Abstracts
Abstracts Chair
Jeffrey Cummings, University of North Carolina Wilmington
Ordered by presentation time
Which Amazon Reviews Should You Read?
Emre Gokce
University of North Carolina Wilmington
Douglas Kline
University of North Carolina Wilmington
Ron Vetter
University of North Carolina Wilmington
Jeffrey Cummings
University of North Carolina Wilmington
Friday - 11/8/2019 in George Bush at 11:45 am
http://proc.conisar.org/2019/abstracts/5252.html
Customers have to make informed purchasing decisions and online
reviews are often a tool customers use to help. However, if there are thousands of reviews,
which ones should be read? We present a method of selecting a smaller subset of
reviews that is similar to the set of all reviews. This is done through document-term frequency
analysis which provides a matrix of term counts for each document (Salton & McGill, 1983)
and optimization models including both linear and quadratic models with integer variables.
Infobesity: Do Facebook Users Actually Use Those Bookmarked Posts?
Philip Kim
Walsh University
Friday - 11/8/2019 in Whitehall Room at 11:45 am
http://proc.conisar.org/2019/abstracts/5251.html
One of the ongoing challenges for users in the digital age is the
issue of information overload. People are being inundated with too much data.
This is compounded by the fact that many people access information from various
formats, devices, and platforms (Eppler & Mengis, 2004). While there may not be
one singular definition of information overload, it occurs when the vast amounts of
relevant information available exceeds the individual’s ability to effectively process and
use it (Dean & Webb, 2011). The feeling of overload is usually accompanied by a loss of
control and a sense of information glut, or “infobesity” (Bawden & Robinson, 2009). More.....
Munging and liking it: wrangling patient time-related data
with IPython
Niki Kunene
Eastern Connecticut State University
Friday - 11/8/2019 in George Bush at 4:10 pm
http://proc.conisar.org/2019/abstracts/5255.html
The uptake for machine learning techniques in healthcare has lagged
industry for multiple reasons including an underdeveloped IT infrastructure. Data complexity
and variability of healthcare data, and that time is a pivotal factor in data are some of the
other cited reasons for the slower uptake of machine learning in healthcare. In this paper
we examine the use of Python/Pandas wrangle a unique healthcare monitoring dataset with
several time-related variables, in preparation for machine learning and time series analysis.
In knowledge discovery and machine learning projects, data wrangling or data munging is the
process of data understanding and preparation where data is identified, extracted, cleaned and
integrated. More....
Analyzing Drug Overdoses in a Community Using R
Vamsi Gondi
Ball State University
Friday - 11/8/2019 in Whitehall Room at 4:10 pm
http://proc.conisar.org/2019/abstracts/5254.html
Drug abuse and overdoses are causing havoc in all 50 states,
some of the states and communities are spellbound by this addiction.
In 2017 alone there were 70,000 fatalities in the US which is 3 times more than
the reported in 2000 (NIH Overdose Death Rates. (2019)). Drugs such as Fentanyl
and prescription opioids resulted more fatalities than all other drugs that include Heroin,
Methamphetamine and Cocaine (NIH Overdose Death Rates. (2019)). The nation-wide,
state-wide and local community agencies are trying to analyze, control and implement
measures to control this epidemic. The means of collecting localized data at the ground level,
identifying the local root causes that are driving the people toward the drugs and identifying
the impact of the measures put-up by government agencies is lacking. More...
Improving Information Security Awareness one Faction at a Time
Leigh Mutchler
James Madison University
Saturday - 11/9/2019 in Whitehall Room at 11:45 am
http://proc.conisar.org/2019/abstracts/5253.html
Companies in the US reportedly spent 66 billion dollars in 2018 on information
security programs (Statista, 2018). The success of these programs relies on employee compliance
with information security policies (D’Arcy & Greene, 2014; Guo, Yuan, Archer, & Connelly, 2011).
The literature reports that employee compliance relies heavily on how familiar employees are
about information security policies and issues (Cram, D’Arcy, & Proudfoot, 2019).
The most commonly applied control to inform employees about information security is
awareness training (Bauer & Bernroider, 2017; Goo, Yim, & Kim, 2014; Peltier, 2005;
Safa, Von Solms, & Furnell, 2016; Wilson & Hash, 2003). More....