AI-Mergency Room Project

React based project that creates a

Abstract

• What does AICR do?

  As it is well known that the number of emergency calls increased eightfold over a course of four days when Hurricane Katrina hit the Gulf Coast and flooded New Orleans, it is very overwhelming to keep the same amount of first-responder teams organized and efficient in emergency situations where the demand of first-responder teams increase dramtically. AI-mergency Control Room ensures that dispatchers stay productive and creates better workflow for first-responders that are involved in these situations as well as allocate emergency resources more efficiently. AICR includes anything from proper incident prioritization to visibility and traceability of major events.

• Goal of the project (who are you developing the project for?)

  The AICR is primarily to help people in dangerous situations such as natural disasters and other emergency situations by supporting dispatchers to stay fully productive during such circumstances. As a group we would like to contribute to the project by providing a UI and also add more functionality to existing features. We are using some external datasets to further extend our project's prospects to expand the project's analytics.

• How does AICR work?

  AICR automatically transcribes incoming calls and extracts all the detailed information by the help of Watson Natural Language Understanding and Watson Knowledge Studio. The status of the situation and all the incidents are then visualized inorder to give the operator full overview of the current situation which inturn helps the dispatcher to track emergency spots and prioritization. AICR is using a database powered by Db2 to store all the call information and allows to trace calls during and after the emergency.

Architecture Diagram

Technology Stack

- Front-end: React, Node JS, 
- IBM Cloud Services, Server-side Javascript
- Database: Db2
- Watson Natural Language Understanding, Watson Language Studio
- Google Speech-to-Text
- MapBox IO API

Cognos Embedded Dashboard Link

https://dataplatform.cloud.ibm.com/dashboards/a3ea944b-d800-4fa0-accc-9fa910398134/view/057be42522aa03e360e7eae4079128007e3f7158b3bb870085837b490a367397a93c13c5c87a1f58d3150d6afbea4658ca

How to install

1. git clone this repo to your machine
2. Comparing your project with the clone of the actual AICR project, place your db-credentials.json, daas-credentials.json, nlu-credentials.json, and google-speech-credentials.json into the /node_app directory
3. Copy/paste your mapbox-credentials.json into /node_app/public/scripts directory. These files are listed in the .gitignore file to that we can practice good security with our files and not push our own credentials to github.

Development Workflow

1. Once you have your requirements set, make sure you're on the master branch & go ahead and make a branch with git checkout -b branch-name-involving-your-feature

2. Once you make changes, check your changes with git status or git diff, then add, commit and push your changes

   • git add .
   • git commit -m 'message about your change'
   • git push origin your-branch-name

3. Make your changes and make sure your code works before you merge it back to master

   • git checkout master
   • git pull origin master
   • git merge your-branch-name hopefully there aren't too many merge conflicts, fix those and commit again
   • git push origin master