Special Issue "Selected Papers from 2022 5th International Conference on Big Data and Education"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 1700

Special Issue Editors

Prof. Dr. Jerry Chun-Wei Lin
E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: AI and machine learning; data analytics; optimization; soft computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiwen Zhang
E-Mail Website
Guest Editor
Department of Digital Media, School of Information Science, Beijing Language and Culture University, Beijing, China
Interests: pattern recognition, computer vision and human-computer interaction

Special Issue Information

Dear Colleagues,

Big Data are an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process data within a tolerable elapsed time. Such datasets are often from various sources (variety) yet unstructured, such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions, and web logs, and are of large size (volume) with fast data in/out (velocity). More importantly, big data have to be of high value (value) and establish trust in them for business decision making (veracity). Various technologies are being discussed to support the handling of big data, such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. Please join us in discussing the exciting topics above at the ICBDE 2022!

We welcome and encourage the submission of high-quality, original papers, which are not being submitted simultaneously for publication elsewhere. The ICBDE 2022 welcomes the submission of papers concerning any branch of the Big Data and education, and their applications in education and other subjects. The subjects covered by the ICBDE 2022 include Big Data applications, Big Data algorithms, e-learning, online education, digital classrooms, Big Data mining and Analytics, etc., and their applications.

Conference Link:

http://www.icbde.org

 

Prof. Dr. Jerry Chun-Wei Lin
Prof. Dr. Xiwen Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Article
Design of Intelligent Management Platform for Industry–Education Cooperation of Vocational Education by Data Mining
Appl. Sci. 2022, 12(14), 6836; https://doi.org/10.3390/app12146836 - 06 Jul 2022
Cited by 1 | Viewed by 568
Abstract
Data are playing an increasingly important role in the development of industry–education cooperation strategies in vocational education and training. The objective of this study was to promote the comprehensive progress of an industry–education cooperation system and improve the effect of the application of [...] Read more.
Data are playing an increasingly important role in the development of industry–education cooperation strategies in vocational education and training. The objective of this study was to promote the comprehensive progress of an industry–education cooperation system and improve the effect of the application of big data technology in this system. First, we designed of a big data technology application in an intelligent management platform system for industry–education cooperation. Second, we analyzed the synthetical design of the system. Finally, we optimized and designed a support vector machine (SVM) data mining (DM) algorithm model based on big data, and evaluated the model. The results revealed that the designed algorithm model provides outstanding advantages compared with similar algorithm models. In general, the highest average computation time of the designed SVM algorithm model is about 95 ms. The overall average calculation time linearly decreases around 200 iterations and tends to be stable, and the lowest overall average computation time is about 20 ms. In the DM process, the highest accuracy rate of the model is about 97%, and the lowest is about 92%. The DM accuracy rate is always stable as the number of iterations of the model continues to increase. The designed model slowly increases the occupancy rate of the system in the process of increasing computing time. At about 60 min, the system occupancy rate of the model tends to be stable, and the highest is maintained at about 23%. This study not only provides technical support for the optimization of DM algorithms with big data technology, but also contributes to the integrated development of industry–education cooperation systems. Full article
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