Page 155 - Computer - 8

Fraud Detection: The heart of data mining is finding patterns, trends, and correlations that link data
points together. Therefore, a company can use data mining to identify outliers or correlations that
should not exist. For example, a company may analyse its cash flow and find a recurring transaction
to an unknown account. If this is unexpected, the company may wish to investigate should funds be
potentially mismanaged.
Human Resources: Human resources often has a wide range of data available for processing, including
data on retention, promotions, salary ranges, company benefits, and employee satisfaction surveys.
Data mining can correlate this data to get a better understanding of why employees leave and what
entices recruits to join.
Customer Service: Customer satisfaction may be caused (or destroyed) for a variety of reasons. Imagine
a company that ships goods. A customer may become unhappy with shipping time, shipping quality,
or communication on shipment expectations. That same customer may become frustrated with long
telephone wait times or slow email responses. Data mining gathers operational information about
customer interactions and summarises findings to determine weak points as well as highlights of what
the company is doing right.

NEURAL NETWORKS
A neural network is a network of biological neurons, in case of humans, or, in a modern sense, an
artificial neural network, composed of artificial neurons or nodes. Artificial Neural Networks (ANNs)
are used for solving artificial intelligence (AI) problems. The connections of the biological neurons are
modelled in artificial neural networks as weights between nodes. A positive weight reflects an excited
connection, while a negative weight reflects an inhibitory connection. All inputs are modified by a
weight and summed. This activity is referred to as a linear combination. Finally, an activation function
controls the scale of the output. For example, an acceptable range of output is usually between 0 and
1, or it could be −1 and 1.
These artificial networks may be used for predictive modelling, adaptive control, and other applications
where they can be trained through a dataset. Self-learning resulting from experience can occur within
networks, which can derive conclusions from a complex and seemingly unrelated set of information.

How do Neural Networks Work?
The human brain is the inspiration behind artificial neural network architecture. Human brain cells,
called neurons, form a complex, highly interconnected network and send electrical signals to each
other to help humans process information. Similarly, an artificial neural network is made of artificial
neurons that work together to solve a problem. Artificial neurons are software modules called nodes,
and artificial neural networks are software programs or algorithms that use computing systems to solve
mathematical calculations.
Basic Structure of ANNs

The idea of ANNs is based on the belief that working of human brain by making the right connections,
can be imitated using silicon and wires as living neurons and dendrites.
The human brain is composed of 86 billion nerve cells called neurons. They are connected to other
thousand cells by axons. Stimuli from external environment or inputs from sensory organs are accepted
by dendrites. These inputs create electric impulses, which quickly travel through the neural network.
A neuron can then send the message to other neuron to handle the issue or does not send it forward.
ANNs are composed of multiple nodes which imitate biological neurons of human brain. The neurons

153