AI-Bot

AI bot for Covid-19

Simplifying Campus Safety: Neural-19, the COVID-19 Chatbot for University Dorms

Introduction:

Neural-19 emerged as a chatbot tailored to tackle COVID-19 challenges within university dormitories. By amalgamating web scraping, speech recognition, and mask detection, Neural-19 aimed to furnish vital information and fortify safety measures.

Problem Statement:

With COVID-19 disrupting campus life, ensuring dormitory safety became paramount. Neural-19 sought to provide clarity on policies, dispense information, and uphold safety protocols.

Functionality Overview:

Neural-19’s functionalities comprised:

  1. Real-Time Data Retrieval: It sourced live COVID-19 statistics, policy updates, testing sites, and vaccination centers.
  2. Mask Detection: Leveraging OpenCV and TensorFlow, Neural-19 identified mask compliance in real-time, bolstering safety measures.
  3. Speech Recognition: Users interacted vocally through Google’s Speech Recognition API, enhancing accessibility.
  4. User Query Handling: Neural-19 processed and responded to user queries efficiently, fostering understanding and engagement.

Technological Framework:

Neural-19 utilized:

  • TensorFlow + Keras: These frameworks facilitated the creation and training of the chatbot’s neural network, enabling it to comprehend and respond to user queries effectively.
  • OpenCV: Employed for mask detection, OpenCV provided a robust platform for real-time analysis of video streams, detecting whether individuals were wearing masks.
  • scikit-learn: This library aided in developing classification models, particularly for mask detection, ensuring accurate identification.
  • Google API: Integration of Google’s Speech Recognition API allowed users to interact with the chatbot through spoken commands, enhancing user experience and accessibility.

Development Timeline:

The development phases of Neural-19 encompassed:

  1. Chatbot Training and GUI Development: This stage involved creating a labeled dataset to train the chatbot, alongside developing a user-friendly graphical interface using Tkinter.
  2. Speech Recognition Integration: The integration of Google’s Speech Recognition API enabled the chatbot to process voice commands, expanding its usability and accessibility.
  3. Mask Detection Training and Integration: Neural-19 underwent training for mask detection models, incorporating OpenCV for real-time analysis, and seamlessly integrating it with the chatbot for cohesive functionality.
  4. Socket Implementation: To facilitate communication between independently running programs, such as the chatbot and mask detection module, sockets were implemented to enable efficient data transfer.

Conclusion:

Neural-19 represented a robust solution to COVID-19 challenges within university dormitories. By leveraging advanced technologies and meticulous development, Neural-19 fostered a safer environment, exemplifying the potential of technology in addressing contemporary societal issues. The implementation won second prize