Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

Zhang, Hehua; Li, Yang; Wei, Anhai; Duan, Aowen; Li, Yongming; Zhao, Ping; Li, Yongqin

doi:10.21037/atm-20-5906

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…At the same time, with the further liberalization of current items and capital items, it will also further increase the risk of foreign exchange transactions and translation [ 14 ]. Zhang et al found that, in addition, after joining the WTO, more and more foreign financial institutions will enter China, enterprises will have more financing channels and methods, the connection with the international capital market is closer, but at the same time, it also increases the exchange rate risk of enterprises, and it further enhances the impact of foreign exchange risk on enterprises [ 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Tao

2022

Computational Intelligence and Neuroscience

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In order to solve the problem that corporate financial risks seriously affect the healthy development of enterprises, credit institutions, securities investors, and even the whole of China, the K-means clustering algorithm, the risk screening process, and the Gaussian mixture clustering algorithm, the risk screening process, are proposed; experiments have shown that although the number of high-risk companies selected by the K-means algorithm is small, only 9% of the full sample, the high-risk cluster can contain nearly 30% of the new “special treatment” companies. If the time period is extended to the next 5 years, this proportion will be higher. Finally we found that if the prediction of “special handling” events is used as the criterion for evaluating high-risk clusters, then K-means clustering can effectively screen out those risky companies that need to be treated with caution by investors. The validity of the experiment is verified.

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Section: Literature Reviewmentioning

confidence: 99%

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Tao

2022

Computational Intelligence and Neuroscience

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A systematic survey of fuzzy deep learning for uncertain medical data

Zheng,

Xu,

et al. 2024

Artif Intell Rev

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

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The Measurement of Metal Mineral Particle Size Under the Microscope Based on Gaussian Pyramids and Directional Maximum Intercept

Luo,

Xie,

et al. 2024

Minerals

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With the development of mineral resources, minerals are becoming increasingly difficult to process. In order to utilize these resources more effectively, in-depth research into process mineralogy has become increasingly important in the field of mineralogy, and particle size measurement under the microscope is one of the critical aspects of process mineralogy. At present, the use of scanning electron microscopes and other equipment for measurement is very expensive, and manual measurement has problems such as poor accuracy and low efficiency. In addition, there is a lack of reference materials for the segmentation algorithm of mineral light images. This article proposes a Gaussian pyramid based on bilateral filtering combined with directional maximum intercept to measure mineral particle size under the microscope. In the experiments, different segmentation algorithms were studied, including Gaussian pyramid segmentation based on bilateral filtering, segmentation based on Fuzzy C-Means, and the rapidly developing deep learning segmentation algorithms in recent years. By comparing the segmentation effects of these three algorithms on various mineral thin-section images, the Gaussian pyramid segmentation algorithm based on bilateral filtering was selected as the optimal one. This was then combined with the directional maximum intercept method to measure the particle size of ilmenite and pyrite images. The experimental results show that the segmentation method based on the bilateral filtering Gaussian pyramid technique has higher segmentation accuracy than the other two algorithms, and can accurately measure the particle size of minerals under the microscope. Compared with manual measurement, this method can effectively and accurately measure the microscopic particle size of target minerals, greatly reducing the workload of measurement personnel and reducing the time spent on measurement.

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Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

Cited by 3 publications

References 30 publications

Identification of Enterprise Financial Risk Based on Clustering Algorithm

Identification of Enterprise Financial Risk Based on Clustering Algorithm

A systematic survey of fuzzy deep learning for uncertain medical data

The Measurement of Metal Mineral Particle Size Under the Microscope Based on Gaussian Pyramids and Directional Maximum Intercept

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