Current Situation and Innovative Methods of Brass Music Teaching Based on Network Information Technology
Main Article Content
Abstract
The intersection of education and technology has witnessed a significant transformation in recent years, with a particular focus on optimizing remote learning experiences. Maintaining network resources for brass music education is crucial to ensure a seamless and effective learning experience. Hence, this paper proposed SDNcOIT (Software-Defined Networking cloud Optimal Information Technology) model in the context of brass music education, a discipline that demands real-time audio and video interactions. The proposed SDNcOIT model uses the cloud environment integrated SDN architecture for the evaluation of brass music education. The constructed network comprises Software-Defined Networking, cloud technology, and a rule-based model, the SDNcOIT system presents a compelling case for enhancing the delivery of music education to students worldwide. The SDNcOIT model implemented the optimization model within the SDN environment for the analysis of the evaluation of the user experience in the cloud environment. The proposed SDNcOIT model uses the gradient descent optimization model for the computation of the brass music in the cloud environment. Through the implementation of the optimization process within the SDNcOIT the features are optimized in the cloud with SDN architecture for the analysis of user experience. The findings from this study reveal substantial performance improvements over time, exemplified by reduced latency, increased throughput, minimized packet loss, and elevated user satisfaction. These improvements are pivotal for a discipline where the quality of audio and video content is paramount. The scalability of the cloud infrastructure ensures that the system can accommodate varying dataset sizes, adapting seamlessly to the dynamic requirements of a growing brass music program. Furthermore, the rule-based model's growing adherence to network behavior results in an exceptionally efficient and well-optimized network, aligning harmoniously with the educational objectives of brass music instruction.
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References
He, W., Zhang, Z. J., & Li, W. (2021). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International journal of information management, 57, 102287.
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565.
Abdelraheem, A., Hussaien, A., Mohammed, M., & Elbokhari, Y. (2021). The effect of information technology on the quality of accounting information. Accounting, 7(1), 191-196.
Abdalla Hamza, P., Gardi, B., Hamad, H., & Anwar, G. (2021). Analysis the impact of Information technology on Efficient tax Management. Bayar and Hamad, Hawkar and Anwar, Govand, Analysis the impact of Information technology on Efficient tax Management (December 6, 2021).
Vidmar, D., Marolt, M., & Pucihar, A. (2021). Information technology for business sustainability: a literature review with automated content analysis. sustainability, 13(3), 1192.
Zahid, A., Poulsen, J. K., Sharma, R., & Wingreen, S. C. (2021). A systematic review of emerging information technologies for sustainable data-centric health-care. International Journal of Medical Informatics, 149, 104420.
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23, 987-1005.
Kulkarni, V., Sahoo, S. K., Thanikanti, S. B., Velpula, S., & Rathod, D. I. (2021). Power systems automation, communication, and information technologies for smart grid: A technical aspects review. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(3), 1017-1029.
Ali, O., Shrestha, A., Osmanaj, V., & Muhammed, S. (2021). Cloud computing technology adoption: an evaluation of key factors in local governments. Information Technology & People, 34(2), 666-703.
Kwilinski, A., Litvin, V., Kamchatova, E., Polusmiak, J., & Mironova, D. (2021). Information support of the entrepreneurship model complex with the application of cloud technologies. International Journal of Entrepreneurship, 25(1), 1-8.
Kumari, P., & Kaur, P. (2021). A survey of fault tolerance in cloud computing. Journal of King Saud University-Computer and Information Sciences, 33(10), 1159-1176.
Vakaliuk, T. A., Korotun, O. V., & Semerikov, S. O. (2021, March). The selection of cloud services for ER-diagrams construction in IT specialists databases teaching. In CTE Workshop Proceedings (Vol. 8, pp. 384-397).
Sheikh, A., Anderson, M., Albala, S., Casadei, B., Franklin, B. D., Richards, M., ... & Mossialos, E. (2021). Health information technology and digital innovation for national learning health and care systems. The Lancet Digital Health, 3(6), e383-e396.
