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Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer
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Author(s): Feiqi Liu (School of Information Science and Engineering, Hebei North University, China), Dong Yang (School of Information Science and Engineering, Hebei North University, China), Yuyang Zhang (College of Robotics Science and Engineering, Northeastern University, China), Chengcai Yang (Zhuolu County Animal Husbandry and Fishery Service Center, China)and Jingjing Yang (School of Information Science and Engineering, Hebei North University, China)
Copyright: 2024
Volume: 20
Issue: 1
Pages: 19
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.336286
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Abstract
The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.
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