Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84%±0.156% (accuracy), 99.84%±0.153% (precision), 99.84%±0.156% (sensitivity), and 99.84%±0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.

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“…There is also some related work from the field of science such as [18,19]. These related studies provide the scientific background of this study.…”

Section: Data Augmentationmentioning

confidence: 99%

A Hybrid Framework for Lung Cancer Classification

2022

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“…The basic idea of MP is based on the decomposability and reconstruction of the signal by adaptively searching for time-frequency atoms that can match the local features of the signal in an overcomplete library, and finally representing the signal as a linear combination of time-frequency atoms [35]. This algorithm provides a sparse linear expansion of the waveform to decompose the signal over an overcomplete function dictionary.…”

Section: Mp Time-frequency Feature Extractionmentioning

confidence: 99%

Sustainable development of vocal music ecology in a digital ecological environment

Liu

2023

3C TIC

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The phenomenon of noise interference during vocal transmission can lead to the problem of poor vocal transmission quality. This paper proposes a study on the sustainability of vocal ecology in a digital ecological environment. First, the matching tracking algorithm can extract the time-frequency characteristics of the effective signal, attenuate the interference of noise, and improve the propagation quality. A sustainable development GA-BP network model is established, and the adjustment amount of each weighting coefficient is obtained according to the gradient algorithm and using the inertia adjustment strategy. The coordination is regulated through feedback control strategies to ultimately achieve ecological sustainability of vocal music. The analysis results show that the average relative error of the simulation prediction of the sustainable development GA-BP network model is 3.54%, the maximum relative error is 8.11%, and the average relative error is within 5% of 70%. It has significant superiority and high efficiency in comparison with the prediction degree of the traditional model.

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“…However, the system's robustness is significantly reduced in the case of noise. This requires that these features be handled correctly [5]. Specific processes can be dissected in detail below.…”

Section: Robustness Of Characteristic Parametersmentioning

confidence: 99%

3D Scene Simulation System of Japanese Teaching Based on Finite Element Equation

Shi

2023

Applied Mathematics and Nonlinear Sciences

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Recognizing vowels in Japanese second speech processing is a common research topic. Its disadvantage is that separate speech and context recognition produces semantic and font processing interference. This study uses Chinese and Japanese words with the same syllables, font, and meaning as experimental materials. The finite element analysis method is used to simulate the three-dimensional scene. It is found that the difference in second language recognition ability comes from the auditory ability of different people. Different ways of acquiring a second language, contact experience, and other factors will affect different learning environments. Second language learning will have a certain effect on the vowelization of the mother tongue. This paper analyzes the cognitive characteristics of the same vowel in different contexts, languages, and frequencies. This opens up a new horizon for the development of English learning.

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Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

Cited by 7 publications

References 12 publications

A Hybrid Framework for Lung Cancer Classification

A Hybrid Framework for Lung Cancer Classification

Sustainable development of vocal music ecology in a digital ecological environment

3D Scene Simulation System of Japanese Teaching Based on Finite Element Equation

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