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Author: search.filters.author.Jayalath, T

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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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    NoFake Trustworthy Reviewer Scoring System for Detecting Fake Reviews
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dissanayaka S.D; Jayawardhana R.A.D.G.S; Jayalath, T; De Silva, H
    The proliferation of fake online reviews has severely undermined consumer trust and market fairness in digital platforms. Traditional detection systems relying on rule-based filters or shallow machine learning models often fail to identify sophisticated, artificially intelligent-generated, or behaviorally deceptive content. This research proposes an intelligent system for fake review detection that integrates advanced Natural Language Processing, behavioral anomaly analysis, and explainable artificial intelligence to enhance accuracy, transparency, and usability. The system employs a hybrid feature set of about 212 dimensions, including Term Frequency-Inverse Document Frequency scores, sentiment-rating inconsistency, readability metrics, and reviewer behavioral indicators such as review frequency and account metadata. A neural network trained on the Deceptive Opinion Spam dataset (40,000 reviews) classifies reviews as genuine or fake, achieving 81.55% accuracy and an Area Under the Receiver Operating Characteristic Curve of 0.9019. Anomaly detection models Isolation Forest generate a dynamic trust score for reviewers, improving detection of coordinated spam campaigns. To ensure transparency, SHapley Additive exPlanations are integrated into a full-stack web application, providing human-readable insights such as Sentiment-Rating Mismatch or Robotic Writing Detected. Results show the proposed system outperforms traditional methods in accuracy and interpretability, offering a scalable and trustworthy solution for digital trust ecosystems.