NozariAYSiamianH.The effect of applying podcast multimedia teaching system on motivational achievement and learning among the boy students. Acta Inform Med2015;
23: 29–32.
2.
ArmenterosMLiawSSSánchez-FrancoMJ, et al.
Analysis of FIFA referees and assistant referees’ motivational factors towards the multimedia teaching materials. Educ Inf Technol2017;
22: 841–872.
3.
ChenYC.An investigation on factors in the integration of reciprocal teaching into multimedia teaching. Eurasia J Math Sci Technol Educ2017;
13: 133–142.
4.
WinarnoSMuthuKSLingLS.Multimedia teaching and learning for computer networks subject in the direct problem-based learning approach: a pilot study. Int Semin Appl Technol Inform Commun2018;
22: 32–33.
5.
WinarnoSMuthuKSLingLS.Multimedia teaching and learning for computer networks subject in the direct problem-based learning approach: a pilot study. Int Semin Appl Technol Inform Commun2018;
34: 23.
6.
FabisiakL.Web service usability analysis based on user preferences. J Org End User Comput2018;
30: 1–13.
7.
XinZKaiW.The construction of evaluation model of Chinese traditional culture multimedia teaching resources allocation in big data environment. Int Conf Intell Transp2018;
14: 133–134.
8.
FengSShengS.Construction of multifunctional video conversion-based multimedia teaching system for college basketball. Int J Emerg Technol Learn2018;
13: 176.
9.
LvZXiuW.Interaction of Edge-Cloud computing based on SDN and NFV for next generation IoT. IEEE Internet Things J2020;
7: 5706–5712.
10.
ZhaoKZhengY.Chinese business English students’ epistemological beliefs, self-regulated strategies, and collaboration in project-based learning. Asia-Pacific Educ Res2014;
23: 273–286.
11.
CarriopastorMLMunizcalderonR.Identification and causes of lexical variation in Chinese business English – lexical variation can be found when English is used as an international working language by businesspeople from China and Hong Kong. English Today2015;
31: 10–15.
12.
ZhihanLH.Song. Trust mechanism of multimedia network. ACM Trans Multimedia Comput Commun Appl2020. DOI: 10.1145/TOMM.
13.
LiuJHZhangJJingL.Formative assessments in junior high school English class based on multimedia-assisted language teaching. AMM2014;
644–650: 5773–5776.
14.
LouM.Design of English multimedia teaching system based on diversification theory. Int J Emerg Technol Learn2017;
12: 119.
15.
ChenY.The application of multimedia computer assisted language learning in English teaching. AMR2014;
926–930: 4634–4637.
16.
TupeN.Multimedia scenario based learning programme for enhancing the English language efficiency among primary school students. Int J Instruct2015;
8: 125–138.
17.
LiuHC.Investigating the impact of cognitive style on multimedia learners’ understanding and visual search patterns: an eye-tracking approach. J Educ Comput Res2018;
55: 1053–1068.
18.
ZhihanLQiaoLSinghAK. Advanced machine learning on cognitive computing for human behavior analysis. IEEE Trans Comput Social Syst2020. DOI: 10.1109/TCSS.2020.3011158
19.
MoJY.The application of multimedia network technology in production practice for foreign language. AMR2014;
926–930: 4705–4708.
20.
LiG. Mismatches in the integration of multimedia technology into college English teaching. In: Eighth international conference on internet computing for science & engineering, Anand Padmanabhan, IEEE, US; 6–8 November 2015, Harbin, China, vol. 26, p.11.
21.
ZhangYHeQXiangY, et al.
Low-cost and confidentiality-preserving data acquisition for internet of multimedia things. IEEE Internet Things J2018;
5: 3442–3451.
22.
JinHY.Research on English learning modes of graduate student under the multimedia and internet environment. AMR2014;
926-930: 4562–4565.
23.
AmpaAT.The implementation of interactive multimedia learning materials in teaching listening skills. ELT2015;
8: 56.
24.
FanYP.Applied-Information technology in viewing, listening and speaking teaching in environment of multimedia network. AMM2014;
685: 512–516.
25.
HwangWYHuangYMShadievR, et al.
Effects of using mobile devices on English listening diversity and speaking for EFL elementary students. AJET2014;
30: 503–516.
26.
MuslemAAbbasM.The effectiveness of immersive multimedia learning with peer support on English speaking and reading aloud. Int J Instruct2017;
10: 203–218.
27.
LvZ. Virtual reality in the context of internet of things. Neural Comput Appl2019.
32: 9593–9602.
28.
YingTMohamedE. Computer network security evaluation simulation model based on neural network. J Intell Fuzzy Syst2019;
37: 3197–3204.
29.
LvZQiaoLSongH.Analysis of the security of internet of multimedia things. ACM Trans Multimedia Comput Commun Appl2021;
16: 1–16.
30.
YuanXLiDMohapatraD, et al.
Deepankar Mohapatra, Mohamed Elhoseny, automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Comput Electr Eng2018;
70: 813–825.
31.
ChenNRongBZhangX, et al.
Scalable and flexible massive MIMO precoding for 5G H-CRAN. IEEE Wireless Commun2017;
24: 46–52.
32.
Jing-HuiLSheng-JiangG. Virtual scene dynamic interactive image segmentation technology based on OpenGL module. Int J Electr Eng Educ2019. DOI: 10.1177/0020720919891080
33.
LydiaELRajJSSelvamRP, et al. Application of discrete transforms with selective coefficients for blind image watermarking. Trans Emerg Tel Tech2021. Available at: https://doi.org/10.1002/ett.3771
34.
TsaiS-BChienM-FXueY, et al.
Using the fuzzy DEMATEL to determine environmental performance: a case of printed circuit board industry in Taiwan. Plos One2015;
10: e0129153.
35.
EassaAMElhosenyMEl-BakryHM, et al.
NoSQL injection attack detection in web applications using RESTful service. Prog Comput Soft2018;
44: 435–444.
36.
ZhihanLSongH. Trust mechanism of multimedia network. ACM Trans Multimedia Comput Commun Appl2020. Available at: https://doi.org/10.1145/3391296
37.
BuckleyPNoonanSGearyC, et al.
An empirical study of gamification frameworks. J Org End User Comput2019;
31: 22–38.
38.
WangRWeiYSongH, et al.
From offline towards real-time verification for robot systems. IEEE Trans Ind Inf2018;
14: 1712–1721.
39.
YangJWangCJiangB, et al. Visual perception enabled industry intelligence: state of the art, challenges and prospects. IEEE Trans Indus Inform 2020; 17: 2204–2219.
