158 citations · 175 across the 13 of their papers we have counts for
13 papers
Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli +2
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to…
Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Shriyank Somvanshi, Pavan Hebli, Gaurab Chhetri +1
This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After fi…
Model Context Protocols in Adaptive Transport Systems: A Survey
Gaurab Chhetri, Shriyank Somvanshi, Md Monzurul Islam +5
The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport systems, where diverse protocols and c…
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…
Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists
Shriyank Somvanshi, Anannya Ghosh Tusti, Rohit Chakraborty +1
Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. Thi…
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba
Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi +5
Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient pro…
A Survey on Kolmogorov-Arnold Network
Shriyank Somvanshi, Syed Aaqib Javed, Md Monzurul Islam +2
This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by t…
A Survey on Deep Tabular Learning
Shriyank Somvanshi, Subasish Das, Syed Aaqib Javed +2
Tabular data, widely used in industries like healthcare, finance, and transportation, presents unique challenges for deep learning due to its heterogeneous nature and lack of spati…