Mobile Platform with Dynamic Optimization of the Pattern in Education in Colleges Through the Perspective of Network Informatization

Article Sidebar

PDF
Published: Jul 30, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6.7739
Keywords:
English Language, Mobile platform, Dynamic Programming, Fuzzy Set, Classification, Network Information

Main Article Content

Xue Wang
Zhengzhou University of Economics and Business, Zhengzhou, Henan, China
Qing Yu
University of Gdańsk, Gdańsk, Poland

Abstract

The combination of mobile learning platforms and network informatization offers numerous benefits to learners, educators, and institutions. Learners can take control of their learning journey, accessing educational materials at their convenience and engaging in collaborative learning activities with peers from diverse backgrounds. This paper aims to explore the integration of mobile learning platforms and network informatization, examining their impact on educational practices, learner engagement, and the overall learning experience. The network informatization is assessed and monitored with Dynamic Programming Optimization (DPO) to compute the feature in reverse osmosis in English education. The attributes and features in the English language are computed and estimated for the periodic information update within the system. The DPO process is implemented along with the mandhani fuzzy set for the estimation of features in English education in colleges and universities. The information processed is updated in the mobile learning platform for the computation of the features in the English language and classification is performed with the deep learning model. Simulation analysis stated that constructed model is effective for the estimation and computation of the features and patterns in English language teaching in colleges and universities.

Article Details

How to Cite
Wang, X. ., & Yu, Q. . (2023). Mobile Platform with Dynamic Optimization of the Pattern in Education in Colleges Through the Perspective of Network Informatization. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 454–464. https://doi.org/10.17762/ijritcc.v11i6.7739
Issue
Vol. 11 No. 6 (2023): June (2023) Issue
Section
Articles

References

Widiastika Asti, M., Hendracipta, N., & A Syachruroji, A. S. (2021). Pengembangan media pembelajaran mobile learning berbasis android pada konsep sistem peredaran darah di sekolah dasar. Jurnal Basicedu, 5(1), 47-64.

Poultsakis, S., Papadakis, S., Kalogiannakis, M., & Psycharis, S. (2021). The management of digital learning objects of natural sciences and digital experiment simulation tools by teachers. Advances in Mobile Learning Educational Research, 1(2), 58-71.

Kaisara, G., & Bwalya, K. J. (2021). Investigating the E-learning challenges faced by students during COVID-19 in Namibia. International Journal of Higher Education, 10(1), 308-318.

Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T., ... & Redi, A. A. N. P. (2021). Determining factors Affecting acceptance of e-learning platforms during the COVID-19 pandemic: Integrating Extended technology Acceptance model and DeLone & Mclean is success model. Sustainability, 13(15), 8365.

Ahmed, I., Ahmad, M., Rodrigues, J. J., Jeon, G., & Din, S. (2021). A deep learning-based social distance monitoring framework for COVID-19. Sustainable cities and society, 65, 102571.

Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., ... & Schultze, J. L. (2021). Swarm learning for decentralized and confidential clinical machine learning. Nature, 594(7862), 265-270.

Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072.

Murshed, M. S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., & Hussain, F. (2021). Machine learning at the network edge: A survey. ACM Computing Surveys (CSUR), 54(8), 1-37.

Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and information technologies, 26(2), 1755-1785.

Dhar, S., Guo, J., Liu, J., Tripathi, S., Kurup, U., & Shah, M. (2021). A survey of on-device machine learning: An algorithms and learning theory perspective. ACM Transactions on Internet of Things, 2(3), 1-49.

Abowarda, A. S., Bai, L., Zhang, C., Long, D., Li, X., Huang, Q., & Sun, Z. (2021). Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale. Remote Sensing of Environment, 255, 112301.

Alam, A. (2022, April). A digital game based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 69-74). IEEE.

Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21(8), 2852.

Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., ... & Cui, T. J. (2022). A programmable diffractive deep neural network based on a digital-coding metasurface array. Nature Electronics, 5(2), 113-122.

Hamdi, M., Bourouis, S., Rastislav, K., & Mohmed, F. (2022). Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network. Frontiers in Public Health, 10, 35.

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021). Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis. IEEE Access, 9, 138509-138542.

Prof. Romi Morzelona. (2017). Evaluation and Examination of Aperture Oriented Antennas. International Journal of New Practices in Management and Engineering, 6(01), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/49

Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., ... & Lemey, C. (2021). Machine learning and natural language processing in mental health: systematic review. Journal of Medical Internet Research, 23(5), e15708.

Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D. (2021). Machine learning and deep learning in smart manufacturing: The smart grid paradigm. Computer Science Review, 40, 100341.

Lv, Z., Li, Y., Feng, H., & Lv, H. (2021). Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE transactions on intelligent transportation systems, 23(9), 16666-16675.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695.

Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach. JMIR Medical Education, 7(1), e24032.

Alhumaid, K., Habes, M., & Salloum, S. A. (2021). Examining the factors influencing the mobile learning usage during COVID-19 Pandemic: An Integrated SEM-ANN Method. Ieee Access, 9, 102567-102578.

Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), 4111.

Mutambara, D., & Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas. Computers & Education, 160, 104010.

Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precision Agriculture, 22, 2053-2091.

Chen, C. H., & Tsai, C. C. (2021). In-service teachers' conceptions of mobile technology-integrated instruction: Tendency towards student-centered learning. Computers & Education, 170, 104224.

Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J. C., & Cecilia, J. M. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77, 818-840.

Li, C., Guo, C., Han, L., Jiang, J., Cheng, M. M., Gu, J., & Loy, C. C. (2021). Low-light image and video enhancement using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(12), 9396-9416.

Shaik, D. ., & Gollapudi, S. K. S. . (2023). Analogy of Distinct Constructions of FinFET GDI Full Adder. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 76–80. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2510

Lv, H., Dao, F. Y., Guan, Z. X., Yang, H., Li, Y. W., & Lin, H. (2021). Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method. Briefings in bioinformatics, 22(4), bbaa255.

Geetha, R., & Thilagam, T. (2021). A review on the effectiveness of machine learning and deep learning algorithms for cyber security. Archives of Computational Methods in Engineering, 28, 2861-2879.

Xiao, X., Liu, B., Warnell, G., & Stone, P. (2022). Motion planning and control for mobile robot navigation using machine learning: a survey. Autonomous Robots, 46(5), 569-597.

Elsisi, M., Tran, M. Q., Mahmoud, K., Lehtonen, M., & Darwish, M. M. (2021). Deep learning-based industry 4.0 and internet of things towards effective energy management for smart buildings. Sensors, 21(4), 1038.