Publications

Publications & Intellectual Property

Peer-reviewed research, scholarly outputs, and patented technological innovations.

AI fora moreinclusiveworld

Conference Paper: 1
Granted Patents: 4
Total Outputs: 5

Peer-Reviewed Publications

Journal and conference papers with peer review.

Conference Paper· 2025

IEEE ETNCC

Using Machine Learning Models to Predict Estimated Time of Arrival (ETA) Based on Doordash Order Data

Zeng Fan, Steve Teoh Chee Hooi, Saliyah Binti Kahar, Muhammad Tayyab, Israr Ahmad

2025 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC),
pp. 701–705.

Abstract

This study explores food delivery ETA prediction using DoorDash order data, applying machine learning models to estimate delivery times under real-world conditions. We compare multiple models, including Linear Regression, Random Forest, XGBoost, and CNN, and analyze their performance in practical ETA prediction.

Research Areas

Machine Learning
ETA Prediction
Predictive Modeling
Applied AI

Resources

IEEE Xplore DOI

BibTeX

@inproceedings{zeng2025doordash,
  title = {{Using Machine Learning Models to Predict Estimated Time of Arrival (ETA) Based on Doordash Order Data}},
  author = {{Zeng Fan} and {Steve Teoh Chee Hooi} and {Saliyah Binti Kahar} and {Muhammad Tayyab} and {Israr Ahmad}},
  booktitle = {2025 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)},
  year = {2025},
  pages = {701--705},
  publisher = {IEEE},
  doi = {10.1109/etncc66224.2025.11299566},
  url = {https://ieeexplore.ieee.org/document/11299566}
}