
Mohammed Farhan Khan
Web Development Intern

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About me
Full Stack Developer
Education

S.F.X. Green Herald School
2014 - 2016A levels
BRAC University
2017 - 2020Bachelors of computer science Computer Science
University of Technology Sydney
2023 - 2024Master's degree Master's of IT (Software Engineering)
Scholastica
2013 - 2013O levels
Experience

ACI Limited
Sept 2019 - Nov 2019Web Development Intern• Developed essential skills for creative web design and frontend development (HTML, CSS, JS)• Gained teamwork skills by collaborating with multiple teams, as it was essential for front endmodeling of websites and digital ad banners

TechTrioZ Solutions
Feb 2021 - Jan 2023Software Engineer
Aug 2021 - Jan 2023Junior Software Engineer
Feb 2021 - Aug 2021

Omnisystems Network Solutions Pty Ltd
Aug 2024 - Dec 2024IT Engineer
Licenses & Certifications
- View certificate

The Complete Java Certification Course
UdemyJul 2020 - View certificate

2021 Complete Python Bootcamp From Zero to Hero in Python
UdemySept 2021 - View certificate

Keeping Up With the Javascripts: ES6
PirpleJul 2021 - View certificate

Build Web Apps with Vue JS 3 & Firebase
UdemyAug 2021
Honors & Awards
- Awarded to Mohammed Farhan KhanFirst place UTS IOS AI Hackathon 2023 - Jun 2023 Developed a prototype model for the platform IOS to detect driver drowsiness for Truckers using swiftUI and an ensemble of classifiers such as Yawn Detection, Eye Detection, Pulse detection with the help of Iphones camera and apple watch. Eye detection: The system utilizes state-of-the-art eye-tracking technology to monitor eye behavior, such as eyelid closures, blink frequency, and gaze direction.Yawn detection: Our driver drowsiness detector employs sophisticated algorithms to detect… Show more Developed a prototype model for the platform IOS to detect driver drowsiness for Truckers using swiftUI and an ensemble of classifiers such as Yawn Detection, Eye Detection, Pulse detection with the help of Iphones camera and apple watch. Eye detection: The system utilizes state-of-the-art eye-tracking technology to monitor eye behavior, such as eyelid closures, blink frequency, and gaze direction.Yawn detection: Our driver drowsiness detector employs sophisticated algorithms to detect yawning patterns, a strong indicator of driver fatigue. By analyzing facial movements and monitoring key facial features, such as mouth and jaw motion, the system can promptly identify signs of drowsiness and issue an alert to the trucker.Heartbeat detection: To enhance the accuracy of drowsiness detection, our system incorporates an innovative heartbeat monitoring component. By incorporating real time data from the health app from apple watch it continuously measures the driver's heart rate. Deviations from a normal heart rate range, indicative of fatigue or drowsiness.With help of all this classifiers drowsiness is detected with OR operation and alert is provided to the driver with sound and vibrations from the apple watch. Show less
- Awarded to Mohammed Farhan KhanWinner of Best Project Award – Software Engineering showcase University of Technology Sydney Jun 2023 Developed and showcased and expanse tracker app with features such as Ai integration to suggest onexpanses, Visual data analytics, OCR recognition from image, pdf generation etc.
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