Page 113 - Proceedings of the 2018 ITU Kaleidoscope
P. 113

Machine learning for a 5G future




            [7] W. Xia, W. Zhu, B. Liao, M. Chen, L. Cai, and L. Huang,  [20] H. N. Roman, S. Kounev, and R. Reussner, “Elasticity
               “Novel architecture for long short-term memory used in  in cloud computing: What it is, and what it is not,”
               question classification,” Neurocomputing, vol. 299, pp.  in Proceedings of the 10th International Conference on
               20–31, 2018.                                       Autonomic Computing (ICAC 2013), 2013.

            [8] Y. Li and H. Cao, “Prediction for tourism flow based on  [21] H. Sak, A. W. Senior, and F. Beaufays, “Long short-term
               LSTM neural network,” Procedia Computer Science,   memory recurrent neural network architectures for large
               vol. 129, pp. 277–283, 2018.                       scale acoustic modeling,” in 15th Annual Conference of
                                                                  the International Speech Communication Association,
            [9] B. Cortez, B. Carrera, Y.-J. Kim, and J.-Y. Jung,  September 2014, pp. 338–342.
               “An architecture for emergency event prediction using
                                                              [22] S. Hochreiter and J. Schmidhuber, “Long Short-Term
               LSTM recurrent neural networks,” Expert Systems with
                                                                  Memory,” Neural computation, vol. 9, no. 8, pp.
               Applications, 2017.
                                                                  1735–1780, 1997.
           [10] F. Liu, Z. Chen, and J. Wang, “Video image target
                                                              [23] Chollet, F. Keras, “Keras: The Python Deep Learning
               monitoring based on RNN-LSTM,” Multimedia Tools
                                                                  library,” https://github.com/fchollet/keras, 2018.
               and Applications, pp. 1–18, 2018.
                                                              [24] M. Ceci, P. F. Lanotte, F. Fumarola, D. P. Cavallo,
           [11] J. Evermann, J.-R. Rehse, and P. Fettke, “Predicting  and D. Malerba, “Completion time and next activity
               process behaviour using deep learning,” Decision   prediction of processes using sequential pattern
               Support Systems, vol. 100, pp. 129 – 140, 2017, smart  mining,” in Discovery Science, S. Džeroski, P. Panov,
               Business Process Management.                       D. Kocev, and L. Todorovski, Eds.  Cham: Springer
                                                                  International Publishing, 2014, pp. 49–61.
           [12] T. Baier, J. Mendling, and M. Weske, “Bridging
               abstraction layers in process mining,” Information  [25] G. T. Lakshmanan, D. Shamsi, Y. N. Doganata,
               Systems, vol. 46, pp. 123–139, 2014.               M. Unuvar, and R. Khalaf, “A markov prediction model
                                                                  for data-driven semi-structured business processes,”
           [13] K. Jensen and L. M. Kristensen, Coloured Petri Nets:  Knowledge  and  Information  Systems,  vol.  42,
               Modelling and Validation of Concurrent Systems, 1st ed.  no. 1, pp. 97–126, Jan 2015. [Online]. Available:
               Springer Publishing Company, Incorporated, 2009.   https://doi.org/10.1007/s10115-013-0697-8
           [14] J. Roa, O. Chiotti, and P. Villarreal, “Behavior  [26] M. Le, B. Gabrys, and D. Nauck, “A hybrid model
               alignment and control flow verification of process   for business process event prediction,” in Research and
               and service choreographies,” Journal of Universal  Development in Intelligent Systems XXIX, M. Bramer
               Computer Science, vol. 18, no. 17, pp. 2383–2406, sep  and M. Petridis, Eds. London: Springer London, 2012,
               2012.                                              pp. 179–192.

           [15] E. Tello-Leal, O. Chiotti, and P. D. Villarreal, “Software
               agent architecture for managing inter-organizational
               collaborations,” Journal of applied research and
               technology, vol. 12, no. 3, pp. 514–526, 2014.
           [16] F. Gottschalk, W. M. van der Aalst, and M. H.
               Jansen-Vullers, “Merging event-driven process chains,”
               in OTM Confederated International Conferences" On
               the Move to Meaningful Internet Systems".  Springer,
               2008, pp. 418–426.

           [17] UML 2.0,   “Unified Modeling Language 2.0,”
               http://www.omg.org/spec/UML/2.0/, 2005.

           [18] K. Greff, R. K. Srivastava, J. Koutník, B. R.
               Steunebrink, and J. Schmidhuber, “LSTM: A search
               space odyssey,” IEEE transactions on neural networks
               and learning systems, vol. 28, no. 10, pp. 2222–2232,
               2017.
           [19] D. Levy, “Production Analysis with Process Mining
               Technology. Dataset,”  2014. [Online]. Available:
               https://data.4tu.nl/repository/collection:event_logs




                                                           – 97 –
   108   109   110   111   112   113   114   115   116   117   118