stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
-  A. Dapogny, K. Bailly and S. Dubuisson(2015), "Pairwise Conditional Random Forests for Facial Expression Recognition," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 3783-3791, doi: 10.1109/ICCV.2015.431.
-  A. Dehghan, E. G. Ortiz, G. Shu and S. Z. Masood(2017), "DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network," arXiv preprint arXiv:1702.04280.
-  A. Mollahosseini, D. Chan and M. H. Mahoor(2016), "Going deeper in facial expression recognition using deep neural networks," 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, pp. 1-10, doi: 10.1109/WACV.2016.7477450.
-  A. R. Biswas and R. Giaffreda, "IoT and cloud convergence: Opportunities and challenges," 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, 2014, pp. 375-376, doi: 10.1109/WF-IoT.2014.6803194.
-  B. Yang, J. Cao, R. Ni and Y. Zhang(2018), "Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images," In IEEE Access, vol. 6, pp. 4630-4640, doi: 10.1109/ACCESS.2017.2784096.
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