Research Publications

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Author: search.filters.author.Thelijjagoda, SSubject: search.filters.subject.Machine Learning

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    WORDEX: Early Dyslexia Detection and Support
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ganegoda, S.H; Dissanayake, O; Samarakoon, S; Jayawardana, N; Thelijjagoda, S; Gunathilake, P
    Dyslexia is a prevalent and complex learning disability that affects approximately 5% of primary school students worldwide. It often manifests as persistent difficulties in reading, writing, spelling, and overall academic performance, which can lead to long-term educational and psychological impacts if not addressed early. To facilitate the early identification and support of dyslexic learners aged 7 to 10, this paper introduces Wordex, an innovative and adaptive educational platform. Wordex is designed to screen for multiple dyslexia subtypes and provide targeted interventions through engaging, interactive, and personalized learning activities. The platform features an integrated machine learning-based screening system that analyzes user interactions and performance metrics to assess the risk of dyslexia. Upon identification, the platform delivers tailored remedial exercises that align with national school curricula, aiming to strengthen specific cognitive and linguistic skills. Wordex is developed using a modern technology stack including Spring Boot, Flutter, Python libraries, Firebase, and MongoDB, and incorporates capabilities such as image processing, supervised learning algorithms, real-time progress tracking, and cloud-based data management. A user-centered design approach and iterative testing cycles were employed to ensure the platform is accessible, intuitive, and pedagogically effective. Wordex contributes significantly to the field of educational technology by offering a scalable, research-informed intervention tool. Future enhancements include multilingual support, broader age group coverage, and integration with classroom learning environments.
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    "articulearn": An Integrative, AI-Driven Speech Therapy System for Children With Speech Disorders
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ranasinghe, K; Zoysa, S.P.D; Annasiwatta, S; Fernando, P; Thelijjagoda, S; Weerathunga, I
    "ArticuLearn", a personalized speech therapy system for children with speech sound disorders that integrates advanced machine learning techniques and interactive digital tools to provide targeted intervention across four key domains: phonological disorder detection, fluency disorder identification and intervention, therapy for childhood apraxia of speech, and personalized speech activity filtering for articulation disorders. By leveraging dedicated LSTM-based classifiers and feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs), this approach automatically identifies specific error types, including phoneme substitutions, omissions, and vowel mispronunciations. In addition, a hierarchical deep learning framework employing attention mechanisms and dynamic time warping is applied to quantify motor planning deficits associated with childhood apraxia of speech, while a reinforcement learning agent adapts therapy prompts based on individual performance. Data were collected from eight children per disorder category along with a normative sample of twenty typically developing children, providing a basis for personalized intervention and progress monitoring. ArticuLearn is designed to complement traditional therapy methods by offering an accessible, scalable solution that supports remote intervention and enhances clinical decision-making. Pilot evaluations suggest that the system can facilitate targeted speech exercises, improve self-monitoring, and foster adaptive learning in young users. This research underscores the potential of combining AI-driven analysis with interactive therapy to transform speech rehabilitation, particularly in resource-limited settings where access to specialized care is challenging.
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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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    Snap & Hear: Comic Book Analyst for Children Having Literacy and Visual Barriers
    (CSEDU 2020 - 12th International Conference on Computer Supported Education, 2020) Yapa, R. B. D; Kahaduwa Arachchi, T. L; Suriyarachchi, V. S; Abegunasekara, U. D; Thelijjagoda, S
    Comic books are very popular across the world due to the unique experience they provide for all of us in the society without any age limitation. Because of this attraction, which comic books have received, it has proved that comic literature will be able to survive in the twenty first century, even with the existence of multidimensional movie theatres as its competitors. While the biggest global filmmakers are busy with making movies from comic books, many researchers have been investigating their time on digitizing the comic stories as it is, expecting to create a new era in the comic world. But most of them have focused only on one or few components of the story. This paper is based on a research which aims to give the full experience of enjoying the comic books for everyone in the world despite of visual and literacy barriers people are having. Proposed solution comes as a web application that translates input image of a comic story into a text format and delivers it as an audio story to the user. The story will be created using extracted components such as characters, objects, speech text and balloons and considering the association among them with the use of image processing and deep learning technologies.
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    Automate Traditional Interviewing Process Using Natural Language Processing and Machine Learning
    (IEEE, 2021-04-02) Senarathne, P; Silva, M; Methmini, A; Kavinda, D; Thelijjagoda, S
    Nowadays, almost everything is equipped with technology. People can save time by using modern day technological applications in the most convenient way. Smart Interviewing System is one such software/tool which automates the traditional interviewing process using modern Natural Language Processing techniques and deep learning applications. The system will be mainly beneficial for interviewers and HR management employees working for different organizations who conduct technology related interviews. The system works with human voice and writing patterns. The system converts human language into system understandable text-based inputs, and these are used as inputs in the automated interviewing process. The system then checks the accuracy of the answers which candidates provided on the both oral interviews/ technical interviews and written tests. Later, the system automatically predicts scores for each answer using concepts of the deep learning. Interviewers can reduce the effort that they have to put in for selecting the most suitable candidates who are qualified enough to work with their organization. SIS is developed based on modern DL and NLP concepts using Python programming language alongside with ReactJS Framework. This system checking and evaluating candidate more accurately in every stage of the interview using advance evaluation parameters than human oriented evaluations. Above process lead system to find more human errors which critically can be affected to future of the organizations. Because of that, it can be led organizations to find best human resources comparing to the traditional interviewing process by sacrificing less time and effort.