Smart education is one of the main applications of Smart cities which requires artificial intelligence-based models during the education process. Optimizing the educational contents to match the student’s capabilities and backgrounds is one of the core tasks in the smart educations. To improve the optimization of an education system, a data mining algorithm is used to classify and summarize the education data and improve the accuracy of teaching content. An optimization and updating scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the developing state of wisdom education. In the test of intelligent education model, the efficiency of AI education and the data accuracy of the data mining algorithm are tested. The test results show that the integration of AI can improve the efficiency of learning and teaching, and make the teaching content more accurate, which is worth to further promoting.
J.Zeng and M.Yang, Internet plus and networks convergence, China Communications12(4) (2015), 42–49.
2.
W.Xiong, F.Xiang and Z.Zhao, Influence of the internet plus to international business development, American Journal of Industrial & Business Management6(4) (2016), 541–549.
3.
B.Zhang, P.Peng, Research on the development of educational resources for the internet plus universities in the national health field, Eurasia Journal of Mathematics Science & Technology Education13(8) (2017), 5085–5093.
4.
J.Wang, F.Zhang, F.Liu and J.Ma, Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimization: A case study of wind speed time series, It Renewable Power Generation10(3) (2016), 287–298.
5.
Z.Ma, Z.Shi, Y.Zhou, et al.A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai-Tibet Plateau with the effects of systematic anomalies removed. Remote Sensing of Environment, 2017, 200.
6.
H.Hong, H.R.Pourghasemi, Z.S.Pourtaghi, Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models, Geomor-phology259 (2016), 105–118.
7.
R.R.Rathod, R.D.Garg, Regional electricity consumption analysis for consumers using data mining techniques and consumer meter reading data, International Journal ofElec-trical Power & Energy Systems78 (2016), 368–374.
8.
R.V.Yampolskiy, Artificial Intelligence Safety Engineering: Why Machine Ethics Is a Wrong Approach. Philosophy and Theory of Artificial Intelligence. Springer Berlin Heidelberg, 2013, pp. 389–396.
9.
D.C.Parkes and M.P.Wellman, Economic reasoning and artificial intelligence, Science349(6245) (2015), 267.
10.
J.McCarthy, Generality in artificial intelligence, Resonance19(3) (2014), 283–296.
11.
A.Bundy, Preparing for the future of Artificial Intelligence, Ai & Society32(2) (2017), 1–3.
12.
K.P.Singh, S.Gupta and P.Rai, Predicting acute aquatic tox-icity of structurally diverse chemicals in fish using artificial intelligence approaches, Ecotoxicology & Environmental Safety95(1) (2013), 221–233.
13.
D.Hassabis, D.Kumaran and C.Summerfield, Neuroscience-inspired artificial intelligence, Neuron95(2) (2017), 245–258.
14.
HamidReza Boveiri, RaoufKhayami, MohamedElhoseny and M.Gunasekaran, An efficient swarm-intelligence approach for task scheduling in cloud-based internet of things applications, Journal of Ambient Intelligence and Humanized Computing,First Online: 04 October 2018 (doi: 10.1007/s12652-018-1071-1).
15.
MohamedElhoseny, NouraMetawa and AboulElla Hassanien, An automated information system to ensure quality in higher education institutions, Proceedings of 12th International Computer Engineering Conference (ICENCO), IEEE, 196–201, 2016 (doi: 10.1109/ICENC0.2016.7856468).
16.
E.Hovy, R.Navigli and S.P.Ponzetto, Collaboratively built semi-structured content and artificial intelligence: The story so far, Artificial Intelligence194 (2013), 2–27.
17.
M.Imran, C.Castillo, L.Ji, et al.AIDR: artificial intelligence for disaster response, International Conference on World Wide Web. ACM, 2014, pp. 159–162.
18.
M.Kumar, Y.H.Mao, Y.H.Wang, T.R.Qiu, C.Yang and W.P.Zhang, Fuzzy theoretic approach to signals and systems: Static systems, Information Sciences418 (2017), 668–702.
19.
W.Zhang, K.Thurow and R.Stoll, A context-aware mhealth system for online physiological monitoring in remote healthcare, International Journal of Computers Communications & Control11(1) (2016), 142–156.
20.
E.S.Rigas, S.D.Ramchurn and N.Bassiliades, Managing electric vehicles in the smart grid using artificial intelligence: A survey, IEEE Transactions on Intelligent Transportation Systems16(4) (2015), 1619–1635.
21.
S.Ramkumar, K.Sathesh Kumar, T.Dhiliphan Rajkumar, M.Ilayaraja and K.Shankar, A review-classification of electrooculogram based human computer interfaces, Biomedical Research29(6) (2018), 1078–1084.
22.
HaidiRao, XianzhangShi, AhoussouKouassi Rodrigue, JuanjuanFeng, YingchunXia, MohamedElhoseny, XiaohuiYuan and LichuanGu, Feature selection based on artificial bee colony and gradient boosting decision tree, Applied Soft Computing74 (2018), 634–642.
23.
NouraMetawaa, M.Kabir Hassana and MohamedElhoseny, Genetic algorithm based model for optimizing bank lending decisions, Expert Systems with Applications80 (2017), 75–82.
24.
BaofuFang, XiaopingGuo, ZaijunWang, YongLi, MohamedElhoseny and XiaohuiYuan, Collaborative task assignment of interconnected, affective robots towards autonomous healthcare assistant, Future Generation Computer Systems92, pp. 241–251.
