Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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    Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach
    (IEEE, 2022-10-04) Nizzad, A.R. M.; Thelijjagoda, S
    Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.
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    Enhanced algorithmic implementation to assist real-time indoor map generation for vision impaired individuals
    (IEEE, 2018-08-08) Jayakody, A; Murray, I; Hermann, J; Lokuliyana, S; Dunuwila, V. R
    The complexity of indoor environments has made navigation difficult for vision impaired individuals as well as individuals with clear vision. Although handheld mobility devices have been developed to assist the vision impaired in navigation, they are incapable of capturing parameters such as distance, angle and direction. This paper presents an appraised framework; the Accessible Building Information Model (AccessBIM), which could be used for generating an indoor map in real-time with the classification of real world objects and their locations. The AccessBIM database is equipped with two optimization algorithms; a database optimization algorithm that reduces the time of query execution through indexing, query re-writing, schema redesigning and a memory optimization algorithm known as “Memcache”. Five scenarios were tested using a simulator to determine the accuracy of the map that is generated. The use of the two algorithms ensured that the real-time map generated through the data collected from the simulation environment was similar to the actual floor plan. Hence, it can be concluded that the AccessBIM framework has the potential to play an integral role in assistive technologies related to localization and mapping, thus significantly improving the quality of life for individuals with vision impairment.