Faculty of Computing

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Author: search.filters.author.Jayalath, THas files: Yes

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    AI Driven Smart Tourism Platform for Personalized Safe and Sustainable Travel Planning
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Srikanthan, S; Senevirathne, C; Rasarathnam, T; Jayalath, T; Rajendran, K
    Tourism planning remains challenging due to the need for group preference alignment, personalized itinerary generation, and real-time budget control, challenges that are not adequately supported by existing platforms. This paper presents an AI-driven modular framework that integrates three components: a semantic-aware group recommender that uses Sentence-BERT embeddings with a learning-to-rank model to match travelers; a hybrid itinerary planner that fuses content-based filtering, collaborative filtering, and machine-learning-based rating prediction to generate preference-aligned and geographically coherent travel plans and a predictive budgeting system that applies regression-based forecasting with live API data to provide dynamic cost estimation. The platform is developed specifically for the Sri Lankan tourism context, incorporating regional travel behavior patterns and destination characteristics into its models. Experiments indicate strong performance across all modules, including high-quality group matching, accurate itinerary prediction, and a substantial improvement in budget estimation accuracy compared with static baselines. Early user testing further highlights increased satisfaction with itinerary relevance and budget transparency. Overall, the framework demonstrates a scalable and adaptive approach to smart tourism planning, advancing personalization, collaboration, and sustainable travel support.
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    Machine Learning-Based Early Detection Of Autism Using Multimodal Conversational Features
    (Institute of Electrical and Electronics Engineers Inc., 2026-06-26) Haturusinghe, R; Gunathilake, B; Abeysundara, S; Senadeera, S; Thelijjagoda, S; Jayalath, T
    Early and reliable screening for autism spectrum disorder (ASD) remains challenging in low-resource and high-variance conversational settings. This paper presents an end-to-end multimodal screening system that analyzes child-caregiver interaction data from audio recordings, CHAT-format transcripts, and text inputs to estimate ASD likelihood and provide clinician-facing explanations. The system integrates three feature families: pragmatic-conversational, acoustic-prosodic, and syntactic-semantic, supporting component-wise classification and late-fusion strategies with modality-aware weighting. Beyond prediction, the platform provides transcript-level behavioral annotations, global and local feature attributions, and counterfactual what-if analysis. Experiments on cross-validated ASDBank data show multimodal fusion achieving 87.2% accuracy (ROC-AUC 0.92), outperforming unimodal baselines by 2-4%.