Research Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194

This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

Browse

Filters

2020 - 20265

Advanced Search

Filter by

Settings

Start date: 2020Subject: search.filters.subject.Gamification

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemEmbargo
    CodeHarbor: A Code Analysis Tool
    (Springer Science and Business Media Deutschland GmbH, 2026) Dewmin T.Y; Kodithuwakku Y.S.; Dayananda I.H.M.B.L; Fernando K.R.A.W; De Silva D.I; Rathnayake S.
    As software systems grow more complex, developers face increasing challenges in maintaining and evolving codebases, often resulting in higher costs and longer development cycles. To address these issues, this study presents CodeHarbor, an intelligent tool that integrates machine learning with code analysis to simplify complex code segments. CodeHarbor calculates complexity metrics and offers personalized, context-aware suggestions for improving code quality. By automating code reviews, detecting anomalies, and recommending optimized refactoring strategies, it enables early issue resolution and enhances maintainability. The backend leverages artificial intelligence to identify patterns, enforce coding standards, and generate actionable insights, while the intuitive frontend provides real-time feedback, visualizations, and detailed improvement summaries. CodeHarbor also highlights repetitive patterns and compliance issues, helping developers track progress and reduce manual review effort. With its seamless integration of analysis and interface, CodeHarbor streamlines development workflows and promotes sustainable, high-quality software engineering.
  • Thumbnail Image
    PublicationOpen Access
    Fitness Warrior: Fitness and Nutrition Tracker with Personalized Goal Generation
    (SLIIT City UNI, 2025-07-08) De Mel, Y.D; Nallapperuma, P.M
    Fitness Warrior is a comprehensive mobile fitness tracking application developed using React Native and Firebase that addresses critical limitations in existing solutions through the innovative integration of machine learning, gamification, and social features. Traditional fitness applications suffer from inaccurate step detection (with error rates exceeding 20% error rates), inefficient nutrition tracking interfaces, poor user retention (with 73% abandonment within three months), and a lack of adaptive personalization. This project uniquely implements ondevice machine learning via TensorFlow.js for privacypreserving step detection, combines TF-IDF vectorization with cosine similarity for efficient food searching, and incorporates principles of Self-Determination Theory through a cohesive social motivation framework. Development followed the Agile Scrum methodology, implementing a CNN-based model processing sensor data at 50Hz sampling rate, creating a database of 2,395 food items with optimized search algorithms, and designing gamified social features. The application achieves 95.2% real-world step counting accuracy compared to manual counting, significantly outperforming conventional threshold-based approaches (48.3% accuracy), while the calorie tracker delivers 92.7% relevant results in top-5 suggestions with 126ms search latency. Evaluation with 21 users demonstrated exceptional impact: 95.3% reported increased daily steps, 90.4% experienced greater calorie intake awareness, and 71.4% found social features strongly motivating. The application received outstanding approval with 90.5% of testers rating overall satisfaction at 8 or higher on a 10-point scale. This research successfully demonstrates how integrated, machine learning-enhanced fitness applications can meaningfully impact user health behaviours while overcoming significant limitations in existing solutions.
  • Thumbnail Image
    PublicationOpen Access
    Agent-Based Gamified Learning Environments for Data Science Education
    (SLIIT Business School, 2023-12-14) Jayalath, N; Rajapakse, C
    Because of the rapid advancement of technology and the increasing importance of the inferences that can be drawn from the big data available in organizations, modern organizations require managers and data Analysts who are capable of data-driven decision-making. But data science students need a natural environment when it comes to learning data-driven decisionmaking, especially when it comes to predictive and prescriptive analytics. Due to costs and other associated risks in a natural organisation setting, it is hard for educational institutions to teach these aspects of decision-making for data science students. Even Though gamification has been implemented in the data analysis domain in various forms, the field still requires a suitable environment to learn predictive analytics interactively for the students. Even though Researchers have identified that Gamified learning environments can improve Predictive analytics learning can be improved by 15.8%, still there is the lack of proper implementation of a suitable gamified learning environment. This research focused on identifying drawbacks of existing learning environments and whether Agent-Based Modeling can be used in modelling a suitable gamified learning environment. Therefore, an agent-based prototype model of a parameterized environment that enables data-driven decision-making in a simulated environment was modeled using Agentbased modeling, which depicts real-life donor interactions. Results suggest that fill in blanks This Agent-based model can be used as a learning environment for data analysis. Upon further modification, A game that applies this Agent-based model can be developed.
  • Thumbnail Image
    PublicationEmbargo
    STEP UP: Systematically Motivating the Children with Low Psychological Maturity Level and Disabled Children using Gamification and Human Computer Interaction
    (IEEE, 2022-07-18) Dharmarathne, R. S. C. K; Medagedara, K. A; Madhubashinee, N. B. W. N.; Maitipe, P. T.; Sriyaratna, D
    Children are the future of this world. Therefore, teaching them to have a better future is very important. Also, as the adults we have to motivate them to overcome the obstacles and challenges they face throughout their lifetime. When considering about the children, there are various types of children in our society. As examples there are children with special needs and there are children who are mentally and physically stable. Children with special needs require special attention than the other children. These kinds of children with special needs have various types of development disabilities. They are children with low psychological maturity level, autism, down syndrome, genetic disorders etc. We have proposed a system to motivate these children who are with low psychological maturity level named as ‘STEP-UP’. This system is a combination of four individual modules that have the common goal to motivate these kinds of children which were implemented using gamification, Image processing, machine learning and Human Computer Interaction. One individual module is focused on disabled children, and it will motivate those children using gamification. Another two modules are focusing on the children who are with low psychological maturity level. And that will motivate those kinds of children using gamification and HCI based technologies like Virtual Reality. The other module is a protocol to secure the data sent between the system and the database. The common goal of this overall STEP-UP system is to motivate the children with low psychological maturity level and disabled children.
  • Thumbnail Image
    PublicationEmbargo
    Amazon Biology: An Augmented Reality-Based E-Book for Biology
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Somakeerthi, D.C.S.; De Silva, G. W. I.U.; De Silva, L.D.T.; Chandrasiri, S.; Joseph, J.K.
    Biology is a conventionally struggling subject to learn from both high school and college students due to its complexity. Students are used to learning Biology from various methods such as reading textbooks, attending lectures. Biology is based on more practical and most of the schools not available proper lab facilities, anatomic structures, and resources to learn the module easily. And teachers who teach the module face a considerable number of issues when delivering the concepts. Some of them face unavailability of teaching aids, time-consuming, lack of lecture materials. Apart from that, the nature of the topic and the teaching style are the main learning problems faced by the students. Therefore, students do not learn the concepts perfectly and interest in the module has been reduced day by day. To overcome these difficulties “Amazon Biology,” mobile application has been proposed. The application consists of three major modules including image processing for the plant classification, augmented reality for human anatomy, and gamification. The proposed application has used the techniques in augmented reality and game-based learning. The developed system delivers nearly 85% level of accuracy and provides more advantages for students. They are effective and efficient learning, teaching via visual materials, and practical.