Evidence map›Paper›PMID 35265112›Full record

ArticleComputational intelligence and neuroscience2022

Intelligent Prediction and Rural Financial Development Based on Abnormal Detection of Sensor Data.

Jiong Liu

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Jiong LiuDepartment of Tourism and Commerce, Xuancheng Vocational and Technical College, Xuancheng, Anhui 242000, China.ORCID https://orcid.org/0000-0003-3237-7139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wireless sensor network is a multisensor wireless network system, which consists of multiple sensors and is configured independently. Because the network generates a large amount of data, the frequency, performance, and computing power of sensor nodes are limited, and they are particularly vulnerable to harsh environments and malicious attackers. This leads to the occurrence of malicious nodes, emergencies, and abnormal data in the sensor network system. Failures can also have a significant impact on sensor network services. The two main functions of wireless sensor network security are abnormal node detection and data anomaly detection. These two directions are mutually independent and complementary. Therefore, under the promotion of the rural revitalization strategy and the precision poverty alleviation strategy, China has increased its agricultural efforts. At this stage, all localities focus on the construction of rural financial systems to ensure that scattered farmers and rural small and micro-enterprises receive comprehensive financial services. The establishment of a rural financial system based on "intelligent forecasting" can improve financial development theories and build new ideas for rural financial development. And, the balance between realization and profitability, and then, through the use of Internet technology to make traditional financial institutions more effective in providing financial services, new online financial platforms can use them to make up for the existing shortcomings of traditional financial institutions as much as possible. In this article, through the research on the intelligent prediction of sensor data anomaly detection, it is applied to the development of rural finance and promotes the development of rural finance.

Indexed as

TechnologyWireless TechnologyChina

Identifiers

PMID35265112
PMCPMC8901299

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.