Deep learning for light scattering computation: Reconstructing light scattering fields from 1-D randomly rough surfaces as an example

Yan, Keding; Yang, Shaorui; Zhao, Yingran; Ma, Chao; Jin, Yifei; Wang, Shouyu

doi:10.1016/j.cpc.2021.108183

Cited by 3 publications

(1 citation statement)

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“…It is not only an important advertisement expression, but also an important communication strategy for enterprises. The number of consumers who are exposed to deep learning is increasing, and the attractiveness of intelligent advertisement is constantly tested, making deep learning advertisement a widely used marketing expression (Yan et al, 2022). With the proposal of deep learning model, it is gradually applied to advertising media.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: The analysis of green advertisement communication strategy based on deep factorization machine deep learning model under supply chain management

Zhu

Jia

et al. 2023

Expert Systems

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Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation.

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Section: Introductionmentioning

confidence: 99%