ArticleScientific reports2025
An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
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7 authors.
Funding
Abstract
The air jet loom is an energy-intensive machine, it is significantly reducing air consumption of relay nozzles for saving energy of air compressor. This paper proposes a Convolutional Neural Network (CNN)-Attention regression model to predict air consumption of the relay nozzle, enhancing accuracy and efficiency with an Improved Football Team Training Algorithm (IFTTA). We initially presented the architectural CNN-Attention model for predicting air consumption of relay nozzles. Then, the hyperparameters of CNN-Attention model were automatically tuned using an IFTTA algorithm that imitates the collaboration in football team training. Finally, experimental validation was performed. The IFTTA-CNN-Attention model stands out with the lowest mean absolute error (MAE) of 0.8686, root mean square error (RMSE) of 1.1027, and the highest determination coefficient (R
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