Q1:
Many predictive analytic models are based on neural network
technologies. What is the role of neural networks in predictive analytics?
The role of neural networks in predictive analytics is most
useful for identification, classification, and prediction and is capable of
finding and differentiating patterns. This data can then be used to make
predictions about the likelihood of the occurrence of future events.
How can neural networks help predict the likelihood of
future events? In answering this questions, specifically reference Blue Cross
Blue Shield of Tennessee.
Blue Cross Blue Shield
of Tennessee – uses a neural network predictive model to predict which health
care resources will be needed by which postoperative patients months and even
years into the future. According to Soyal Momin, manager of research and
development at Blue Cross Blue Shield, “If we’re seeing a pattern that predicts
heart failure, kidney failure, or diabetes, we want to know that as soon as
possible.”
Neural networks can help predict the likelihood of future
events for Blue Cross Blue Shield of Tennessee because by using artificial
intelligence, it can predict any health issue trends. This information can then
be used to help treat any medical diseases among patients.
Q2:
What if the Richmond police began to add demographic data
its predictive analytics system to further attempt to determine the type of person
(by demographic) who would in all likelihood commit a crime. Is predicting the
type of person who would commit a crime by demographic data (ethnicity, gender,
income level, and so on) good or bad?
Good. Predicting a crime by demographic data may be
unethical, but in some cases it could be very helpful to saving people’s lives.
Therefore whatever can help with a predictive analytics system is qualified as
beneficial.
Q3:
In the movie Gattaca, predictive analytics were used to
determine the most successful career for a person. Based on DNA information,
the system determined whether or not an individual was able to advance through
an educational track to become something like an engineer or if the person
should complete only a lower level of education and become a janitor. The
government then acted on the system’s recommendations and placed people in
various career tracks.
Is this a good or bad use of technology?
This is bad use of predictive analytics because a person may
not want to grow up and follow in the same career path as his or her parents.
By using the DNA information, the system will come up with careers mainly
related to those of the person’s parents. Although there may be patterns in the
career fields chosen within a family, there is usually one person in a family
that is not exactly like everyone else related to him or her. Therefore, this
person would be more likely to pursue a career in a different field than his or
her relatives. If a person were to work in the career field chosen through DNA
information, he or she may not enjoy it as much as working in a career field of
their choice. This is because working in a career field chosen by someone else
is not a person’s personal dream in the career industry. This is due to how
each people feel, think and behave are different.
How is this different from the variety of personal tests you
can take that inform you of your aptitude for different careers?
Personal tests ask various personal questions, rather than
using historical data to predict a person’s future. Personal tests take more
into account what the individual taking the test enjoys doing as leisure
activities rather than what the previous generations enjoyed to predict a person’s
possible career path. Personal tests results can be more individualized because
the results depend on the individual’s personal answer, rather than the answer
of a relative in the previous generations.
Q4:
What role can geographic information systems (GISs) play in the
use of predictive analytics?
GISs can show maps of possible future events.For example GISs could generate a map of the population of certain
animals and their location in the coming years or the locations people may
start to migrate to.
As you answer this question, specifically reference FedEx’s
use of predictive analytics to
(1) determine which customers will respond negatively to a
price increase
FedEx can use GISs to map a location where people are poorer.
(2) project additional revenues from proposed drop-box
locations
FedEx use GISs to locate future spots where they can make more
revenue.
Q5:
The Department of Defense (DoD) and the Pacific Northwest
National Laboratory are combining predictive analytics with visualization
technologies to predict the probability that a terrorist attack will occur. For
example, suspected terrorists caught on security cameras who loiter too long in
a given place might signal their intent to carry out a terrorist attack.
How can this type of predictive analytics be used in an
airport?
Put up cameras around the building and if a suspected terrorist
seems to be wondering / intending to carry out a terrorist attack, arrest them.
The scanning systems can pick up on traits such as how much
luggage they may carry, what they wear, how long they stay in each terminal,
etc.
At what other buildings and structure might this be used?
Anywhere where there is already some kind of security, this
could be added. For example, tourist attractions, museum, high traffic areas,
government buildings, concerts, banks, etc.
No comments:
Post a Comment