Harr-NMF Feature Extraction for Multilevel Educational Technology Teaching Big Data System. (18th May 2022)

• Record Type: Journal Article
• Authors: Fu, Siyi; Mojtahe, Kaza
• Other Names: Arif Muhammad Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-05-18
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/3178763
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : An essential role in improving educational informatization and encouraging new approaches to education is played by educational technology, a specialist field that supports both theorising and doing in this area. When it comes to learning educational technology, students must have excellent professional knowledge and abilities, as well as a thorough understanding of data. Harr-NMF feature extraction for educational technology teaching large data systems is the focus of this project. With the help of big data technology, the construction of a teaching resource sharing system provides useful solutions for the storage and sharing of current and future massive teaching resources, enables fast and accurate analysis and retrieval of massive teaching resources and data, improves the security and scalability of the system while maintaining system stability, and promotes education teaching informatization to a new level. The system can be used to analyze and retrieve data quickly and accurately. By mapping different types of Harr-NMF features into a gradient matrix using grayscale projection, pattern classification is performed through the simplified gradient matrix as features. The algorithm complexity of feature extraction is eventually reduced to a great extent by the fast advantage of feature extraction by this algorithm. The Harr-NMF algorithm and the similarity relation matrix are used to obtain the optimal parameter values and the corresponding affiliation matrix and class centers, thus providing effective initial values for Harr-NMF, which effectively solves the problems of slow convergence and easy to fall into local minima of the Harr-NMF algorithm. The algorithm of this paper is compared with several improved algorithms of Harr-NMF in terms of convergence, recognition rate, and other performance to verify the feasibility and effectiveness of this paper. With a combination of qualitative and quantitative approaches, the data literacy of teaching educational technology is dissected and constructed to enhance its data literacy and improve the quality of education technology discipline personnel training.