Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Usage Analysis of Mobile Devices   Mobile devices have evolved from just communication devices into an
indispensable part of people's lives in form of smartphones, tablets and smart
watches. Devices are now more personal than ever and carry more information
about a person than any other. Extracting user behaviour is rather difficult
and time-consuming as most of the work previously has been manual or requires
feature extraction. In this paper, a novel approach of user behavior detection
is proposed with Deep Learning Network (DNN). Initial approach was to use
recurrent neural network (RNN) along with LSTM for completely unsupervised
analysis of mobile devices. Next approach is to extract features by using Long
Short Term Memory (LSTM) to understand the user behaviour, which are then fed
into the Convolution Neural Network (CNN). This work mainly concentrates on
detection of user behaviour and anomaly detection for usage analysis of mobile
devices. Both the approaches are compared against some baseline methods.
Experiments are conducted on the publicly available dataset to show that these
methods can successfully capture the user behaviors.

30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th