Name
Title | ||
|---|---|---|
Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things. |
Abstract | ||
|---|---|---|
Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) i... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2017.2739340 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Data models,Big Data,Computational modeling,Tensile stress,Neural networks,Machine learning,Feature extraction | Data modeling,Computer science,Convolutional neural network,Feature extraction,Artificial intelligence,Overfitting,Deep learning,Artificial neural network,Big data,Feature learning,Machine learning | Journal |
Volume | Issue | ISSN |
14 | 2 | 1551-3203 |
Citations | PageRank | References |
21 | 0.66 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Li | 1 | 214 | 28.84 |
| Zhikui Chen | 2 | 692 | 66.76 |
| Laurence T. Yang | 3 | 6870 | 682.61 |
| Qingchen Zhang | 4 | 372 | 19.17 |
| M Jamal Deen | 5 | 524 | 76.75 |