Research Papers - Dept of Information Technology

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Author: search.filters.author.Wickramasinghe, L

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    Student Teaching and Learning System for Academic Institutions
    (IEEE, 2022-07-18) Kumarasiri, A. D. S. S; Delwita, C. E. M. S. M; Haddela, P. S; Samarasinghe, R. P; Udishan, R. P. I; Wickramasinghe, L
    In today's global online environment, automation is essential for establishing a competitive advantage. Conversational Artificial Intelligence Systems are an example of an automation technology that has been embraced by some of world's most famous companies. In the field, higher education, these handy gadgets come in handy for administrators.Students' responses and performances are highly important and desired areas to improve the teaching-learning environment in the education organism. Evaluate the feedback to aid in the identification of flaws and actions. Institutes and universities in the field of education collect both quantitative and qualitative responses in order to improve the teaching-learning environment. However, most educational institutions and universities lack a sufficient student feedback evaluation system for determining students' feelings. The grading technique is currently being utilized to collect input. However, the grading process does not reveal the students' genuine feelings about the educational system, whereas written feedback allows students to describe specific areas and situations. Sentiment analysis is a type of qualitative feedback analysis that is based on records. The Nave Bayes (NB) classifier, Support Vector Machine (SVM), and Random Forest were employed in the majority of recent machine learning-based Sentiment Analysis prototypes. When it comes to measuring performance, most people utilize the kids' average grades, however this is not an accurate way. Because students can benefit from the experience indefinitely.This proposal offered a method for analyzing the sentiment of students' responses by combining Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence algorithms (AI). The goal is to combine machine learning algorithms and natural language processing approaches to achieve high accuracy. In this proposal, I suggested a collective model that connects machine learning algorithms to improve accuracy and performance. At the end of the presentation, AI planned to collect student responses in real time. The proposed method analyzes student comments from course reviews to determine mood, emotions, and satisfaction vs. displeasure. The approach categorizes sentimentalities into two categories: positive and negative, and senders' feelings into eight categories (8 emotion groups). The Fuzzy logic technique will be used to assess student performance. We intend to make our program available for broad use in the education industry so that organizations can improve the teaching-learning environment's quality.
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    Real time deception detection for criminal investigation
    (IEEE, 2019-10-08) Lakshan, I; Wickramasinghe, L; Disala, S; Chandrasegar, S; Haddela, P. S
    Deception Detection System (PREDICTOR) is a solution to support the criminal investigation process by providing a technological analysis in justifying the guilt of an accused criminal in the investigation process. This study gives guidelines to substantiate decision making in the interrogation. In judicature, the importance of a platform that is capable of analyzing the genuineness and the (a) reliability of a lie and a truth, (b) emotion of the suspect and the (c) attentiveness has been recognized for a long period. The feasibility of using Machine Learning (ML) techniques to build such platforms has been explored before. However, no known platform could identify the suspect's authenticity, emotion, and attentiveness. The goal is to analyze the brain waves and build a real-time deception detection application to analyze lie/truth, emotion and the attentiveness, which will support the investigation process in a wide range of angles to decision making. Electroencephalogram (EEG) based real-time lie detection, emotion detection, and attention detection will be implemented using ML tools and techniques along with the help of special hardware equipment called MUSE 2 headband. Especially this equipment is required for the data acquisition as well as the creation of the final application. The outcome of this system is a solution to be used during the criminal investigation process as a deception detection system for lie, emotion and attentiveness of the suspect. This is more effective in the questioning process to get an idea of the suspect. This system will have a major impact on the Police Department, Criminal Investigation Department, and Judicial System to ensure the real criminal and reduce the workload of Criminal Investigation officers.