Cognitive Linguistic Corpus Classification and Terminology Database Design Based on Multimedia Technology
Main Article Content
Abstract
With the continuous development of the social economy, multimedia has been valued more and more as a computer technology with strong professional technology and high application level. The quality of multimedia professionals is predicted and analyzed for multimedia professional talent through the Internet according to a Chinese keyword extraction algorithm. It realizes the extraction of keywords through Internet intelligence information acquisition for solving the problem of Internet information explosion, aiming to solve the talent quality prediction analysis. The prediction and analysis of multimedia professional talent quality play a crucial role in talent recruitment and development in the ever-evolving multimedia industry. This paper constructed a Fuzzy Secured Hybrid Search (FSHS) for keyword extraction in the Chinese Language. The proposed FSHS model computes the features in the text for the computation of the talent quality prediction for the extraction of the keywords. Through the utilization of the fuzzy logic model, the features in the text are computed and classification is performed classification and extraction of the features. The simulation results show that the Chinese keyword extraction algorithm has a high recall rate and precision rate, and can effectively predict the quality of professional talents.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Tai, K. H., Hong, J. C., Tsai, C. R., Lin, C. Z., & Hung, Y. H. (2022). Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experience. Computers & Education, 182, 104458.
Janssens, B., Bogaert, M., & Maton, M. (2023). Predicting the next pogačar: a data analytical approach to detect young professional cycling talents. Annals of Operations Research, 325(1), 557-588.
Austin, E. W., Austin, B. W., Willoughby, J. F., Amram, O., & Domgaard, S. (2021). How media literacy and science media literacy predicted the adoption of protective behaviors amidst the COVID-19 pandemic. Journal of health communication, 26(4), 239-252.
Zuo, Z., & Zhao, K. (2021). Understanding and predicting future research impact at different career stages—A social network perspective. Journal of the Association for Information Science and Technology, 72(4), 454-472.
Liu, S., & Wang, J. (2021). Ice and snow talent training based on construction and analysis of artificial intelligence education informatization teaching model. Journal of Intelligent & Fuzzy Systems, 40(2), 3421-3431.
Abdelfattah, F., Al Halbusi, H., & Al-Brwani, R. M. (2022). Influence of self-perceived creativity and social media use in predicting E-entrepreneurial intention. International Journal of Innovation Studies, 6(3), 119-127.
Guggemos, J., & Seufert, S. (2021). Teaching with and teaching about technology–Evidence for professional development of in-service teachers. Computers in Human Behavior, 115, 106613.
Xin, Y. (2021). Analyzing the quality of business English teaching using multimedia data mining. Mobile Information Systems, 2021, 1-8.
Woon, L. S. C., Mansor, N. S., Mohamad, M. A., Teoh, S. H., & Leong Bin Abdullah, M. F. I. (2021). Quality of life and its predictive factors among healthcare workers after the end of a movement lockdown: the salient roles of COVID-19 stressors, psychological experience, and social support. Frontiers in psychology, 12, 652326.
Allal-Chérif, O., Aranega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822.
Xu, Z., & Xiao, Y. (2021, June). Analyzation on the current situation of informatization talent team based on big data. In Journal of Physics: Conference Series (Vol. 1941, No. 1, p. 012065). IOP Publishing.
Hofmann, J., Schnittka, O., Johnen, M., & Kottemann, P. (2021). Talent or popularity: What drives market value and brand image for human brands?. Journal of Business Research, 124, 748-758.
Liu, H., Tan, K. H., & Pawar, K. (2022). Predicting viewer gifting behavior in sports live streaming platforms: The impact of viewer perception and satisfaction. Journal of Business Research, 144, 599-613.
Yang, R., Wang, L., Wu, C., Song, H., Hu, J., Jing, C., ... & Wang, H. (2021). Nomogram for Predicting Bone Development State of Female Children and Adolescents–A Fast Screening Approach Based on Pubes Stages for Growth and Development. Frontiers in Pediatrics, 9, 694958.
