AI Bibliography

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Work, R. O., Brimley, S., & Security, C. F. A. N. A. (2014). 20yy: Preparing for war in the robotic age. Center for a New American Security.  
10/06/2020, 21:13
Wrbouschek, M., & Slunecko, T. (2021). Liminal moods and sense-making under conditions of uncertainty. International Review of Theoretical Psychologies, 1(1), 142–161.  
18/03/2024, 21:42
Wright, N. (2018). How artificial intelligence will reshape the global order. Foreign Affairs, 10.  
30/06/2020, 09:26
Wu, Y., Hu, F., Min, G., & Zomaya, A. Y. (2017). Big data and computational intelligence in networking. CRC Press.  
12/06/2020, 15:36
Wu, R., Zhao, Y., Clarke, A., & Kendall, A. 2017, A framework using machine vision and deep reinforcement learning for self-learning moving objects in a virtual environment. Paper presented at Naval Post Graduate School.  
19/10/2020, 13:51
Wurster, M. A. (2021). Superiority in the electromagnetic spectrum--panel introduction. Joint Air and Space Power Conference - 2021, 143.  
16/08/2021, 09:44
Wylie Jr, J. (2014). Military strategy: A general theory of power control. Naval Institute Press.  
20/10/2023, 17:20
Xiang, N. (2019). Red ai: Victories and warnings from china's rise in artificial intelligence. Independently Published.  
10/06/2020, 19:37
Xie, X., Lu, Q., Parlikad, A. K., & Schooling, J. M. (2020). Digital twin enabled asset anomaly detection for building facility management. IFAC-PapersOnLine, 53(3), 380–385.  
08/02/2023, 09:36
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., & Zhu, H., et al.. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365–35381.  
04/02/2021, 11:02
Xing, C., Chen, N., Zhang, X., & Gong, J. (2017). A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sensing, 9(5), 484.  
12/11/2020, 13:29
Xu, H. A., Modirshanechi, A., Lehmann, M. P., Gerstner, W., & Herzog, M. H. (2021). Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making. PLOS Computational Biology, 17(6), e1009070.  
22/06/2021, 09:14
Xu, S., Rogers, T., Fairweather, E., Glenn, A., Curran, J., & Curcin, V. (2018). Application of data provenance in healthcare analytics software: Information visualisation of user activities. AMIA Summits on Translational Science Proceedings, 2018, 263.  
06/12/2022, 09:35
Yada, Y., Yasuda, S., & Takahashi, H. (2021). Physical reservoir computing with force learning in a living neuronal culture. Applied Physics Letters, 119(17), 173701.  
01/11/2021, 17:15
Yadav, V., & Bethard, S. (2019). A survey on recent advances in named entity recognition from deep learning models. arXiv preprint arXiv:1910.11470.  
28/10/2020, 16:37
Yadav, A., & Satyannarayana, P. (2020). Multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in mahanadi river basin, india. International Journal of River Basin Management, 18(2), 207–215.  
06/08/2020, 10:05
Yan, G. (2020). The impact of artificial intelligence on hybrid warfare. Small Wars & Insurgencies, 31(4), 898–917.  
21/09/2021, 14:18
Yang, G. Z. (2018). Implantable sensors and systems: From theory to practice. Springer International Publishing.  
24/06/2020, 16:52
Yang, L. T., Jin, H., & Ungerer, T. (2006). Autonomic and trusted computing: Third international conference, atc 2006, wuhan, china, september 3-6, 2006. Springer Berlin Heidelberg.  
24/06/2020, 16:31
Yang, Q., & Parasuraman, R. 2020, Needs-driven heterogeneous multi-robot cooperation in rescue missions. Paper presented at 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).  
11/08/2022, 15:55
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WIKINDX 6.7.0 | Total resources: 1621 | Username: -- | Bibliography: WIKINDX Master Bibliography | Style: American Psychological Association (APA)