Investigation on the analysis of integration of IoT and AI technologies with information security for advanced education 4.0

Upreti, Kamal; Dadhich, Priyanka; Gautam, Anjali; Singh, Jagendra; Kushwah, Virendra Singh; Parashar, Jyoti; Gangwar, Divya; Ghosh, Soumi

doi:10.47974/JDMSC-1998

Open Access Research Article

Investigation on the analysis of integration of IoT and AI technologies with information security for advanced education 4.0

^*Kamal UpretiCorresponding authorkamalupreti1989@gmail.comDepartment of Computer ScienceDelhi NCR CampusCHRIST UniversityUttar Pradesh, IndiaView full profile → , Priyanka Dadhichdadhichdrpriyanka@gmail.comSchool of Computer ApplicationManav Rachna International Institute of Research and Studies (MRIIRS)Faridabad, Haryana, IndiaView full profile → , Anjali Gautamanjali.gautam09@gmail.comSchool of Computer ApplicationManav Rachna International Institute of Research and Studies (MRIIRS)Faridabad, Haryana, IndiaView full profile → , Jagendra Singhjagendrasingh@gmail.comDepartment of Computer Science and EngineeringAlliance School of Advanced ComputingAlliance UniversityBangalore, Karnataka, IndiaView full profile → , Virendra Singh Kushwahkushwah.virendra248@gmail.comSchool of Computer Science & EngineeringBhopal-Indore HighwayVIT Bhopal UniversityBhopal, Madhya Pradesh, IndiaView full profile → , Jyoti Parasharjyoti.parashar123@gmail.comDepartment of Computer Science and EngineeringDr. Akhilesh Das Gupta Institute of Professional StudiesShashti Park, New Delhi, IndiaView full profile → , Divya Gangwardivyagangwar2013@gmail.comDepartment of ManagementBabu Banarasi Das Institute of Technology and ManagementDr. A.P.J. Abdul Kalam Technical UniversityLucknow, Uttar Pradesh, IndiaView full profile → , Soumi Ghoshghoshdrsoumi@gmail.comDepartment of Information TechnologyMaharaja Agrasen Institute of TechnologyRohini, Delhi, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1445–1454Vol. 27Issue 4June 2024DOI: 10.47974/JDMSC-1998 Crossmark XML

Published Online:: 26 Jun 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1998
Pages:: 1445–1454

Abstract

This research examines the integration of emerging technologies in the form of the Internet of Things and Artificial Intelligence in driving forward to the educational application of Education 4.0. The systematic meta-analysis study provides evidence in the transformative capability of these technologies regarding attendance, performance, and learning pathway. The system’s implementation was in the form of IoT sensors to capture and record student attendance, while the use of Artificial Intelligence based on machine learning models such as Support Vector Machine, Artificial Neural Network, k-Nearest Neighbors, and Decision Tree generated a personalized recommendation for the academic improvement or sports activity to be participated as an extracurricular activity. The performance evaluation of these models was illustrated for accuracy to correctly predict student responses related to the provided recommendations. The findings of implementation suggest the system’s significant impacts given the augmented performance achievement with respect to academics and sports is the result of the implementation. It was measured comparing students’ performance before and after system implementation to capture the interpretation of student improvement regarding the use of the implemented system. The findings indicated that the system’s implementation contributed to the increase in academic improvement from 65% to 75% and sports performance from 55% to 70% depending on student response to the provided academical or extracurricular recommendations. Such findings confirm an overall improvement in performance based on the use of the presented system.

Keywords

Mathematical modeling Ordinary differential equations A duopoly economy Market share

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] S. Gargrish, A. Mantri, and D. P. Kaur, “Augmented reality-based learning environment to enhance teaching-learning experience in geometry education,” Procedia Computer Science, vol. 172, no. 2019, pp. 1039–1046, (2020).
[2] N. Sultana and M. Tamanna, “Evaluating the Potential and Challenges of IoT in Education and Other Sectors during the COVID-19 Pandemic: The Case of Bangladesh,” Technology in Society, vol. 68, no. July 2021, p. 101857, (2022).
[3] K. Gunasekera, A. N. Borrero, F. Vasuian, and K. P. Bryceson, “Experiences in building an IoT infrastructure for agriculture education,” Procedia Computer Science, vol. 135, pp. 155–162, (2018).
[4] T. Lei, Z. Cai, and L. Hua, “5G-oriented IoT coverage enhancement and physical education resource management,” Microprocessors and Microsystems, vol. 80, no. September 2020, p. 103346, (2021).
[5] W. Shi, A. Haga, and Y. Okada, “Web-Based 3D and 360∘ VR Materials for IoT Security Education and Test Supporting Learning Analytics,” Internet of Things (Netherlands), vol. 15, p. 100424, (2021).
[6] W. Fan and C. A. Wolters, “School motivation and high school dropout: The mediating role of educational expectation,” British Journal of Educational Psychology, vol. 84, no. 1, pp. 22–39, (2014).
[7] T. Motoyoshi, N. Tetsumura, H. Masuta, K. Koyanagi, T. Oshima, and H. Kawakami, “Tangible gimmick for programming education using RFID systems,” IFAC-PapersOnLine, vol. 49, no. 19, pp. 514–518, (2016).
[8] X. Hu, Y. M. Goh, and A. Lin, “Educational impact of an Augmented Reality (AR) application for teaching structural systems to non-engineering students,” Advanced Engineering Informatics, vol. 50, no. September, p. 101436, (2021).
[9] Y. Shi, M. Wang, Z. Qiao, and L. Mao, “Effect of semantic web technologies on distance education,” Procedia Engineering, vol. 15, pp. 4295–4299, (2011).
[10] Upreti, Kamal, Peng, Sheng-Lung, Kshirsagar, Pravin Ramdas, Chakrabarti, Prasun, Al-Alshaikh, Halah A., Sharma, A. K. & Poonia, Ramesh Chandra. A multi-model unified disease diagnosis framework for cyber healthcare using IoMT- cloud computing networks, Journal of Discrete Mathematical Sciences and Cryptography, 26:6, 1819–1834 (2023).
[11] Arora, R., Agrawal, A., Arora, R., Poonia, R. C., & Madaan, V. Prediction and forecasting of COVID-19 outbreak using regression and ARIMA models. Journal of Interdisciplinary Mathematics, 24(1), 227–243 (2021).
[12] F. Yu and E. R. Da Silva, “Design for robot assembly: Challenges of online education,” Procedia CIRP, vol. 100, pp. 482–487, (2021).

Views: 308Downloads: 71Citations: 2