| Deep learning vs. classical machine learning: A comparison of methods for fluid intelligence prediction L Guerdan, P Sun, C Rowland, L Harrison, Z Tang, N Wergeles, Y Shang Adolescent Brain Cognitive Development Neurocognitive Prediction: First …, 2019 | 10 | 2019 |
| sUAS and Machine Learning Integration in Waterfowl Population Surveys Z Tang, Y Zhang, Y Wang, Y Shang, R Viegut, E Webb, A Raedeke, ... 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 5 | 2021 |
| A new GNN-based object detection method for multiple small objects in aerial images Z Tang, Y Liu, Y Shang 2023 IEEE/ACIS 23rd International Conference on Computer and Information …, 2023 | 2 | 2023 |
| A detection confidence-regulated path planning (dcrpp) algorithm for improved small object counting in aerial images M Krusniak, K Leppanen, Z Tang, F Gao, Y Wang, Y Shang 2020 IEEE International Conference on Consumer Electronics (ICCE), 1-6, 2020 | 2 | 2020 |
| Deep Learning Models for Waterfowl Detection and Classification in Aerial Images Y Zhang, Y Feng, S Wang, Z Tang, Z Zhai, R Viegut, L Webb, A Raedeke, ... Information 15 (3), 157, 2024 | 1 | 2024 |
| Detection Probability and Bias in Machine-Learning-Based Unoccupied Aerial System Non-Breeding Waterfowl Surveys R Viegut, E Webb, A Raedeke, Z Tang, Y Zhang, Z Zhai, Z Liu, S Wang, ... Drones 8 (2), 54, 2024 | 1 | 2024 |
| Deep Learning Methods for Tree Detection and Classification Y Zhang, Y Wang, Z Tang, Z Zhai, Y Shang, R Viegut 2022 IEEE 4th International Conference on Cognitive Machine Intelligence …, 2022 | | 2022 |
| Nonbreeding Waterfowl Behavioral Response to Crewed and Uncrewed Aerial Surveys on Conservation Areas in Missouri RA Viegut, EB Webb, AH Raedeke, Z Tang, Y Zhang, Y Shang Journal of the Southeastern Association of Fish and Wildlife Agencies 11 …, 0 | | |
| Knowledge Discovery 86 D Wu, Z Chen, M Duan, M Zhu, X Zhu, Z Jiang, Y Lou, Z Tang, Y Liu, ... | | |
