INNOVATIVE APPROACHES TO MALICIOUS URL DETECTION: USING MACHINE LEARNING UNLEASHED

Abstract

The adoption of new technology and equipment is urgently needed to improve the state-of-the-art in traffic control 
as the problem of urban traffic congestion worsens. It has been shown that the conventional approaches, such 
timers and human control, are insufficient to address this dilemma. This project proposes a method to measure the 
vehicle density in real time utilizing digital image processing and clever edge recognition in order to control 
traffic. In comparison to the current systems, this demanding traffic control system delivers a notable improvement 
in response time, vehicle management, automation, dependability, and overall efficiency. In addition, four sample 
photos of various traffic situations are used to properly show the entire process, from image acquisition to edge 
detection and, ultimately, green light allocation.