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Muhammad Talha Nadeem

FAST · 2025 · i19 - 0802

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Phone

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GitHub

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Academic

Program

BSCS

CGPA

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Year

2025

Education

FAST NUCES

Address

Islamabad

DOB

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Career

Current role

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Target role

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Skills

Internet of Things, Machine Learning, Cloud Computing, Embedded Systems

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.

Dynamic Whistle Blower Traffic Monitoring System 
This project aims to design and implement a Dynamic Whistleblower Traffic Monitoring System, 
an ML & loT-powered solution to enhance traffic law enforcement and road safety. Speeding is a 
major cause of road accidents, yet the absence of real-time, automated monitoring systems leads 
to increased violations and fatalities. This project leverages ML, loT, and cloud computing to provide 
an efficient and scalable approach to speed detection and enforcement. 
The system integrates 2D LIDAR sensors, high-resolution cameras, and machine learning models to 
detect speeding vehicles, recognize number plates, and transmit data to a cloud-based dashboard 
for automated violation reporting. By ensuring accuracy, efficiency, and real-time enforcement, this 
solution aims to reduce road accidents, improve compliance, and lay the foundation for smart, ML 
& loT-driven traffic management in the future. 
Technology Used: 
Internet of Things, Machine Learning, 
Cloud Computing, Embedded Systems. 
Supervisor Name: 
Dr. Muhammad Tariq 
Group Members: 
Amir Sallach Khan (i21 - 0962) 
Muhammad Talha Nadeem (i19 - 0802) 
FAST NUCES ISLAMABAD CAMPUS

AI enrichment

Muhammad Talha Nadeem is a BSCS graduate from FAST NUCES Islamabad who developed a dynamic traffic monitoring system integrating ML, IoT, and cloud computing. The project utilized 2D LIDAR sensors and embedded systems to enable real-time speed detection and automated violation reporting.

Skills (AI)

["Machine Learning", "Internet of Things", "Cloud Computing", "Embedded Systems", "LIDAR", "Computer Vision"]

Status: ai_done

Provenance

Source file: FAST School of Electrical Engineering - Graduate Directory 2025.pdf
From job #18 page 39
Created: 1778140415