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@article{198840,
        author = {Manoj Kannan M and G. Balamurugan},
        title = {INTELLIGENT TOURIST PREDICTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12368-12372},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198840},
        abstract = {The Intelligent Tourist Prediction System is a smart and data-driven solution that leverages advanced Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning techniques to analyze, understand, and predict tourist behavior, preferences, and travel trends. In today’s rapidly evolving tourism industry, accurately predicting tourist interests has become essential for enhancing user experience, improving service quality, and optimizing resource management. Tourism service providers often struggle to process large volumes of heterogeneous data generated from various sources such as travel bookings, social media interactions, weather reports, and seasonal demand patterns. This system aims to overcome these challenges by developing a robust predictive framework that integrates multiple data sources and applies deep learning algorithms to generate accurate and real-time predictions. The model utilizes techniques such as Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNN), which are highly effective in handling time-series and sequential data. These models analyze historical travel data, user preferences, demographic details, and external influencing factors like climate conditions, festivals, and economic trends to forecast future tourist destinations and behaviors. Furthermore, the system incorporates data preprocessing techniques such as data cleaning, normalization, feature extraction, and dimensionality reduction to improve prediction accuracy and model efficiency. The predicted results can be used to provide personalized travel recommendations to users, helping them choose suitable destinations based on their interests and past behavior. Additionally, tourism authorities and businesses can utilize these insights for demand forecasting, strategic planning, marketing optimization, and efficient allocation of resources. One of the key advantages of this system is its ability to deliver real-time predictions and adapt to changing patterns in tourist behavior. It reduces dependency on traditional manual forecasting methods, which are often inaccurate and time-consuming. However, challenges such as data privacy, data sparsity, and dynamic environmental factors must be carefully managed to ensure system reliability and effectiveness.},
        keywords = {Tourist Prediction, Deep Learning, Artificial Intelligence, Recommendation System, Travel Analytics, Machine Learning, Big Data, Smart Tourism.},
        month = {April},
        }