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http://dx.doi.org/10.14400/JDC.2019.17.10.259

Using the Deep Learning for the System Architecture of Image Prediction  

Cheon, Eun Young (Dept. of Computer Science & Engineering Chungnam National University)
Choi, Sung-Ja (Dept. of Software Education Center, Gachon University)
Publication Information
Journal of Digital Convergence / v.17, no.10, 2019 , pp. 259-264 More about this Journal
Abstract
This paper proposes an image prediction system architecture for deep running in enterprise environment. Easily transform into an artificial intelligence platform for an enterprise environment, and allow sufficient deep-running services to be developed and modified even in Java-centric architectures to improve the shortcomings of Java-centric enterprise development because artificial intelligence platforms are concentrated in the pipeline. In addition, based on the proposed environment, we propose a more accurate prediction system in the deep running architecture environment that has been previously learned through image forecasting experiments. Experiments show 95.23% accuracy in the image example provided for deep running to be performed, and the proposed model shows 96.54% accuracy compared to other similar models.
Keywords
Deep Learning; Softmax; ReLU; CNN; Inception; Architecture;
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