2021
DOI: 10.12700/aph.18.7.2021.7.3
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Separation of Several Illnesses Using Correlation Structures with Convolutional Neural Networks

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Cited by 9 publications
(3 citation statements)
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“…( 6), h i represents the height of the input image; f represents the width of the convolution kernel; s represents the step size of the convolution; and p represents the number of expanded outer layer 0 s. The wide expression of input and output after convolution is shown in Eq. (7).…”
Section: Application and Improvement Of Recommendation Algorithm Base...mentioning
confidence: 99%
See 1 more Smart Citation
“…( 6), h i represents the height of the input image; f represents the width of the convolution kernel; s represents the step size of the convolution; and p represents the number of expanded outer layer 0 s. The wide expression of input and output after convolution is shown in Eq. (7).…”
Section: Application and Improvement Of Recommendation Algorithm Base...mentioning
confidence: 99%
“…Currently, there are many applications of recommendation algorithms, including various hybrid models [6]. Convolutional neural networks are widely used in data extraction in multiple disciplines due to their apparent advantages in data feature processing [7]. In the human visual system, if the attention is high, the time for humans to process complex data information will decrease.…”
Section: Introductionmentioning
confidence: 99%
“…The proposal of the convolutional layer greatly improves the classification accuracy and has recently been widely used in the classification field [19]. The convolutional feature extractor can extract features from images automatically [20], but it requires adequate training samples, long training time, and high-performance execution equipment, which increases industrial production costs and limits its industrial application.…”
Section: Introductionmentioning
confidence: 99%