8 papers
From Tiny Machine Learning to Tiny Deep Learning: A Survey
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri +8
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counte…
ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
Mahmuda Sultana Mimi, Md Monzurul Islam, Anannya Ghosh Tusti +2
Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work,…
A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed +6
Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems.…
Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes
Shriyank Somvanshi, Anannya Ghosh Tusti, Mahmuda Sultana Mimi +4
The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved i…
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
Gian Antariksa, Rohit Chakraborty, Shriyank Somvanshi +4
Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting…
Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
Shriyank Somvanshi, Rohit Chakraborty, Subasish Das +1
Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyc…