Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources

Karn, Arodh Lal; Webber, Julian; Mehbodniya, Abolfazl; David, D. Stalin; Subramaniam, Balu; Rangasamy, Rajasekar; Sengan, Sudhakar

doi:10.1007/978-981-19-7455-7_28

Cited by 1 publication

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“…These resources cover various fields, from basic education to higher education and from subject teaching to vocational skill training, and exist in rich and diverse forms, such as audio explanations, teaching videos, online courses, and so on [18] [19]. With intuitive visual and auditory information as the medium, these resources convey knowledge and information more vividly and concretely, bringing great convenience to learners [20] [21]. However, with the rapid increase in audio learning resources, some problems have occurred in their management and utilization.…”

Section: Introductionmentioning

confidence: 99%

Classification and Retrieval of Multimedia Audio Learning Resources

Zhang

2023

Int. J. Emerg. Technol. Learn.

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With the development of the Internet and new media, multimedia and audio learning resources have been widely used in teaching and learning. However, their classification and retrieval have become important and urgent issues to be addressed. This study conducted in-depth research on the classification system, construction, and retrieval of multimedia audio learning resources, with the aim of solving several problems with existing research methods, such as timeconsuming manual labeling, inconsistent labeling, and traditional retrieval methods neglecting the correlation between audio and metadata. First, a classification model of audio learning resources was constructed. It processed single-mode data from audios and annotated texts and further abstracted the single-mode information into high-level feature vectors. Then the complementarity between multi-modalities was used to fuse the abstract features or decisionmaking results and eliminate information redundancy between modalities, thereby learning a better feature representation of multimedia audio learning resources. Second, a retrieval method for the resources based on self-similarity matrix filtering was proposed, which aimed to improve the accuracy and efficiency of retrieval. This study provides a new theoretical and practical perspective for classifying and retrieving multimedia audio learning resources.

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Section: Introductionmentioning

confidence: 99%

Classification and Retrieval of Multimedia Audio Learning Resources

Zhang

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources

Cited by 1 publication

References 12 publications

Classification and Retrieval of Multimedia Audio Learning Resources

Classification and Retrieval of Multimedia Audio Learning Resources

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