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RETRACTED ARTICLE: Marine oil pollution remediation and enterprise capital efficiency based on improved neural network

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Arabian Journal of Geosciences Aims and scope Submit manuscript

This article was retracted on 29 November 2021

An Editorial Expression of Concern to this article was published on 28 September 2021

This article has been updated

Abstract

Neural network algorithm is a kind of artificial intelligence algorithm that simulates the working process of human brain neurons. Neural network algorithm has a high fault tolerance and a strong adaptability in the phase of use, which is good for complex nonlinear operation. At the same time, it is also an important model for the successful combination of physics, training, and the transition from an industrial system to an intelligent system. The most obvious aspect of a neural network algorithm is that it can form a physical knowledge fusion system. Its most impressive qualities are intellect, precision, and customization. Accordingly, based on the criteria of Industry 4.0, our research focuses on the creation of a control framework for the treatment of offshore oil pollution by using the concept of knowledge on physical fusion in order to ensure the proper operation of offshore oil pollution treatment facilities. In particular, the neural network enhances the performance of the system through continuous learning, that is, abstraction, simplification, and simulation. Many researchers have performed in-depth and comprehensive research on their adaptive and self-learning abilities in different application scenarios. Multilayer feedforward neural network is a kind of neural network formed by an error back propagation algorithm. It is seen in a number of different scenes. The successful combination of offshore oil pollution treatment and neural networks will not only increase the performance of companies’ resources and sewage treatment but also reduce the cost of sewage treatment. This paper will therefore research the role of the neural network in the remediation of marine oil pollution combined with enterprise capital efficiency and on the basis of particular scenarios for predicting water quality parameters.

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Authors and Affiliations

  1. Science and Technology Finance and Mathematical Finance Key Laboratory of Sichuan Province, Business School of Sichuan University, 29 Wangjiang Road, Chengdu, 610064, China

    Liangbing Yang

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  1. Liangbing Yang
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Contributions

Liangbing Yang designed the research framework and wrote the manuscript, and he was responsible for proofreading and optimization of the results.

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Correspondence to Liangbing Yang.

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The authors declare no competing interests.

Additional information

Responsible Editor: Ahmed Farouk

This article is part of the Topical Collection on Big Data and Intelligent Computing Techniques in Geosciences

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12517-021-09028-5

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Yang, L. RETRACTED ARTICLE: Marine oil pollution remediation and enterprise capital efficiency based on improved neural network. Arab J Geosci 14, 463 (2021). https://doi.org/10.1007/s12517-021-06806-z

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  • DOI: https://doi.org/10.1007/s12517-021-06806-z

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