| LLNet: A deep autoencoder approach to natural low-light image enhancement KG Lore, A Akintayo, S Sarkar Pattern Recognition 61, 650-662, 2017 | 1442 | 2017 |
| A deep learning framework to discern and count microscopic nematode eggs A Akintayo, GL Tylka, AK Singh, B Ganapathysubramanian, A Singh, ... Scientific reports 8 (1), 9145, 2018 | 110 | 2018 |
| Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network C Liu, A Akintayo, Z Jiang, GP Henze, S Sarkar Applied Energy 211, 1106-1122, 2018 | 71 | 2018 |
| Prognostics of combustion instabilities from hi-speed flame video using a deep convolutional selective autoencoder A Akintayo, KG Lore, S Sarkar, S Sarkar International Journal of Prognostics and Health Management 7 (4), 2016 | 50 | 2016 |
| Energy prediction using spatiotemporal pattern networks Z Jiang, C Liu, A Akintayo, GP Henze, S Sarkar Applied Energy 206, 1022-1039, 2017 | 27 | 2017 |
| 3D convolutional selective autoencoder for instability detection in combustion systems T Gangopadhyay, V Ramanan, A Akintayo, PK Boor, S Sarkar, ... Energy and AI 4, 100067, 2021 | 25 | 2021 |
| Early detection of combustion instabilities using deep convolutional selective autoencoders on hi-speed flame video A Akintayo, KG Lore, S Sarkar, S Sarkar arXiv preprint arXiv:1603.07839, 2016 | 19 | 2016 |
| An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection A Akintayo, N Lee, V Chawla, M Mullaney, C Marett, A Singh, A Singh, ... arXiv preprint arXiv:1603.07834, 2016 | 16 | 2016 |
| A symbolic dynamic filtering approach to unsupervised hierarchical feature extraction from time-series data A Akintayo, S Sarkar 2015 American Control Conference (ACC), 5824-5829, 2015 | 12 | 2015 |
| Probabilistic Graphical Modeling of Distributed Cyber-Physical Systems S Sarkar, Z Jiang, A Akintayo, S Krishnamurthy, A Tewari Cyber-Physical Systems: Foundations, Principles and Applications, 514, 2016 | 11 | 2016 |
| Hierarchical symbolic dynamic filtering of streaming non-stationary time series data A Akintayo, S Soumik Signal Processing 121, 76-88, 2018 | 7 | 2018 |
| High speed video-based health monitoring using 3d deep learning S Ghosal, A Akintayo, P Boor, S Sarkar Dynamic Data-Driven Application Systems (DDDAS), 2017 | 7 | 2017 |
| Hierarchical symbolic dynamic filtering of streaming non-stationary time series data A Akintayo, S Sarkar arXiv preprint arXiv:1702.01811, 2017 | 5 | 2017 |
| Building energy disaggregation using spatiotemporal pattern network C Liu, Z Jiang, A Akintayo, GP Henze, S Sarkar 2018 Annual American Control Conference (ACC), 1052-1057, 2018 | 2 | 2018 |
| Hierarchical feature extraction from spatiotemporal data for cyber-physical system analytics AJ Akintayo Iowa State University, 2017 | 2 | 2017 |
| LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement K Gwn Lore, A Akintayo, S Sarkar arXiv e-prints, arXiv: 1511.03995, 2015 | 1 | 2015 |
| A deep learning framework to discern and count microscopic nematode eggs A Singh, B Ganapathysubramanian, A Singh, S Sarkar, G Tylka | | 2018 |
| An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection A Singh, B Ganapathysubramanian, V Chawla, M Mullaney, C Marett, ... | | 2016 |
Soumik SarkarDirector, Translational AI Center, Professor, Iowa State University在 iastate.edu 的电子邮件经过验证
