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@article{187174,
        author = {Tanisha Agrawal and Suryansh Pratap Singh and Vickey Bagde and Sudhanshu sharma and Vikas Dayma and Urvashi Sharma},
        title = {AlertHive: Real-Time Disaster Intelligence and Response Dashboard},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3581-3588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187174},
        abstract = {Disaster management demands quick access to reliable information for timely response. AlertHive: Real-Time Disaster Intelligence and Response Dashboard is an intelligent system that aggregates disaster-related data from social media, news APIs, and public sources. Using Natural Language Processing (NLP) and Machine Learning (ML), it extracts key details such as location, disaster type, and severity, while filtering irrelevant or duplicate data. The processed information is displayed on a real-time, interactive dashboard for emergency responders and agencies. AlertHive addresses limitations of existing systems by ensuring faster updates, improved data credibility, and broader local coverage. By reducing information overload and enhancing situational awareness, the platform enables quicker, more informed decision-making—ultimately improving coordination and response efficiency during disaster events.},
        keywords = {Disaster Management, Real-Time Dashboard, Natural Language Processing (NLP), Machine Learning (ML), Data Aggregation, Situational Awareness, Emergency Response .},
        month = {November},
        }