Tandon, A., Dhir, A., Talwar, S., Kaur, P., & Mäntymäki, M. (2022). Social media induced fear of missing out (FoMO) and phubbing: Behavioural, relational and psychological outcomes. Technological Forecasting and Social Change, 174, 121149.
Goldsmit, J., Schlegel, R. W., Filbee-Dexter, K., MacGregor, K. A., Johnson, L. E., Mundy, C. J., ... & Archambault, P. (2021). Kelp in the Eastern Canadian Arctic: Current and future predictions of habitat suitability and cover. Frontiers in Marine Science, 18, 742209.
Ceglar, A., & Toreti, A. (2021). Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting. Npj Climate and Atmospheric Science, 4(1), 42.
Litam, S. D. A., Ausloos, C. D., & Harrichand, J. J. (2021). Stress and resilience among professional counselors during the COVID‐19 pandemic. Journal of Counseling & Development, 99(4), 384-395.
Suryani, T., Fauzi, A. A., & Nurhadi, M. (2021). SOME-Q: A model development and testing for assessing the consumers’ perception of social media quality of small medium-sized enterprises (SMEs). Journal of Relationship Marketing, 20(1), 62-90.
Kim, H., Ham, Y. G., Joo, Y. S., & Son, S. W. (2021). Deep learning for bias correction of MJO prediction. Nature Communications, 12(1), 3087.
Liu, Q., Yuan, H., Hamzaoui, R., Su, H., Hou, J., & Yang, H. (2021). Reduced reference perceptual quality model with application to rate control for video-based point cloud compression. IEEE Transactions on Image Processing, 30, 6623-6636.
Chessman, H. M. (2021). Student affairs professionals, well-being, and work quality. Journal of student affairs research and practice, 58(2), 148-162.
Sudha, G., Sasipriya, K. K., Nivethitha, D., & Saranya, S. (2021, December). Personality prediction through CV analysis using machine learning algorithms for automated e-recruitment process. In 2021 4th international conference on computing and communications technologies (ICCCT) (pp. 617-622). IEEE.
Hanafizadeh, P., Shafia, S., & Bohlin, E. (2021). Exploring the consequence of social media usage on firm performance. Digital Business, 1(2), 100013.
Långh, U., Perry, A., Eikeseth, S., & Bölte, S. (2021). Quality of early intensive behavioral intervention as a predictor of children's outcome. Behavior Modification, 45(6), 911-928.
Liu, Q., Yuan, H., Su, H., Liu, H., Wang, Y., Yang, H., & Hou, J. (2021). PQA-Net: Deep no reference point cloud quality assessment via multi-view projection. IEEE transactions on circuits and systems for video technology, 31(12), 4645-4660.
Pekkala, K., & van Zoonen, W. (2022). Work-related social media use: The mediating role of social media communication self-efficacy. European Management Journal, 40(1), 67-76.
Ahmed, S., & Sheikh, A. (2021). Information and communication technology skills among library and information science professionals: A predictor of enhanced library services. Journal of Librarianship and Information Science, 53(3), 444-453.
Luo, A., & Yang, Y. (2021). Prediction and analysis of the quality of multimedia professional talents combining multiobjective data fuzzy evolution. Advances in Multimedia, 2021, 1-7.
Wenzhi, Z., Yenchun, W., Chuangang, S., & Hao, W. (2021). Social media for talent selection? a validity test of inter-judge agreement and behavioral prediction. Information Technology and Management, 22, 1-12.
Shen, G. (2022). AI-enabled talent training for the cross-cultural news communication talent. Technological Forecasting and Social Change, 185, 122031.
Han, Y., & Wang, Y. (2021). Investigating the correlation among Chinese EFL teachers' self-efficacy, work engagement, and reflection. Frontiers in Psychology, 12, 763234.
Tai, K. H., Hong, J. C., Tsai, C. R., Lin, C. Z., & Hung, Y. H. (2022). Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experience. Computers & Education, 182, 104458.