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@article{182387,
        author = {POLEPALLY SAI PANKAJ GOUD and GUDLA DHANUSH and B SHISHINDAR and NANDAM SRI SAI SURESH},
        title = {User-Driven Weather Monitoring Application for Culinary Businesses: Enhancing Decision Making and Customer Experience},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1676-1682},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182387},
        abstract = {This paper presents a novel approach utilizing deep learning model LSTM (long short term memory) with TSA (tunicate swarm algorithm) for the development of a User-Driven Weather Monitoring Application tailored specifically for culinary businesses. Leveraging advanced machine learning techniques, the proposed application aims to enhance decision-making processes and improve customer experiences in the culinary industry. By harnessing the power of LSTM with TSA, the application provides real-time weather updates and forecasts, enabling culinary businesses to make informed decisions regarding menu planning, outdoor dining arrangements, and operational strategies based on weather conditions. The system integrates user feedback and preferences, allowing for personalized weather notifications and recommendations tailored to individual business needs. Through the utilization of proposed LSTM with TSA, the User-Driven Weather Monitoring Application offers a sophisticated yet intuitive solution to address the unique challenges faced by culinary establishments in adapting to varying weather patterns and optimizing customer satisfaction.},
        keywords = {Weather Monitoring Application, Culinary Businesses, Recommendations, Customer Satisfaction, Tunicate Swarm Algorithm.},
        month = {July},
        }