基于transformer和自适应嵌入策略的可逆信息隐藏

Zhou, Linna; Lu, Zhigao; You, Weike; Fang, Xiaofei

doi:10.1631/fitee.2300041

Cited by 1 publication

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, y(i) denotes the observed value of the rabbit housing environment at the ith instance, y pred(i) signifies the corresponding predicted value, and n stands for the length of the sequence used for verification. The variables S RMSE , S MAE , and S MAPE correspond to the values of RMSE, MAE, and MAPE, respectively (Zhou et al, 2023).…”

Section: Experimental Settingsmentioning

confidence: 99%

Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer

Liu,

Yang,

Zhang

et al. 2024

International Journal of Data Warehousing and Mining

View full text Add to dashboard Cite

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.

show abstract

Section: Experimental Settingsmentioning

confidence: 99%