Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering
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Debra, N. (2006). Organizational Behavior: Foundations, Realities & Challenges, South-Western.
Baker, 1987, Evolving concepts of biological control of plant pathogens, Ann. Rev. Phytopathol., 25, 67, 10.1146/annurev.py.25.090187.000435
Ratti, 2006, Mobile landscapes: Using location data from cell phones for urban analysis, Environ. Plan. B Plan. Des., 33, 727, 10.1068/b32047
Zhao, 2017, Mining user attributes using large-scale APP lists of smartphones, IEEE Syst. J., 11, 315, 10.1109/JSYST.2015.2431323
Rosser, 2011, Smartphone applications for pain management, J. Telemed. Telecare, 17, 308, 10.1258/jtt.2011.101102
Pandey, 2013, Smartphone apps as a source of cancer information: Changing trends in health information-seeking behavior, J. Cancer Educ., 28, 138, 10.1007/s13187-012-0446-9
Rawassizadeh, 2016, Scalable daily human behavioral pattern mining from multivariate temporal data, IEEE Trans. Knowl. Data Eng., 28, 3098, 10.1109/TKDE.2016.2592527
Angelov, P. (2013). Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time, John Wiley & Sons, Ltd.
Lara, 2013, A survey on human activity recognition using wearable sensors, IEEE Commun. Surv. Tutor., 15, 1192, 10.1109/SURV.2012.110112.00192
Gravenhorst, 2015, Mobile phones as medical devices in mental disorder treatment: An overview, Pers. Ubiquitous Comput., 19, 335, 10.1007/s00779-014-0829-5
Janecek, 2015, The cellular network as a sensor: From mobile phone data to real-time road traffic monitoring, IEEE Trans. Intell. Transp. Syst., 16, 2551, 10.1109/TITS.2015.2413215
Khan, 2013, Mobile phone sensing systems: A survey, IEEE Commun. Surv. Tutor., 15, 402, 10.1109/SURV.2012.031412.00077
Paraskevopoulos, P., Dinh, T., Dashdorj, Z., and Palpa, Y.T. (2013, January 1–3). Identification and characterization of human behavior patterns from mobile phone data. Proceedings of the NetMob 2013, Cambridge, MA, USA.
Abdallah, 2015, Adaptive mobile activity recognition system with evolving data streams, Neurocomputing, 150, 304, 10.1016/j.neucom.2014.09.074
Motiwalla, 2007, Mobile learning: A framework and evaluation, Comput. Educ., 49, 581, 10.1016/j.compedu.2005.10.011
Blondel, 2015, A survey of results on mobile phone datasets analysis, EPJ Data Sci., 4, 1, 10.1140/epjds/s13688-015-0046-0
Srinivasan, V., Moghaddm, S., Mukherji, A., Rachuri, K.K., Xu, C., and Tapia, Y.E.M. (2014, January 13–17). MobileMiner: Mining Your Frequent Patterns on Your Phone. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.
Kostakos, V., Ferrerira, D., Goncalves, J., and Hosio, Y.S. (2016, January 12–16). Modelling Smartphone Usage: A Markov State Transition Model. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.
Botta, 2015, Quantifying crowd size with mobile phone and Twitter data, R. Soc. Open Sci., 2, 150, 10.1098/rsos.150162
Jo, 2012, Circadian pattern and burstiness in mobile phone communication, New J. Phys., 14, 13, 10.1088/1367-2630/14/1/013055
Aledavood, 2015, Daily rhythms in mobile telephone communication, PLoS ONE, 10, 1, 10.1371/journal.pone.0138098
Incel, 2013, A review and taxonomy of activity recognition on mobile phones, BioNanoScience, 3, 145, 10.1007/s12668-013-0088-3
Shoaib, 2015, A survey of online activity recognition using mobile phones, Sensors, 15, 2059, 10.3390/s150102059
Ghotekar, 2016, Analysis and Data Mining of Call Detail Records using Big Data Technology, Int. J. Adv. Res. Comput. Commun. Eng., 5, 280, 10.17148/IJARCCE.2016.51264
Moses, E.O., and Festus, Y.O.A. (2017). Multidimensional Analysis and Mining of Call Detail Records Using Pattern Cube Algorithm. Comput. Eng. Inf. Technol., 6.
