Small sample prediction of Wordle games based on optimized convolutional neural networks

Xu Mo; Weizhi Wang; Shuaikang Yang; Yunxiang Wang; Yiming Geng

doi:10.1117/12.2685904

28 July 2023 Small sample prediction of Wordle games based on optimized convolutional neural networks

Xu Mo, Weizhi Wang, Shuaikang Yang, Yunxiang Wang, Yiming Geng

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564G (2023) https://doi.org/10.1117/12.2685904
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

The Wordle game was very popular after it was launched, attracting many players in the early stage. Under the rules of the game, the different characteristics of words may affect the distribution of scores in the different mode. The purpose of this report is to establish a model to study on the percentage distribution of attempts to submit reports. The innovation of this paper lies in the combination of convolutional neural network (CNN), bidirectional long and short term memory network (BiLSTM) and double attention mechanism (DA), and proposes a DCNN-BiLSTM network structure suitable for small sample prediction. Finally, a good prediction result is obtained.

Citation Download Citation

Xu Mo, Weizhi Wang, Shuaikang Yang, Yunxiang Wang, and Yiming Geng "Small sample prediction of Wordle games based on optimized convolutional neural networks", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564G (28 July 2023); https://doi.org/10.1117/12.2685904

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