Page 203 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

Chapter 7 Early detection and diagnosis using deep learning 193

Figure 7.1 Artificial neural networks.

1.1.1 Applications
DL and its concepts can be applied to the real-world and solve
social issues. This groundbreaking technology has the power to
affect industries ranging from healthcare to finance. It can
uncover new information and make existing procedures both
easier and more efficient. While it has a vast variety of application
in various fields, some of the most important and upcoming ones
are as follows:
1. Self-driving cars
Autonomous driving has been worked upon for a long time
now, and DL is our best shot for achieving that goal. By using
data obtained from geomapping, sensors, and cameras, scientists
are developing models that ensure safe driving practices. These
self-driving cars account into various factors such as traffic and
pedestrian-only roads and are run through millions of possible
scenarios. Certain companies are striving toward engineering-
added functions to these cars such as automated food delivery.
2. Fraud news detection
News aggregation is extensively used in today's world to
customize every consumer's news feed. Various social, geograph-
ical, and economical parameters are considered to cater to every-
one's individual preferences. Fraud news detection serves as an
extension of this news aggregation. News holds tremendous
power in determining important events such as elections. DL
helps filter out fake news and helps get rid off privacy breaches.