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Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications
Guest editors: Varatharajan Ramachandran
Article type: Research Article
Authors: Cao, Jingxin; *
Affiliations: School of Computer, Xi’an Aeronautical University, Xi’an, China
Correspondence: [*] Corresponding author. Jingxin Cao, School of Computer, Xi’an Aeronautical University, Xi’an, China. E-mail: [email protected].
Abstract: With the continuous progress of the times, the development of college education is also constantly tending to enrich and diversify. In the course of curriculum setting in many colleges and universities, more and more attention is paid to the teaching of computer courses for college students. In the course of setting up and teaching, we still follow the traditional teaching mode and do not pay more attention to students’ practical practice. By observing the computer course materials selected by many colleges and universities for students, we can see that most of the textbooks still focus on arranging some exercises from the point of view that science and engineering are students, and lack the basic knowledge of the curriculum. Because the understanding and research of computer course is not deep enough, the teaching effect obtained since the course is not ideal. By studying the relevant knowledge of machine learning and some important problems in the development of neural network algorithm theory, this paper puts forward some viewpoints based on the current curriculum system in colleges and universities in order to improve the learning quality of computer courses. And hope to build a variant learning model to improve students’ interest in computer courses. The exploration and inference of some knowledge in this paper are mostly my own views, some places are not professional enough, the majority of experts and scholars can criticize and correct at will.
Keywords: Computer public course, machine learning, neural network algorithm, teaching reform
DOI: 10.3233/JIFS-189518
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6865-6876, 2021
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