Earticle

초록

영어

Addressing traffic congestion in Vehicular Adhoc Networks (VANETs) is crucial for ensuring safety, social welfare, and economic progress. This study introduces a novel approach utilizing transfer learning in conjunction with the Gradient Boosting algorithm to optimize information transmission within VANETs. By leveraging pre-trained nodes as information sources, the proposed model effectively trains newly registered nodes, enhancing congestion control performance. Simulation results conducted in Python demonstrate the model's effectiveness, showcasing reduced execution times compared to traditional fuzzy logic-based methods. Integration of this model into existing congestion control systems promises real-time congestion screening capabilities. The study highlights the importance of further research collaboration to tackle realtime implementation challenges and advance traffic congestion management using AI-based techniques. Simulation results have indicated that the proposed system model achieves a performance of 95.43% accuracy. It also noted that the use of the proposed system in producing the HRA results is more accurate compared to the past methods.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. PROPOSED METHODOLOGY
IV. SIMULATION RESULTS
V. CONCLUSION
VI. FUTURE WORK AND LIMITATIONS
VII. REFERENCES

저자

• Muhammad Ubaid Ullah [ University of South Asia, Lahore, Pakistan ]
• Attia Amin [ Department of Computer Science NCBA&E Lahore, Pakistan ]
• Muhammad Sajid Farooq [ Department of Cyber Security, NASTP Institute of Information Technology Lahore (NIIT), Lahore, 54000, Pakistan ]
• Muhammad Saleem [ School of Computer Science, Minhaj University Lahore Lahore, Pakistan ]
• Javaid Ahmad Maik [ Department of Computer Science, NCBA&E Lahore, Pakistan ]
• Muhammad Adnan Khan [ School of Computing, Skyline University College, Sharjah, United Arab Emirates. RSCI, Riphah International University, Lahore 54000, Pakistan ]

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