REAL-TIME DISASTER INFORMATION AGGREGATION SOFTWARE USING DEEP LEARNING.

Abstract

The present invention relates to a real-time disaster information aggregation software designed to enhance disaster management through advanced data analysis and machine learning techniques. This innovative system leverages various data sources, including social media platforms, news outlets, and public monitoring systems, to collect and process information on both natural and man-made disasters. Utilizing deep learning models such as YOLO (You Only Look Once), VGGNet, and Bi-LSTM (Bidirectional Long Short-Term Memory), the software effectively analyzes images, videos, and textual data to accurately classify disaster types and provide real-time situational awareness. The application features a user-friendly interface that allows disaster response agencies to access critical information, visualize disaster data on interactive dashboards, and receive automated recommendations for response strategies. Through a robust data preprocessing pipeline and integration of REST APIs and WebSocket technology, the system ensures timely updates and effective communication among response teams. By providing accurate, actionable insights and location-based alerts, this invention aims to improve the efficiency and speed of disaster response efforts, ultimately saving lives and minimizing damage during emergencies. This patent seeks to protect the unique methodologies and technologies employed in this comprehensive disaster information aggregation system.

Specification

Description:Field of the Invention
The invention pertains to a real-time disaster information aggregation software designed to collect, process, and analyze data related to disasters from multiple sources, including social media, public platforms, and news outlets. Utilizing advanced machine learning models such as deep Convolutional Neural Networks (CNN), YOLO, and Natural Language Toolkit (NLTK) integrated with BERT (Bidirectional Encoder Representations from Transformers), this system provides immediate classification, location-based alerts, and action recommendations for disaster response teams. The software's integration of image, text, and video analysis ensures a comprehensive approach to disaster prediction, monitoring, and management.

This invention falls under the field of computer science and engineering, specifically within artificial intelligence (AI), machine learning (ML), and real-time disaster management systems. It integrates multiple AI technologies to analyze and categorize disaster data for real-time visualization and response.

Background of the Invention

Traditional disaster management systems primarily rely on manual reporting and monitoring processes, which often result in delayed response times and inadequate information dissemination. With the increasing availability of data through social media, news channels, and public monitoring platforms, there is a need for a sophisticated system that can process this vast amount of information in real time, ensuring timely and accurate disaster categorization and response. Existing solutions lack the integration of real-time video, image, and textual analysis combined with deep learning models that can provide precise and immediate situational awareness.

Summary of the Invention
The proposed invention is a web-based AI-driven application that classifies and visualizes disaster-related data collected from various sources such as social media, public platforms, and live news feeds. The software uses multiple deep learning models like CNN (Convolutional Neural Networks), YOLO (You Only Look Once), and Bi-LSTM (Bidirectional Long Short-Term Memory) for image and video analysis. Text data is processed using NLTK and BERT models combined with support vector machines (SVM). The software categorizes disasters into different types-natural (e.g., floods, earthquakes) and man-made (e.g., building collapses, fires)-and provides a real-time, interactive dashboard. This system is capable of notifying relevant authorities, including National Disaster Response Force (NDRF) teams, and provides recommendations for action plans based on the predicted output and casualty estimations.


Brief Description of the Invention

The software consists of the following key components:
Data Aggregation: Collects data from various sources (social media, public platforms, news channels) using REST APIs, WebSocket, and Boost Library for real-time communication.
Data Preprocessing: Applies techniques such as noise filtering, resizing, normalization, and enhancement using OpenCV and Pillow libraries to prepare the dataset.
Model Training and Disaster Categorization:
Utilizes a combination of deep CNN with VGGNet, YOLOV4, and ResNet models to classify images and videos.
For text data, BERT and Bi-LSTM models, along with SVM, extract and categorize relevant disaster information.
User Interface and Visualization: Features an interactive UI built using MERN Stack and Flutter for mobile compatibility. The dashboard displays real-time disaster data visualizations, location-based alerts, and predictive analytics.
Automated Response System: Provides recommendations to disaster response agencies based on model outputs, using XGBoost and CNN models for further planning.

