Evidence map›Paper›PMID 34204245›Full record

ArticleFoods (Basel, Switzerland)2021

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs.

Zhihao Hao, Guancheng Wang, Dianhui Mao, Bob Zhang, Haisheng Li, Min Zuo, Zhihua Zhao, Jerome Yen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

8 authors.

Zhihao HaoBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, Beijing 100048, China.
Guancheng WangDepartment of Computer and Information Science, University of Macau, Macau 999078, China.
Dianhui MaoBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, Beijing 100048, China.
Bob ZhangDepartment of Computer and Information Science, University of Macau, Macau 999078, China.ORCID 0000-0003-2497-9519
Haisheng LiBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, Beijing 100048, China.
Min ZuoBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, Beijing 100048, China.
Zhihua ZhaoSchool of Law, China University of Political Science and Law, Beijing 102249, China.
Jerome YenDepartment of Computer and Information Science, University of Macau, Macau 999078, China.

Funding

Beijing Municipal Philosophy and Social Science Foundation 19GLB036Beijing Technology and Business University, and the University of Macau MYRG2019-00006-FSTNational Key Technology R&D Program of China 2019YFC1605306National Social Science Fund of China 18BGL202Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety BTBD-2021KF05
6 · The paper itself

Abstract

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers' reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.

Indexed as

blockchaindeep learningfood marketregulation

Identifiers

PMID34204245
PMCPMC8234153

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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.