Cyberphysical Smart Cities Infrastructures. Группа авторов

system with real‐time monitoring. IEEE Internet of Things Journal. 8 3384–3393.

      16 16 Boulos, M.N.K., Peng, G., and VoPham, T. (2019). An overview of GeoAI applications in health and healthcare. International Journal of Health Geographics, 18 1–9.

      17 17 Al‐Turjman, F., Nawaz, M.H., and Ulusar, U.D. (2020). Intelligence in the internet of medical things era: a systematic review of current and future trends. Computer Communications 150: 644–660.

      18 18 Ullah, Z., Al‐Turjman, F., Mostarda, L., and Gagliardi, R. Applications of artificial intelligence and machine learning in smart cities. (2020). Computer Communications. 154 313–323.

      19 19 Manikandan, R., Patan, R., Gandomi, A.H. et al. (2020). Hash polynomial two factor decision tree using IoT for smart health care scheduling. Expert Systems with Applications 141: 112924.

      20 20 Khatri, C., Hedayatnia, B., Venkatesh, A. et al. (2018). Advancing the state of the art in open domain dialog systems through the Alexa prize. arXiv preprint arXiv:1812.10757.

      21 21 Rak, M., Salzillo, G., and Romeo, C. (2020). Systematic IoT penetration testing: Alexa case study. ITASEC, pp. 190–200.

      22 22 Elhoseny, H., Elhoseny, M., Riad, A.M., and Hassanien, A.E. (2018). A framework for big data analysis in smart cities. International Conference on Advanced Machine Learning Technologies and Applications, Springer, pp. 405–414.

      23 23 Ju, J., Liu, L., and Feng, Y. (2018). Citizen‐centered big data analysis‐driven governance intelligence framework for smart cities. Telecommunications Policy 42 (10): 881–896.

      24 24 Bhattacharya, S., Somayaji, S.R.K., Gadekallu, T.R. et al. (2020). A review on deep learning for future smart cities. Internet Technology Letters e187.

      25 25 Kumar, S., Datta, D., Singh, S.K., and Sangaiah, A.K. (2018). An intelligent decision computing paradigm for crowd monitoring in the smart city. Journal of Parallel and Distributed Computing 118: 344–358.

      26 26 Usman, M., Jan, M.A., He, X., and Chen, J. (2019). A survey on big multimedia data processing and management in smart cities. ACM Computing Surveys (CSUR) 52 (3): 1–29.

      27 27 Mohammadi, F.G. and Abadeh, M.S. (2014). Image steganalysis using a bee colony based feature selection algorithm. Engineering Applications of Artificial Intelligence 31: 35–43.

      28 28 Shenavarmasouleh, F. and Arabnia, H. (2019). Causes of misleading statistics and research results irreproducibility: a concise review. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 465–470.

      29 29 Hashem, I.A.T., Chang, V., Anuar, N.B. et al. (2016). The role of big data in smart city. International Journal of Information Management 36 (5): 748–758.

      30 30 Mohammadi, F.G., Shenavarmasouleh, F., Arabnia, H.R., and Amini, M.H. (2020). Impact of weather conditions on the Covid‐19 pandemic in the United States: a big data approach. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE.

      31 31 Chakole, J.B., Kolhe, M.S., Mahapurush, G.D. et al. (2021). A Q‐learning agent for automated trading in equity stock markets. Expert Systems with Applications 163: 113761.

      32 32 Liao, Z., Peng, J., Chen, Y. et al. (2020). A fast Q‐learning based data storage optimization for low latency in data center networks. IEEE Access 8: 90630–90639.

      33 33 Fan, J., Wang, Z., Xie, Y., and Yang, Z. (2020). A theoretical analysis of deep Q‐learning. Learning for Dynamics and Control, PMLR, pp. 486–489.

      34 34 Boussakssou, M., Hssina, B., and Erittali, M. (2020). Towards an adaptive E‐learning system based on Q‐learning algorithm. Procedia Computer Science 170: 1198–1203.

      35 35 Joo, H., Ahmed, S.H., and Lim, Y. (2020). Traffic signal control for smart cities using reinforcement learning. Computer Communications 154: 324–330.

      36 36 Wang, A., Zhang, A., Chan, E.H.W. et al. (2021). A review of human mobility research based on big data and its implication for smart city development. ISPRS International Journal of Geo‐Information 10 (1): 13.

      37 37 Zhu, L., Yu, F.R., Wang, Y. et al. (2018). Big data analytics in intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems 20 (1): 383–398.

      38 38 Xiong, G., Zhu, F., Fan, H. et al. (2014). Novel its based on space‐air‐ground collected big‐data. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1509–1514.

      39 39 Mazimpaka, J.D. and Timpf, S. (2017). How they move reveals what is happening: understanding the dynamics of big events from human mobility pattern. ISPRS International Journal of Geo‐Information 6 (1): 15.

      40 40 Mohammadi, F.G. and Amini, M.H. (2019). Promises of meta‐learning for device‐free human sensing: learn to sense. Proceedings of the 1st ACM International Workshop on Device‐Free Human Sensing, pp. 44–47.

      41 41 Wei, K., Deng, C., and Yang, X. (2020). Lifelong zero‐shot learning. Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI‐20, pp. 551–557.

      42 42 Mohammadi, F.G., Arabnia, H.R., and Amini, M.H. (2019). On parameter tuning in meta‐learning for computer vision. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE, pp. 300–305.

      43 43 Wijekoon, A., Wiratunga, N., and Sani, S. (2018). Zero‐shot learning with matching networks for open‐ended human activity recognition. CEUR Workshop Proceedings.

      44 44 Yu, L., Feng, Q., Qian, Y. et al. (2020). Zero‐virus: zero‐shot vehicle route understanding system for intelligent transportation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 594–595.

      45 45 Pandey, A., Puri, M., and Varde, A. (2018). Object detection with neural models, deep learning and common sense to aid smart mobility. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, pp. 859–863.

      46 46 Asali, E., Shenavarmasouleh, F., Mohammadi, F.G. et al. (2020). DeepMSRF: A novel deep multimodal speaker recognition framework with feature selection. ArXiv, abs/2007.06809.

      47 47 Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: unified, real‐time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788.

      48 48 Safari, Z., Mursi, K.T., and Zhuang, Y. (2020). Fast automatic determination of cluster numbers for high dimensional big data. Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis, pp. 50–57.

      49 49 Golob, T.F. and Recker, W.W. (2003). Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions. Journal of Transportation Engineering 129 (4): 342–353.

      50 50 Xu, J., Deng, D., Demiryurek, U. et al. (2015). Mining the situation: spatiotemporal traffic prediction with big data. IEEE Journal of Selected Topics in Signal Processing 9 (4): 702–715.

      51 51 Mohamed, N. and Al‐Jaroodi, J. (2014). Real‐time big data analytics: applications and challenges. 2014 International Conference on High Performance Computing & Simulation (HPCS), IEEE, pp. 305–310.

      52 52 Park, H.‐M., Park, N., Myaeng, S.‐H., and Kang, U. (2020). PACC: Large scale connected component computation on Hadoop and Spark. PLoS ONE 15 (3): e0229936.

3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review

       Farzan Shenavarmasouleh1, Ghareh Mohammadi1, M. Hadi Amini2, and Hamid Reza Arabnia1

       1Department of Computer Science, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA

       2Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA

3.1