Evidence map›Paper›PMID 35341201›Full record

ArticleComputational intelligence and neuroscience2022

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning.

Ruifeng Zhang, Xiaoyan Zhou, Yanfeng Jin, Jing Li

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Ruifeng ZhangShijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, Hebei 050021, China.ORCID https://orcid.org/0000-0003-0559-8542
Xiaoyan ZhouShijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, Hebei 050021, China.ORCID https://orcid.org/0000-0001-8805-6153
Yanfeng JinShijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, Hebei 050021, China.ORCID https://orcid.org/0000-0002-3507-5403
Jing LiShijiazhuang Posts and Telecommunications Technical College, Shijiazhuang, Hebei 050021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products.

Indexed as

AlgorithmsMachine LearningElectronics

Identifiers

PMID35341201
PMCPMC8947898

What OpenQuestion holds

Textmetadata
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.