 |
| CrossRef Text and Data Mining |
| Result of CrossRef Text and Data Mining Search is the related articles with entitled article. If you click link1 or link2 you will be able to reach the full text site of selected articles; however, some links do not show the full text immediately at now. If you click CrossRef Text and Data Mining Download icon, you will be able to get whole list of articles from literature included in CrossRef Text and Data Mining. |
| Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring |
| Se-Hui Song, Dong Keun Kim |
|
Healthc Inform Res. 2017;23(4):285-292. Published online October 31, 2017 DOI: https://doi.org/10.4258/hir.2017.23.4.285 |
|
Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring Healthcare Informatics Research. 2017;23(4):285  Water stress classification using Convolutional Deep Neural Networks JUCS - Journal of Universal Computer Science. 2022;28(3):311-328 Stress Classification using Deep Learning with 1D Convolutional Neural Networks Knowledge Engineering and Data Science. 2021;4(2):145 Image-Based Process Monitoring Using Deep Belief Networks IFAC-PapersOnLine. 2018;51(18):115-120 Breast cancer classification using deep belief networks Expert Systems with Applications. 2016;46:139-144 Multi-Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks-Deep Belief Network Model Pattern Recognition and Image Analysis. 2021;31(1):81-90 Dynamic texture-based scene classification using Deep Belief Networks Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications - LOPAL '18. 2018; A classification-based deep belief networks model framework for daily streamflow forecasting Journal of Hydrology. 2021;595:125967 Fuzzy deep belief networks for semi-supervised sentiment classification Neurocomputing. 2014;131:312-322 Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks Introduction to Deep Learning Using R. 2017;125-136 |
