ArticleComputational intelligence and neuroscience2022
Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.
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. It has been retracted, and should not be counted. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Retracted: Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/F05578717585E81AE7D0DD44FE927A
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropagation neural network (BPNN) model, it uses BPNN to analyze the economic development level of Overseas Chinese Town East (OCT East). Firstly, the weight of each index is determined by the Analytic Hierarchy Process (AHP), and the expected value of the comprehensive evaluation is obtained. Secondly, to ensure the validity of the evaluation model for the development level of the tourism complex, the BPNN model is trained and tested to enable it to be applied to the evaluation of the economic development level of OCT East. The development level of OCT East from 2012 to 2021 is divided into three stages: high, higher, and lower. The development characteristics and existing problems of the OCT East are analyzed, and the optimization strategy of the consumption economy of the scenic spots is put forward in a targeted manner. The research results manifest, that from 2012 to 2021, the development level index of OCT East increased from 0.2457 to 0.5304, and it was in a state of steady growth. In 2019, the development level index reached 0.6497, and it was upgraded to "high-level," but the average development level index of OCT East was only 0.5662, and there was a lot of room for improvement. According to the divided evaluation indicators, the development level of OCT East is evaluated. In 2012, the development level was low. From 2013 to 2018, it was at a high level, and from 2019 to 2021, it was a high level of development. By studying the Tourism Consumption Structure (TCS) of scenic spots in the OCT East, the research method of the consumption economic structure has been expanded. Therefore, it not only provides a reference for optimizing the consumption of scenic spots, but also contributes to the progress of the social tourism economy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.