@inproceedings{thakuria2024deep,title={Deep Learning Models for Diabetic Retinopathy Detection: A Comparative Study},author={Thakuria, Munmi and Zhuang, Yong},booktitle={2024 IEEE International Conference on Big Data (BigData)},pages={5078--5081},year={2024},organization={IEEE}}
2023
BigData
CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead Streamflow Forecasting
Yong Zhuang, David L Small, Patrick D Flynn, and 4 more authors
In 2023 IEEE International Conference on Big Data (BigData), 2023
@inproceedings{zhuang2023castle,title={CASTLE: A Cascaded Spatio-Temporal Approach for Long-lead Streamflow Forecasting},author={Zhuang, Yong and Small, David L and Flynn, Patrick D and Palash, Wahid and Islam, Shafiqul and Chen, Ping and Ding, Wei},booktitle={2023 IEEE International Conference on Big Data (BigData)},pages={1031--1038},year={2023},organization={IEEE},}
2022
ICDM
Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems
Yong Zhuang, Matthew Almeida, Wei Ding, and 3 more authors
In 2022 IEEE International Conference on Data Mining (ICDM), 2022
@inproceedings{zhuang2022widening,title={Widening the Time Horizon: Predicting the Long-Term Behavior of Chaotic Systems},author={Zhuang, Yong and Almeida, Matthew and Ding, Wei and Flynn, Patrick D and Islam, Shafiqul and Chen, Ping},booktitle={2022 IEEE International Conference on Data Mining (ICDM)},pages={833--842},year={2022},organization={IEEE},}
2021
TKDD
Mitigating class-boundary label uncertainty to reduce both model bias and variance
Matthew Almeida, Yong Zhuang, Wei Ding, and 2 more authors
ACM Transactions on Knowledge Discovery from Data (TKDD), 2021
@article{zhuang2021mitigating,title={Mitigating class-boundary label uncertainty to reduce both model bias and variance},author={Almeida, Matthew and Zhuang, Yong and Ding, Wei and Crouter, Scott E and Chen, Ping},journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},volume={15},number={2},pages={1--18},year={2021},publisher={ACM New York, NY, USA},}
2018
ICBK
Galaxy: Towards Scalable and Interpretable Explanation on High-dimensional and Spatio-Temporal Correlated Climate Data
Yong Zhuang, David L Small, Xin Shu, and 3 more authors
In 2018 IEEE International Conference on Big Knowledge (ICBK), 2018
@inproceedings{zhuang2018galaxy,title={Galaxy: Towards Scalable and Interpretable Explanation on High-dimensional and Spatio-Temporal Correlated Climate Data},author={Zhuang, Yong and Small, David L and Shu, Xin and Yu, Kui and Islam, Shafiqul and Ding, Wei},booktitle={2018 IEEE International Conference on Big Knowledge (ICBK)},pages={146--153},year={2018},organization={IEEE},}
2017
ICBK
Crime hot spot forecasting: A recurrent model with spatial and temporal information
Yong Zhuang, Matthew Almeida, Melissa Morabito, and 1 more author
In 2017 IEEE International Conference on Big Knowledge (ICBK), 2017
Crime is a major social problem in the United States, threatening public safety and disrupting the economy. Understanding patterns in criminal activity allows for the prediction of future high-risk crime "hot spots" and enables police precincts to more effectively allocate officers to prevent or respond to incidents. With the ever-increasing ability of states and organizations to collect and store detailed data tracking crime occurrence, a significant amount of data with spatial and temporal information has been collected. How to use the benefit of massive spatial-temporal information to precisely predict the regional crime rates becomes necessary. The recurrent neural network model has been widely proven effective for detecting the temporal patterns in a time series. In this study, we propose the Spatio-Temporal neural network (STNN) to precisely forecast crime hot spots with embedding spatial information. We evaluate the model using call-for-service data provided by the Portland, Oregon Police Bureau (PPB) for a 5-year period from March 2012 through the end of December 2016. We show that our STNN model outperforms a number of classical machine learning approaches and some alternative neural network architectures.
@inproceedings{zhuang2017crime,title={Crime hot spot forecasting: A recurrent model with spatial and temporal information},author={Zhuang, Yong and Almeida, Matthew and Morabito, Melissa and Ding, Wei},booktitle={2017 IEEE International Conference on Big Knowledge (ICBK)},pages={143--150},year={2017},organization={IEEE},}
2016
ICNSC
An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting
Yong Zhuang, Kui Yu, Dawei Wang, and 1 more author
In 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), 2016
@inproceedings{zhuang2016evaluation,title={An evaluation of big data analytics in feature selection for long-lead extreme floods forecasting},author={Zhuang, Yong and Yu, Kui and Wang, Dawei and Ding, Wei},booktitle={2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC)},pages={1--6},year={2016},organization={IEEE},}
CI
Long-lead prediction of extreme precipitation cluster via a spatiotemporal convolutional neural network
WY Zhuang, and Wei Ding
In Proceedings of the 6th International Workshop on Climate Informatics: CI, 2016
@inproceedings{zhuang2016long,title={Long-lead prediction of extreme precipitation cluster via a spatiotemporal convolutional neural network},author={Zhuang, WY and Ding, Wei},booktitle={Proceedings of the 6th International Workshop on Climate Informatics: CI},year={2016},}