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Fahad Ahmad Siddiqui

FAST · 2022 · I18 – 0590
Email
Phone
LinkedIn
GitHub

Academic

Program
BSCS
CGPA
Year
2022
Education
SEECS
Address
DOB

Career

Current role
Target role
Skills
Java, Android Studio, Java Spring Suite 4.0, Firebase Database, SQLite, MERN Stack

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.
Accident Detection and Emergency Response System 
 
Almost everyone carries their smartphone with them while driving, and smartphones nowadays 
have a lot of sensors present inside them.  
Our system makes use of these sensors to detect an accident, and inform relevant authorities about 
the exact location of the accident. 
The user will sign-up and provide their personal details, along with emergency contacts.  
During the trip in case of an accident, it will be detected using the readings on the Accelerometer, 
Gyroscope and Microphone present inside the smartphone. 
Once an accident has been confirmed, the concerned authorities (helplines and emergency 
contacts) will be sent an alert along with the location.  
The ambulance portal will be shown the shortest path to the location.  
All the people using this app in a nearby radius will be notified of the accident and its location. 
The Admin Panel will be able to oversee real-time accidents, along with a history of events. 
Statistics will be saved, showing the number of events taken place in particular areas in a given time 
selected by Admin user.  
The Admin will have access to user’s information to make contact if needed.  
 
 
 
 
Technology Used: 
 
Java, Android Studio, Java Spring Suite 4.0,  
 
 
Firebase Database, SQLite, MERN Stack 
 
Supervisor Name: 
 
 
Mr. Bilal Khalid Dar 
 
    Group Members:  
Fahad Ahmad Siddiqui (I18 – 0590) 
 
Muhammad Saad Minhas (I18 – 0691) 
 
Muntahim Hussain Khan (i18 – 0707)

AI enrichment

Fahad Ahmad Siddiqui is a BSCS graduate who contributed to a group project developing an Accident Detection and Emergency Response System using smartphone sensors. The application utilized Java, Android Studio, and the MERN stack to detect accidents and notify authorities and nearby users.
Skills (AI)
["Java", "Android Studio", "Spring Suite 4.0", "Firebase Database", "SQLite", "MERN Stack", "Mobile Application Development"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2022 Final Version (07-06-2022).pdf
From job #25 page 211
Created: 1778170963