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Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions
Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz
Article type: Research Article
Authors: Wang, Ruijuana | Zhuo, Weib; *
Affiliations: [a] School of Software, Hanwei Internet of Things Research School, Zhengzhou University, Zhengzhou Henan, China | [b] Henan Institute of Big Data, Zhengzhou University, Zhengzhou Henan, China
Correspondence: [*] Corresponding author. Wei Zhuo, Henan Institute of Big Data, Zhengzhou University, Zhengzhou Henan, China. E-mail: [email protected].
Abstract: The image intelligent processing analysis technology uses a computer to imitate and execute some intellectual functions of the human brain, and realizes an image processing system with artificial intelligence, that is, an image processing analysis technology is an understanding of an image. The degree of intelligent automated analysis and processing is low, many operations need to be done manually, causing human error, inaccurate detection, and time-consuming and laborious. Deep learning method can extract features step by step in the original image from the bottom to the top. Therefore, based on feature analysis technology, this paper uses the deep learning method to intelligently and automatically analyse the visual image. This method only needs to send the image into the system, and then the manual analysis is not needed, and the analysis result of the final image can be obtained. The process is completely intelligent and automatically processed. First, improve the deep learning model and use massive image data to choose and optimize parameters. Results indicate that our method not only automatically derives the semantic information of the image, but also accurately understands the image accurately and improve the work efficiency.
Keywords: Image intelligent, automatic processing analysis, deep learning
DOI: 10.3233/JIFS-189447
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6081-6090, 2021
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