MSc in Information Technology

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Open Access
Development and Integration of an AI-Driven PHP Adapter for Automated Mathematical Question Classification and Assessment: Enhancing Student Profiling and Feedback Mechanisms
(SLIIT, 2024-12) Nishamali, M.K.C.P.
The transformative growth of AI can be seen in almost every sector. AI can be a useful application for the educational domain as well. This research aims to combine IT to develop mathematics subjects by leveraging AI in practice mainly introducing Capabilities of Open AI. The primary objective is to create OpenAI API through a specially created PHP adapter to classify mathematical questions into six main themes Sets and Probability, Algebra, Numbers, Geometry, Measurements, and Statistics. This automated AI-driven classification system helps to create online assessments within the blink of an eye. The Integration of Open AI API with a PHP-based framework makes a bridge between AI capabilities and education needs. This framework is the ideal solution for manual and traditional school assessments. This plugin can be implemented in other university-level courses as well. The sample of the adapter plugin is only created and tested for secondary school mathematics classes for grade 10. This AI-driven mathematics classification system is designed to optimize the assessment process by providing additional objectives such as leveraging automated student grading feedback so teachers and students can see the result instantly. Additionally, answers are automatically generated after the assessment, displaying the solving steps that help students identify their mistakes. Meanwhile, this system also predicts the student’s mathematics pass mark based on the results of the tests taken from this system.
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Decision Support System for Overcoming the Challenges in Vocational Education in Sri Lanka
(2021) Lakshani, J. K. A. M.
The vocational education is undergoing continuous changes. In the past, high youth unemployment has taken place due to unfamiliarity with vocational education. Researchers and policy makers are paying attention to the vocational education because of the hidden importance of the vocational education. In Sri Lanka, there is a vocational education system as the 13 years mandatory education system. The project is going to discover the challenges of the vocational education and give some solution to enhance the effectiveness of vocational education using the sample scenario of the professional entry. There are several issues in vocational education system. Among them, the major challenge is the lower rate of successfully completed students than commencing students. The main objective of this research is to develop a Data-driven decision support system to mitigate the students’ dropouts from vocational education using deep learning model with higher level of accuracy rate than previous systems. Accurate data collection helps to maintain the integrity of the research in any field. The project has collected real data set from the students and teachers in selected government schools in Sri Lanka. Data has collected mainly in three categories as demographic factors, academic performance and candidate interest. Collected data has analyzed according to the data analysis techniques. Decision support system has used machine learning model to predict the suitable vocational education pathways to the students. The model has used deep neural network (DNN) with PyTorch library. After training the model, the model has predicted the accuracy level as 96.06%.
Embargo
Biomedical Waste Sorting & Classification Using Deep Learning
(2021-05) Ahmed Akmal, M. A
Biomedical wastes (BMWs) include potentially infectious, sharps, pharmaceuticals and radioactive wastes probably generated by hospitals, vaccination centers, biomedical laboratories, etc. Handling and disposal of biomedical wastes potentially have multiple risk factors. Currently, hospitals and laboratories use color-coded bins to classify and categorize different types of wastes to ease the handling and the disposal process. Sometimes due to human errors these wastes could be miscategorized or misplaced in different bins. In recycling terms this is known as waste contamination. Contaminating the biomedical waste streams causes a huge potential threat to the people who handle them. Computer vision based biomedical waste classification is one of the best ways to prevent these issues. But applying pure computer vision algorithms is much more suitable for small tasks such as pattern recognition, edge detection etc. In order to classify different kinds of biomedical wastes, then convolutional neural networks (CNN) would be a much more suitable choice. This research proposes a deep learning model which accurately classifies several selected biomedical wastes such as syringes, blades and sample collection tubes with a prediction accuracy around 96% on the test dataset. Further the implemented model approximately localizes the biomedical wastes to serve robotics and smart-bin applications.

MSc in Information Technology

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