International Conference on Advancements in Computing [ICAC]

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The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

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Author: search.filters.author.Fernando, G.U.D.

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    Smart Personal Intelligent Assistant for Candidates of IELTS Exams
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Senevirathne, S.S.; Fernando, G.U.D.; White, J.B.; Divyanjala, S.T.H.; Samaratunge Arachchillage, U.S.S.; Dias, D.P.N.P.
    Many IELTS candidates encounter problems at the examinations and majority of them are unable to achieve their goals even though they strive hard to accomplish their targets. Candidates strive to achieve higher band score in exams, but fail to achieve them due to the ignorance of prevailing weaknesses which have to be identified if they were to succeed. At present, IELTS seems to be the most demanding exam among applicants who are planning to embark their higher studies or migration purposes. Currently, there is no proper mechanism to assist candidates and generate an improvement plan by identifying the weaknesses of them. As a solution, Smart Personal Intelligent Assistant for Candidates Exams (SPIACIE) has been proposed to detect IELTS candidates’ weaknesses through an analysis of their answers. The SPIACIE assesses four components (Reading, Writing, Listening, and Speaking) in IELTS exams. This paper is specifically based on the Long Short-Term Memory (LSTM) network model used to analyze the score of grammar and cohesion. To analyze the similarity of the sentences, the cosine proximity technique is proposed to evaluate the paraphrasing of the graph explanations. The final outcome of this application is to generate an improvement plan, developed using Machine Learning (ML) algorithms. The proposed algorithms are; Gaussian naïve base for reading exam, support vector machines for listening exam, decision tree classifier for speaking exam, and k-neighbors classifier for writing exam. An improvement plan on the prediction model is provided to increase the band score of the IELTS exams, based on applicants’ weakness.