Al-Malah, D. K. A. R., Aljazaery, I. A., Alrikabi, H. T. S., & Mutar, H. A. (2021, February). Cloud computing and its impact on online education. In IOP Conference Series: Materials Science and Engineering (Vol. 1094, No. 1, p. 012024). IOP Publishing.
Heidari, A., Navimipour, N. J., & Unal, M. (2022). Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review. Sustainable Cities and Society, 104089.
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565.
Vakaliuk, T. A., Spirin, O. M., Lobanchykova, N. M., Martseva, L. A., Novitska, I. V., & Kontsedailo, V. V. (2021, March). Features of distance learning of cloud technologies for the organization educational process in quarantine. In Journal of physics: Conference series (Vol. 1840, No. 1, p. 012051). IOP Publishing.
Li, F., Lu, H., Hou, M., Cui, K., & Darbandi, M. (2021). Customer satisfaction with bank services: The role of cloud services, security, e-learning and service quality. Technology in Society, 64, 101487.
Nuryana, Z., Pangarso, A., & Zain, F. M. (2021). Factor of Zoom Cloud Meetings: Technology Adoption in the Pandemic of COVID-19. International Journal of Evaluation and Research in Education, 10(3), 816-825.
Purnama, S., Aini, Q., Rahardja, U., Santoso, N. P. L., & Millah, S. (2021). Design of educational learning management cloud process with blockchain 4.0 based e-portfolio. Journal of Education Technology, 5(4), 628-635.
Zheng, W., Muthu, B., & Kadry, S. N. (2021). Research on the design of analytical communication and information model for teaching resources with cloud‐sharing platform. Computer Applications in Engineering Education, 29(2), 359-369.
Kwilinski, A., Litvin, V., Kamchatova, E., Polusmiak, J., & Mironova, D. (2021). Information support of the entrepreneurship model complex with the application of cloud technologies. International Journal of Entrepreneurship, 25(1), 1-8.
He, W., Zhang, Z. J., & Li, W. (2021). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International journal of information management, 57, 102287.
He, W., Zhang, Z. J., & Li, W. (2021). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International journal of information management, 57, 102287.
Alzamily, J. Y. I., Ariffin, S. B., & Abu-Naser, S. S. (2022). Classification of Encrypted Images Using Deep Learning–Resnet50. Journal of Theoretical and Applied Information Technology, 100(21), 6610-6620.
Tabassum, N., Ditta, A., Alyas, T., Abbas, S., Alquhayz, H., Mian, N. A., & Khan, M. A. (2021). Prediction of cloud ranking in a hyperconverged cloud ecosystem using machine learning. Computers, Materials & Continua, 67(3), 3129-3141.
Alam, A. (2022). Cloud-based e-learning: scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. In Computer Communication, Networking and IoT: Proceedings of 5th ICICC 2021, Volume 2 (pp. 1-9). Singapore: Springer Nature Singapore.
Mishra, S., & Tyagi, A. K. (2022). The role of machine learning techniques in internet of things-based cloud applications. Artificial intelligence-based internet of things systems, 105-135.
Alashhab, Z. R., Anbar, M., Singh, M. M., Leau, Y. B., Al-Sai, Z. A., & Alhayja’a, S. A. (2021). Impact of coronavirus pandemic crisis on technologies and cloud computing applications. Journal of Electronic Science and Technology, 19(1), 100059.
Cheng, Y. M. (2021). Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202.
Rehman, A., Abbas, S., Khan, M. A., Ghazal, T. M., Adnan, K. M., & Mosavi, A. (2022). A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Computers in Biology and Medicine, 150, 106019.
Sharipov, F. F., Krotenko, T. Y., & Dyakonova, M. A. (2021). Digital potential of economic education: information technologies in a management university. Current Achievements, Challenges and Digital Chances of Knowledge Based Economy, 561-572.