Page 471 -

470 Part Three Key System Applications for the Digital Age

Sources: Michael Fitzgerald, “Predicting Where You’ll Go and What 2008; Caroline McCarthy, “Meet Sense Networks, the Latest Player
You’ll Like,” The New York Times, June 22, 2008; Erick Schonfeld, in the Hot ‘Geo’ Market,” news.cnet.com, June 9, 2008.
“Location-Tracking Startup Sense Networks Emerges from Stealth
to Answer the Question: Where Is Everybody?” TechCrunch.com, Case contributed by Dr Ahmed Elragal, German
June 9, 2008; “Macrosense,” sensenetworks.com, accessed July University in Cairo

CASE STUDY QUESTIONS

1. What systems are described here? What valuable 3. How did implementing the Shipping Information
information do they provide? System address the business needs and informa-
2. What value did the IT/IS investments add to tion requirements of Albassami?
Albassami?

the biological or human brain. Neural networks “learn” patterns from large
quantities of data by sifting through data, searching for relationships, building
models, and correcting over and over again the model’s own mistakes.
A neural network has a large number of sensing and processing nodes
that continuously interact with each other. Figure 11.9 represents one
type of neural network comprising an input layer, an output layer, and a
hidden processing layer. Humans “train” the network by feeding it a set of
training data for which the inputs produce a known set of outputs or con-
clusions. This helps the computer learn the correct solution by example.
As the computer is fed more data, each case is compared with the known
outcome. If it differs, a correction is calculated and applied to the nodes
in the hidden processing layer. These steps are repeated until a condition,
such as corrections being less than a certain amount, is reached. The neural
network in Figure 11.9 has learned how to identify a fraudulent credit card

MIS_13_Ch_11 Global.indd 470 1/17/2013 2:30:06 PM