Description of Images
The images illustrate various components and processes of the real-time disaster information aggregation software. The image presents the software's architecture flow, detailing each stage from data aggregation, using sources like social media and news platforms, to disaster categorization and visualization via advanced deep learning models such as CNN and YOLO.
The image shows a prototype of the user interface, highlighting how disaster data is visualized in real-time on an interactive, map-based dashboard.
The prototype figure represents the user interface of the real-time disaster information aggregation software. It showcases the design elements and functionalities available to the end-users, such as disaster response agencies. The prototype likely includes components like:
• Registration and Login: A section where users can create an account and log in to access the system.
• Dashboard: A central area displaying real-time data on ongoing disasters, categorized by type and severity, using visual elements like graphs and maps for intuitive understanding.
• Data Upload Features: Options for users to upload images, videos, and text data relevant to disaster scenarios, which can be analyzed by the system.
• Chatbot Integration: A user-friendly interface that allows users to query the system, receive recommendations, and get responses about disaster management.
• Document Management: Areas for uploading essential documents related to disaster management, such as affidavits and certificates.
The overall design emphasizes user experience (UX), ensuring that responders can quickly access critical information and tools needed to act effectively during disaster situations.
The architecture figure outlines the high-level structure of the software, detailing how various components interact to achieve the system's goals. Key elements typically depicted in the architecture diagram include:
• Data Aggregation Layer: Illustrates how data is collected from multiple sources, such as social media platforms, news websites, and public monitoring systems, using REST APIs and WebSocket for real-time data transmission.
• Data Preprocessing Layer: Demonstrates the processes involved in preparing data for analysis, including noise filtering, normalization, and resizing of images and videos. This ensures the data is clean and suitable for model training.
• Machine Learning Models: Highlights the different deep learning models (e.g., YOLO, VGGNet, ResNet, Bi-LSTM) used for analyzing images, videos, and text data. This layer emphasizes the multi-modal approach to processing and predicting disaster scenarios.
• Output Layer: Shows how the processed data is presented to users through a dashboard, with alerts and actionable insights regarding disaster management.
• Feedback Mechanism: A component that captures user interactions and outcomes to improve the system's recommendations and accuracy over time.
This architecture diagram emphasizes the system's efficiency, scalability, and ability to provide real-time insights to disaster response teams. This image focuses on the software's data preprocessing and model training phases, where deep CNN and YOLO models are employed for efficient categorization and analysis of disaster data, ensuring optimal performance and accuracy.
The image describes the process of dataset preparation, specifying the types of labeled data used for training the models, including natural disasters (e.g., floods, earthquakes) and man-made incidents (e.g., building collapses, fires). Image depicts the integration of social media platforms and public monitoring systems, showing how data is collected and processed in real-time using WebSocket and REST APIs. The image details the flow of information from disaster prediction to the generation of action recommendations for disaster response teams like NDRF, leveraging models such as SVM and CNN to provide situational analysis, casualty estimations, and strategic planning guidance.
The use case figure illustrates the workflow of how the software operates within the context of a disaster management scenario. Key aspects depicted in the use case include:
• User Roles: Identifies the different actors involved, such as disaster response agencies, administrators, and the general public.
• User Actions: Outlines the steps taken by users, starting from registration and login to uploading disaster-related data and querying the system via a chatbot.
• System Responses: Describes how the software processes user inputs, analyzes data, and provides outputs such as visual alerts on the dashboard and recommendations for action based on the predicted disaster type.
• Feedback Loop: Highlights how users can update the database with new information or corrections based on real-time observations, thus enhancing the system's predictive accuracy and relevance.
This use case provides a practical overview of the software's functionalities and interactions, demonstrating its effectiveness in facilitating timely and informed disaster response actions.


, Claims:We Claim
1. A real-time disaster information aggregation software that collects and processes data from multiple sources, including social media, news platforms, and public monitoring systems.
2. The software utilizes advanced AI models (CNN, YOLO, Bi-LSTM, BERT) for comprehensive text, image, and video analysis to categorize and predict disaster types in real time.
3. An interactive user interface that visualizes disaster data on a map-based dashboard, providing location-based alerts and actionable insights for disaster response teams.
4. An automated recommendation system that employs SVM and XGBoost models to suggest emergency response actions, considering disaster severity and casualty information.
5. Integration of REST APIs and WebSocket for seamless data input and real-time updates across multiple platforms.

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