Deep learning

1. REDUCING TRAFFIC
CONGESTION BY OPTIMIZING
TRAFFIC LIGHTS USING DEEP
LEARNING TECHNIQUES
This Photo by Unknown author is licensed under CC BY-SA.

2. BACKGROUND AND
RATIONALE





3. BACKGROUND AND
RATIONALE





4. SCOPE AND LIMITATIONS OF THE
STUDY

5. SCOPE OF THE STUDY




6. LIMITATIONS OF THE STUDY
1.
-

7. TECHNOLOGICAL
CONSTRAINTS



8. GENERALIZATION
CHALLENGES



9. COST IMPLICATIONS
Implementing deep learning systems incurs costs
for integration, maintenance, and infrastructure
upgrades. Potential financial limitations may
hinder widespread adoption.

10. REGULATORY AND LEGAL
CONSIDERATIONS




11. STATEMENT OF THE
PROBLEM
•
•
•


12. RESEARCH QUESTIONS




13. GENERAL OBJECTIVES



14. SPECIFIC OBJECTIVES




15. METHODOLOGY



16. METHODOLOGY
-
-

17. METHODOLOGY
-
-
-
-
-
-

18. SIGNIFICANCE OF THE
STUDY



19. SIGNIFICANCE OF THE
STUDY

•
•
•
•

20. 