1 citations · 2 across the 3 of their papers we have counts for
4 papers
Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)
Sudesh Bhagat, Ibne Farabi Shihab, Jonathan Wood
This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narrative…
Unlocking Insights Addressing Alcohol Inference Mismatch through Database-Narrative Alignment
Sudesh Bhagat, Raghupathi Kandiboina, Ibne Farabi Shihab +3
Road traffic crashes are a significant global cause of fatalities, emphasizing the urgent need for accurate crash data to enhance prevention strategies and inform policy developmen…
Accuracy is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models
Sudesh Ramesh Bhagat, Ibne Farabi Shihab, Anuj Sharma
This study investigates the relationship between deep learning (DL) model accuracy and expert agreement in classifying crash narratives. We evaluate five DL models -- including BER…
Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments
Ibne Farabi Shihab, Sudesh Ramesh Bhagat, Anuj Sharma
This study aims to compare the effectiveness of a robust ensemble model with the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate detection of sidewalks. Accurate…