggesting appropriate replacements based on context, a grammar correction feature adept at identifying and rectifying grammatical inaccuracies while accommodating the unique grammatical structures of Tamil, a summarization component adept at condensing paragraphs while retaining core concepts, and a transcription feature enabling the real-time conversion of spoken Tamil into accurate text. By addressing the complexities of the Tamil language, this research endeavor seeks to contribute to the expansion of language processing tools. The ultimate objective is to empower users with the means to detect and rectify errors while enhancing their proficiency in spoken Tamil. This synthesis of components represents a significant stride towards the development of a comprehensive web-based platform for identifying and rectifying Tamil spelling and grammar errors.