Separation of Several Illnesses Using Correlation Structures with Convolutional Neural Networks

Jenei, Attila Zoltán; Kiss, Gábor; Tulics, Miklos Gabriel; Schiferl, D.

doi:10.12700/aph.18.7.2021.7.3

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%

“…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%

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Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Li¹,

Yin²,

Hui³

2023

Computers, Materials &Amp; Continua

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With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people's livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the neural network framework. Through the text convolutional network, the input layer in CNN has transformed into two channels: static ones and non-static ones. Meanwhile, the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher. The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding. It obtains data name features through a convolution kernel. Finally, the top pooling layer obtains the length vector. The attention system layer obtains the characteristics of the data type. Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage. The proposed algorithm shows excellent accuracy and robustness.

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Section: Application and Improvement Of Recommendation Algorithm Base...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Li¹,

Yin²,

Hui³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…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%

A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Zhu

Ding

et al. 2021

Electronics

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Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

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Detection of Speech Related Disorders by Pre-trained Embedding Models Extracted Biomarkers

Jenei

Kiss

Schiferl

2022

Lecture Notes in Computer Science

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Separation of Several Illnesses Using Correlation Structures with Convolutional Neural Networks

Cited by 9 publications

References 0 publications

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Detection of Speech Related Disorders by Pre-trained Embedding Models Extracted Biomarkers

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