Design of Data Trend Analysis Algorithm in Multimedia Teaching Communication Platform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146870" target="_blank" >RIV/00216305:26220/22:PU146870 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11036-021-01880-9" target="_blank" >https://link.springer.com/article/10.1007/s11036-021-01880-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11036-021-01880-9" target="_blank" >10.1007/s11036-021-01880-9</a>
Alternative languages
Result language
angličtina
Original language name
Design of Data Trend Analysis Algorithm in Multimedia Teaching Communication Platform
Original language description
Traditional methods have some problems such as low analysis accuracy and long time consumption in analyzing trend data of multimedia teaching and communication platform. Therefore, this paper designs a new trend analysis algorithm in multimedia teaching and communication platform.The linear regression model is used to segment the data flow of the multimedia teaching and communication platform, the inverse lemma of a matrix is introduced to modify the model parameters of the data trend analysis of the multimedia teaching and communication platform, and the recursive regression modeling is used to design the data trend analysis algorithm of the multimedia teaching and communication platform.To verify the effectiveness of this method in analyzing data trends in the multimedia teaching communication platform, a comparative experiment is designed.The results show that the algorithm in this paper has a significant clustering trend and shorter trend analysis time when analyzing the changing trend of multimedia teaching communication platform data and can effectively improve the accuracy of clustering trend judgment.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MOBILE NETWORKS & APPLICATIONS
ISSN
1383-469X
e-ISSN
1572-8153
Volume of the periodical
2022
Issue of the periodical within the volume
2
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
10
Pages from-to
„“-„“
UT code for WoS article
000751595600004
EID of the result in the Scopus database
2-s2.0-85124296497