Psychological Analysis on of Network Rumors Spread in Covid-19 Epidemic

Tian, Wutao; Li, Xiao; Jiao, Shurui

doi:10.24869/psyd.2022.595

Cited by 1 publication

(1 citation statement)

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“…The literature [12] starts from a neural network algorithm and combines with big data recognition technology to analyze the emotional expression of oil painting subjects in the public environment and establish an analysis model of emotional expression of oil painting creation based on big data and neural networks. The literature [13] argues that color is also a carrier for painters to convey emotions in oil paintings, and studying emotional color expressions in oil paintings is more helpful for people to understand the emotions conveyed by oil paintings. Literature [14], however, argues that it is the emotional expression of oil painting figures that is the important field of oil painting creation and that the depiction and expression of melancholic psychology in high-quality figure oil painting is highly unified emotion and art and that artists who are brave to explore and innovate will try to find new forms of expression to express their inner feelings in oil painting properly.…”

Section: Introductionmentioning

confidence: 99%

Modeling the connection between oil painting creation and university students’ inspiration in the context of “Internet+”

Tao,

Zhao,

Sahir

2023

Applied Mathematics and Nonlinear Sciences

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In the Internet era, oil painting creation requires constant observation and learning before it can be inspired, and the inspiration for oil painting creation is like a godsend. This paper constructs a fast Fourier transform convolutional model based on fast Fourier transform and convolutional neural network to study the connection between oil painting creation and university students’ inspiration in the context of “Internet+”. The model was evaluated algorithmically, and a dataset of oil paintings was built to investigate the influence of the hue and brushwork technique on students’ inspiration. From the algorithm evaluation, the algorithm of this paper’s model reduces the floating-point computation from 34.76G to 20.08G than the traditional time-domain convolution algorithm, which reduces the floating-point computation by nearly 15G and greatly improves the running speed of this paper’s model. From the hue examples, the percentage of blue-gray, pale white, and light blue oil paintings are 34.95%, 18.59%, and 10.73%, respectively. Various hues will bring students different emotional expressions and induce their creative inspiration. In terms of brushwork techniques, the average percentages of the six brushwork techniques are 16.53%, 22.21%, 15.68%, 11.73%, 14.69%, and 19.16%, respectively. The fast Fourier transform convolution model established under the background of “Internet+” can effectively analyze the different tones and brushstroke techniques in oil paintings so that students can feel the emotion and vitality of the works in the process of observation and thus promote the generation of inspiration for oil painting creation of college students.

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Section: Introductionmentioning

confidence: 99%