L. Ravi and S. Vairavasundaram, A collaborative location based travel recommendation system through enhanced rating prediction for the group of users, Computational intelligence and neuroscience, issue.7, 2016.

A. Noulas, S. Scellato, N. Lathia, and C. Mascolo, A random walk around the city: New venue recommendation in locationbased social networks, Privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international confernece on social computing (socialcom), pp.144-153, 2012.

A. Rodriguez-carrion, C. Garcia-rubio, C. Campo, A. Cortés-martín, E. Garcia-lozano et al., Study of lz-based location prediction and its application to transportation recommender systems, Sensors, vol.12, issue.6, pp.7496-7517, 2012.

L. Aalto, N. Göthlin, J. Korhonen, and T. Ojala, Bluetooth and wap push based location-aware mobile advertising system, Proceedings of the 2nd international conference on Mobile systems, applications, and services, pp.49-58, 2004.

N. Marmasse and C. Schmandt, Location-aware information delivery withcommotion, International Symposium on Handheld and Ubiquitous Computing, pp.157-171, 2000.

J. Scott, J. Bernheim-brush, B. Krumm, M. Meyers, S. Hazas et al., Preheat: controlling home heating using occupancy prediction, Proceedings of the 13th international conference on Ubiquitous computing, pp.281-290, 2011.

C. Marta, C. A. Gonzalez, A. Hidalgo, and . Barabasi, Understanding individual human mobility patterns, 2008.

L. Song, D. Kotz, R. Jain, and X. He, Evaluating location predictors with extensive wi-fi mobility data, INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, vol.2, pp.1414-1424, 2004.

L. Song, D. Kotz, R. Jain, and X. He, Evaluating nextcell predictors with extensive wi-fi mobility data, IEEE Transactions on Mobile Computing, vol.5, issue.12, pp.1633-1649, 2006.

A. Asahara, K. Maruyama, A. Sato, and K. Seto, Pedestrian-movement prediction based on mixed markov-chain model, Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, pp.25-33, 2011.

S. Gambs, M. Killijian, and M. Cortez, Next place prediction using mobility markov chains, Proceedings of the First Workshop on Measurement, Privacy, and Mobility, p.3, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00736947

S. Gambs, M. Killijian, and M. Cortez, Show me how you move and i will tell you who you are, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp.34-41, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00556833

Q. Liu, S. Wu, L. Wang, and T. Tan, Predicting the next location: A recurrent model with spatial and temporal contexts, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, pp.194-200, 2016.

T. Mikolov, M. Karafiát, L. Burget, J. , and S. Khudanpur, Recurrent neural network based language model, Eleventh Annual Conference of the International Speech Communication Association, 2010.

, Comparison of loc2vec-CNN, onehot-CNN and O(k) Markov according to the accuracy metric for every user, vol.6

F. Wu, K. Fu, Y. Wang, Z. Xiao, and X. Fu, A spatialtemporal-semantic neural network algorithm for location prediction on moving objects, Algorithms, vol.10, issue.2, p.37, 2017.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, 2013.

S. Bai, Z. Kolter, and V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, 2018.

S. Yue, Imbalanced malware images classification: a cnn based approach, 2017.

S. Laraba and M. Brahimi, Joëlle Tilmanne, and Thierry Dutoit. 3d skeleton-based action recognition by representing motion capture sequences as 2d-rgb images, Computer Animation and Virtual Worlds, vol.28, issue.3-4, p.1782, 2017.

N. Thao-le-minh, K. Inoue, and . Shinoda, A fine-to-coarse convolutional neural network for 3d human action recognition, 2018.

V. Boddapati, A. Petef, J. Rasmusson, and L. Lundberg, Classifying environmental sounds using image recognition networks, Procedia Computer Science, vol.112, pp.2048-2056, 2017.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.115, issue.3, pp.211-252, 2015.

D. Kotz and K. Essien, Analysis of a campus-wide wireless network, Wireless Networks, vol.11, issue.1-2, pp.115-133, 2005.

T. Henderson, D. Kotz, and I. Abyzov, The changing usage of a mature campus-wide wireless network, Computer Networks, vol.52, issue.14, pp.2690-2712, 2008.

P. Baumann, W. Kleiminger, and S. Santini, The influence of temporal and spatial features on the performance of next-place prediction algorithms, Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp.449-458

M. Shah-singh, V. Pondenkandath, B. Zhou, P. Lukowicz, and M. Liwickit, Transforming sensor data to the image domain for deep learningan application to footstep detection, 2017 International Joint Conference on, pp.2665-2672, 2017.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1798-1828, 2013.

J. Camacho, -. , and M. Taher-pilehvar, From word to sense embeddings: A survey on vector representations of meaning, Journal of Artificial Intelligence Research, vol.63, pp.743-788, 2018.

Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, Deepface: Closing the gap to human-level performance in face verification, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1701-1708, 2014.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al.,

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2818-2826, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

N. Forrest, S. Iandola, . Han, W. Matthew, K. Moskewicz et al., Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size, 2016.

G. Huang, Z. Liu, L. Van-der-maaten, and K. Weinberger,

D. Kotz, T. Henderson, I. Abyzov, and J. Yeo, CRAW-DAD dataset dartmouth/campus, 2009.

R. Rehurek and P. Sojka, Software framework for topic modelling with large corpora, Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer, 2010.