2,194 research outputs found
A Large-Scale CNN Ensemble for Medication Safety Analysis
Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing
drug surveillance, and data from health-related forums and medical communities
can be of a great significance for estimating such effects. In this paper, we
propose an end-to-end CNN-based method for predicting drug safety on user
comments from healthcare discussion forums. We present an architecture that is
based on a vast ensemble of CNNs with varied structural parameters, where the
prediction is determined by the majority vote. To evaluate the performance of
the proposed solution, we present a large-scale dataset collected from a
medical website that consists of over 50 thousand reviews for more than 4000
drugs. The results demonstrate that our model significantly outperforms
conventional approaches and predicts medicine safety with an accuracy of 87.17%
for binary and 62.88% for multi-classification tasks
Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications
Due to the popularity of smart mobile phones and context-aware technology,
various contextual data relevant to users' diverse activities with mobile
phones is available around us. This enables the study on mobile phone data and
context-awareness in computing, for the purpose of building data-driven
intelligent mobile applications, not only on a single device but also in a
distributed environment for the benefit of end users. Based on the availability
of mobile phone data, and the usefulness of data-driven applications, in this
paper, we discuss about mobile data science that involves in collecting the
mobile phone data from various sources and building data-driven models using
machine learning techniques, in order to make dynamic decisions intelligently
in various day-to-day situations of the users. For this, we first discuss the
fundamental concepts and the potentiality of mobile data science to build
intelligent applications. We also highlight the key elements and explain
various key modules involving in the process of mobile data science. This
article is the first in the field to draw a big picture, and thinking about
mobile data science, and it's potentiality in developing various data-driven
intelligent mobile applications. We believe this study will help both the
researchers and application developers for building smart data-driven mobile
applications, to assist the end mobile phone users in their daily activities.Comment: Journal, 11 pages, Double Colum
Identifying Recent Behavioral Data Length in Mobile Phone Log
Mobile phone log data (e.g., phone call log) is not static as it is
progressively added to day-by-day according to individ- ual's diverse behaviors
with mobile phones. Since human behavior changes over time, the most recent
pattern is more interesting and significant than older ones for predicting in-
dividual's behavior. The goal of this poster paper is to iden- tify the recent
behavioral data length dynamically from the entire phone log for recency-based
behavior modeling. To the best of our knowledge, this is the first dynamic
recent log-based study that takes into account individual's recent behavioral
patterns for modeling their phone call behaviors.Comment: 14th EAI International Conference on Mobile and Ubiquitous Systems:
Computing, Networking and Services (MobiQuitous 2017), Melbourne, Australi
Dynamical Casimir effect for magnons in a spinor Bose-Einstein condensate
Magnon excitation in a spinor Bose-Einstein condensate by a driven magnetic
field is shown to have a close analogy with the dynamical Casimir effect. A
time-dependent external magnetic field amplifies quantum fluctuations in the
magnetic ground state of the condensate, leading to magnetization of the
system. The magnetization occurs in a direction perpendicular to the magnetic
field breaking the rotation symmetry. This phenomenon is numerically
demonstrated and the excited quantum field is shown to be squeezed.Comment: 8 pages, 3 figure
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