4th Annual Research Conference of SLIIT CITY UNI

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4160

Browse

Filters

20253

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Machine Learning

Search Results

Now showing 1 - 3 of 3
  • 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
    Smart Chat: A Mobile Chat Application Based on Machine Learning
    (SLIIT City UNI, 2025-07-08) Yathuraj, G; Abeysinghe, A
    In an increasingly digital world, communication is primarily conducted through messaging apps, but these platforms cannot often convey emotional nuance. This limitation can lead to misunderstandings, emotional disconnects, and deteriorating relationships. SmartChat addresses this gap by integrating machine learning-based emotion recognition into a mobile chat app, allowing users to send and receive voice messages enriched with emotional context. Built using React Native and compatible with both Android and iOS, SmartChat analyzes voice cues such as tone, pitch, and cadence to detect and display emotions to the user. This innovation improves the clarity and empathy of conversations, making digital communications more humancentered. Beyond general messaging, SmartChat has the potential to be used in critical contexts such as education, mental health support, and emotional literacy. By making emotionally aware communication accessible across languages and cultures, SmartChat contributes to fostering healthy interpersonal relationships and supports the broader goal of social sustainability through technology.
  • Thumbnail Image
    PublicationOpen Access
    AI-Driven To-Do List: Optimizing Task Categorization and Prioritization Using Ensemble Models
    (SLIIT City UNI, 2025-07-08) Vishaliney, P.; Pemasiri, C.S; Kanthakumar, M; Yatigammana, N
    This paper introduces the AI-driven smart todo list that can cluster and prioritize activities by using the machine-learning methods. Traditionally, to-do list services are immovable and have an element of compromising users to input the information themselves; this sort of bare tool can easily lead to unproductiveness in accomplishment of duties. To address this situation, we supplement ensemble modeling, namely Logistic Regression, XGBoost, and Multilayer Perceptron, to delegate the tasks to the desired categories and define priorities by their urgency. Measured based on standard measures, the ensemble will achieve 47.7 percent accuracy when doing classification and 72.8 percent when predicting priority, and High Priority tasks will gain in this evaluation. Using BERT-based embeddings in combination with TF-IDF-based vectorization, the system should improve its effectiveness because it understands the semantics of described tasks. Together these blocks form a superb ensemble architecture that can beat stand-alone model when it comes to classification and forecasting. More importantly, the system still leaves itself potential to adjust to user behavior and therefore it can improve task management, and it is a feasible platform in real time organization of tasks.