Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146871" target="_blank" >RIV/00216305:26220/22:PU146871 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11036-022-02068-5" target="_blank" >https://link.springer.com/article/10.1007/s11036-022-02068-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11036-022-02068-5" target="_blank" >10.1007/s11036-022-02068-5</a>
Alternative languages
Result language
angličtina
Original language name
Logistic Regression Analysis of Targeted Poverty Alleviation with Big Data in Mobile Network
Original language description
In order to improve the identification accuracy and shorten the analysis time of poor households in poverty alleviation, this paper studies a logistic regression analysis algorithm of targeted poverty alleviation based on mobile big data. Based on the theories related to big poverty alleviation data, Apriori algorithm is used to mine the basic information of households collected through mobile network based on Maslow's hierarchy of needs theory. A multi-dimensional item data of poverty detection is obtained by analyzing the frequent itemsets of association rules in poor areas, and the poverty characteristics of poor areas from different dimensions are analyzed. Taking the big data platform of targeted poverty alleviation in Jiangxi Province, China, as an example, the economic assistance data is selected and sent into the k-means algorithm to cluster by taking the village as the unit. Then, combined with the correlation of poverty characteristics, the abnormal phenomena in poverty alleviation are found, and the effectiveness of the targeted assistance to poverty alleviation target areas is analyzed. Based on nonlinear logistic regression, the identification model of poor households is built, and the Spark frame is used to extract, transform and read the characteristics of samples respectively. Finally, the poor households are identified with the logistic regression algorithm. Experimental results show that the average recognition accuracy of poor households reaches 92%, and the mining time of poverty feature analysis is only 18 s, which improves the efficiency of data analysis than current algorithms.
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
12
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
„“-„“
UT code for WoS article
000896491200001
EID of the result in the Scopus database
2-s2.0-85143618666