Predicting clustered locations using HMMs with varying information levels

Halim, Sadaf MD; Murshed, Md. Najib

dc.contributor.author	Halim, Sadaf MD
dc.contributor.author	Murshed, Md. Najib
dc.date.accessioned	2020-10-27T15:13:34Z
dc.date.available	2020-10-27T15:13:34Z
dc.date.issued	2018-11-15
dc.identifier.citation	[1] Wesley Mathew, Ruben Raposo et al. : “Predicting future locations with Hidden Markov Models”, ACM Conference on Ubiquitous Computing, 2012 [2] Neelabh Pant, Ramez Elmasri et al. : “Detecting Meaningful Places and Predicting Locations Using Varied K-Means and Hidden Markov Model”, 17th SIAM International Conference on Data Mining, April 2017 [3] Fabrice Benhamouda et al. : “A New Framework for Privacy-Preserving Aggregation of Time-Series Data” , ACM Transactions on Privacy and Security, January 2018 [4] Chao Zhang, Keyang Zhang et al. : “GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media” , Knowledge Discovery and Data Mining 2016, ACM [5] Lizi Lao, Xianghan He, Et al. : “Attributed Social Network Embedding” , IEEE Transactions on Knowledge and Data Engineering, May 2017 [6] Neil ZG, Bin L.: “Attribute inference attacks on online social networks”, ACM Transactions on Privacy and Security, January 2018 [7] “Fitness tracking app Strava gives away location of secret US army bases”, Accessed: 10/09/2018. Available: https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location- of-secret-us-army-bases#img-1 [8] “GeoLife GPS Trajectories”. Dataset available at: https://www.microsoft.com/en- us/download/details.aspx?id=52367&from=https%3A%2F%2Fresearch.microsoft.com%2Fen- us%2Fdownloads%2Fb16d359d-d164-469e-9fd4-daa38f2b2e13%2F [9] “Machine Learning – Clustering”, Accessed: 13/10/2018. Available: https://web.stanford.edu/class/cs102/notes/Clustering.pdf [10] “Hidden Markov Model: Simple Definition & Overview”, Accessed: 13/10/2018. Available: https://www.statisticshowto.datasciencecentral.com/hidden-markov-model/	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/584
dc.description	Supervised by Prof. Dr. Abu Raihan Mostofa Kamal	en_US
dc.description.abstract	Due to the recent explosive growth of location-aware services, predicting the next position of a user is of increasing importance in enabling intelligent information services. To this effect, we first introduce a system where the Hidden Markov Model (HMM), a very popular tool used in fields like Natural Language Processing and Bioinformatics, is used to generate predictions over clustered location data. This method prioritizes the generality of the “area” of the user in favor of his or her exact co-ordinates. Secondly, frequent data reporting is expensive, and minimizing this whilst maintaining reasonable accuracy is preferable. On the flipside, security concerns like information leakage arise where hiding a user and preventing good prediction is preferable. Both these issues require us to analyze a relationship between the accuracy of future predictions against the frequency of current data being reported. This was our second end goal – we investigated how accuracy changed with data-reporting frequency (and other parameters) in an attempt to establish a model for accuracy variation.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Predicting clustered locations using HMMs with varying information levels	en_US
dc.type	Thesis	en_US