Leo, 2016, Call detail records to characterize usages and mobility events of phone users, Comput. Commun., 95, 43, 10.1016/j.comcom.2016.05.003
Saramaki, 2015, From seconds to months: Multi-scale dynamics of mobile telephone calls, Eur. Phys. J. B, 88, 164, 10.1140/epjb/e2015-60106-6
Botta, 2017, Analysis of the communities of an urban mobile phone network, PLoS ONE, 12, 1, 10.1371/journal.pone.0174198
Bitar, N., Imran, A., and Refai, Y.H. (2016, January 3–6). A user centric self-optimizing grid-based approach for antenna steering based on call detail records. Proceedings of the 2016 IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar.
Kumar, M., Hanumanthappa, M., and Kumar, Y.T.S. (2017, January 19–21). Crime investigation and criminal network analysis using archive call detail records. Proceedings of the 2016 International Conference on Advanced Computing ICoAC, Chennai, India.
Longtong, Y., and Narapiyakul, Y.L. (2016, January 14–17). Suspect tracking based on call logs analysis and visualization. Proceedings of the 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand.
Angelov, P. (2009). Evolving fuzzy systems. Encyclopedia of Complexity and Systems Science, Springer.
Angelov, P., Ramezani, R., and Zhou, Y.X. (2008, January 1–8). Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system. Proceedings of the International Joint Conference on Neural Networks, Hong Kong, China.
Guedes, 2016, An evolving approach to unsupervised and Real-Time fault detection in industrial processes, Expert Syst. Appl., 63, 134, 10.1016/j.eswa.2016.06.035
Kangin, D., Angelov, P., Iglesias, J.A., and Sanchis, Y.A. (November, January 29). Evolving Classifier TEDAClass for Big Data. Proceedings of the 2015 INNS Conference on Big Data, San Francisco, CA, USA.
Iglesias, 2010, Human Activity Recognition Based on Evolving Fuzzy Systems, Int. J. Neural Syst., 20, 355, 10.1142/S0129065710002462
Grinstein, G., Plaisant, C., Laskowski, S., O’Connell, T., and Scholtz, J. (2008, January 19–24). VAST 2008 Challenge: Introducing mini-challenges. Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, Columbus, OH, USA.
Mantzaris, V., and Highan, Y.D.J. (2016). Asymmetry through time dependency. Eur. Phys. J. B Condens. Matter Complex Syst., 89.
Heard, 2016, Convergence of Monte Carlo distribution estimates from rival samplers, Stat. Comput., 26, 1147, 10.1007/s11222-015-9595-0
Leung, C.K., Johnston, P., Carmichael, C.L., Xing, R.R., and Hung-Cheung Yuen, Y.D.S. (2017). Interactive Visual Analytics of Big Data. Ontologies and Big Data Considerations for Effective Intelligence, IGI Global.
Angelov, P.P. (2002). Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems, Springer.
Angelov, P.P. (2007). Machine Learning. (Collaborative Systems). (WO2008053161), U.S. Patent.
Sadeghi-Tehran, P., Angelov, P., and Ramezani, Y.R. (July, January 28). A Fast Approach to Autonomous Detection, Identification, and Tracking of Multiple Objects in Video Streams under Uncertainties. Proceedings of the 2010 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), Dortmund, Germany.
Iglesias, 2010, Evolving classification of agents behaviors: A general approach, Evol. Syst., 1, 161, 10.1007/s12530-010-9008-8
Ruan, D., Chen, G., Kerrer, E.E., and Wets, Y.G. (2005). Intelligent Data Mining: Techniques and Applications, Springer Science & Business Media.
2010, An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Trans. Syst. Man Cybern. Part B, 34, 484