ArticleHeliyon2024
Cultivation strategies of English thinking ability in the environment of Internet of Things.
Article in Heliyon, 2024. 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 It has been retracted, and should not be counted. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Retraction · 2025-04-23Author Unresponsive · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aims to broaden the horizons of English learners and solve the problem of insufficient cultivation of English thinking. With the widespread use of the Internet of Things (IoT) and from the perspective of deep learning, the Local Similar Convolutional Neural Network (LSNN) recommendation model is designed by adding adjustment layers to the Convolutional Neural Network (CNN). The LSNN model alleviates the sparsity of data. Through comparative experiments on related data, the data sparsity is 0.7-0.9. The LSNN prediction is higher than that of Euclidean Distance and Pearson correlation coefficient, which proves that LSNN can alleviate data sparsity. The LSNN model has the lowest mean absolute error (MAE) of 0.83, which is smaller than the previous CNN's MAE. The LSNN model recommends the expansion books they need most for English learners, and then improves the vision of English learners, thereby strengthening the cultivation of English learners' thinking ability.
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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.