Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform

2013 ◽  
Author(s):  
Apichon Witayangkurn ◽  
Teerayut Horanont ◽  
Yoshihide Sekimoto ◽  
Ryosuke Shibasaki
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mobile Phones ◽  
Event Detection ◽  
Large Scale ◽  
Hidden Markov ◽  
Gps Data ◽  
Cloud Platform ◽  
Anomalous Event

scholarly journals Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259670
Author(s):  
Albertas Dvirnas ◽  
Callum Stewart ◽  
Vilhelm Müller ◽  
Santosh Kumar Bikkarolla ◽  
Karolin Frykholm ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Sequence Information ◽  
True Positive ◽  
Dna Barcodes ◽  
Structural Variations ◽  
Genomic Alterations ◽  
Data Set

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.

scholarly journals Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data

2012 ◽  
Vol 12 ◽  
pp. 218-223 ◽  
Author(s):  
Naveen K. Bansal ◽  
Xin Feng ◽  
Wenjing Zhang ◽  
Wutao Wei ◽  
Yuanhao Zhao
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Temporal Pattern ◽  
Hidden Markov ◽  
Sludge Bulking

scholarly journals Bayesian event detection for sport games with hidden Markov model

2011 ◽  
Vol 15 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Shigeru Motoi ◽  
Toshie Misu ◽  
Yohei Nakada ◽  
Tomohiro Yazaki ◽  
Go Kobayashi ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov

scholarly journals Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

2016 ◽  
Author(s):  
Hong Gao ◽  
Hua Tang ◽  
Carlos Bustamante
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

Hidden Markov Model based driving event detection and driver profiling from mobile inertial sensor data

2015 ◽  
Author(s):  
Saurabh Daptardar ◽  
Vignesh Lakshminarayanan ◽  
Sharath Reddy ◽  
Suraj Nair ◽  
Saswata Sahoo ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Model Based
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