Volume 16 Number 3 (May. 2021)
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JSW 2021 Vol.16(3): 130-134 ISSN: 1796-217X
doi: 10.17706/jsw.16.3.130-134

Research on Key Technologies of Data Service Based on Adaptive Deep Learning

Zhigang Zhao1, Xinju Zhang2*
1(University of Science and Technology Beijing, Ministry of Emergency Management Communication Information Center, Beijing 100013, China).
2(Ministry of Emergency Management Communication Information Center, Beijing 100013, China)

Abstract—With the widespread adoption of big data technology, the diversity of data sources is continuously evolving. Data service technology is a technology derived from providing effective data interfaces for data applications. Based on the adaptive deep learning algorithm, this study proposes an improved service plan. First, the problem of randomly selecting the sending location of the data packet in the asynchronous random service scheme was analyzed, which leads to the waste of channel resources. Then, combined with the adaptive deep learning algorithm, an adaptive service scheme is specified. For the problem of large delay, the data frame is divided into multiple uniform position intervals. Therefore, the user learns the position interval until the user tends to select a position within the fixed position interval to send the data packet. Furthermore, in the algorithm’s iterative process, adaptive deep learning was usedbased on the ability of the algorithm to perform intensive learning. A detailed analysis of the various scenarios, the essential mode of the technology, and the computer simulation of the throughput and packet loss rate indicators of the proposed scheme in the three environments are provided to demonstrate the superiority of the proposed scheme.

Index Terms—Adaptive deep learning, data service, particle weights, asynchronous random


Cite: Zhigang Zhao, Xinju Zhang, "Research on Key Technologies of Data Service Based on Adaptive Deep Learning," Journal of Software vol. 16, no. 3, pp. 130-134, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)


General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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