All Outputs (2)

A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification (2022)
Journal Article
Ahmad, M., Ghous, U., Hong, D., Khan, A. M., Yao, J., Wang, S., & Chanussot, J. (2022). A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, 60, 1-16. https://doi.org/10.1109/TGRS.2022.3209182

Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled training samples, which outlays high costs in terms of time and res... Read More about A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification.

Computing on Wheels: A Deep Reinforcement Learning-Based Approach (2022)
Journal Article
Ahsan Kazmi, S. M., Ho, T. M., Nguyen, T. T., Fahim, M., Khan, A., Piran, M. J., & Baye, G. (2022). Computing on Wheels: A Deep Reinforcement Learning-Based Approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22535-22548. https://doi.org/10.1109/TITS.2022.3165662

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation... Read More about Computing on Wheels: A Deep Reinforcement Learning-Based Approach.