Industry Use-Cases on Neural Networks

What are Neural Networks?

Artificial Neural Networks(ANNs) usually are called Neural Networks(NN) are computing systems inspired by the biological neural networks that constitute animal brains.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons. Neural networks are just one of many tools and approaches used in machine learning algorithms. The neural network itself may e used as a piece in many different machine learning algorithms to process complex data inputs into a space that computers can understand.

NN is being applied to many real-life problems today, including speech and image recognition, spam email filtering, finance, and medical diagnosis, etc.

How Artificial Neural Networks Function?

ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate output through a transfer function to the output layer.

Applications in Deep Learning and Artificial Intelligence

ANN is a form of Deep Learning and one of the main tools used in Machine Learning.

Consequently ANN’s play an increasingly important role in the development of artificial intelligence.

The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.

This technique allows the system’s hidden layers to become versatile.

Adapting to situations where the outcome doesn’t match the one originally intended.

The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.

Deep learning neural networks are networks made up of multiple layers.

This allows the system to become more versatile.

Different layers are able to analyze and extract different features.

This process allows the system to identify new data or images.

It also allows for unsupervised learning and more complex tasks to be undertaken.

Attributes of Neural Networks

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

• Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, the software uses adaptive learning to teach math and language arts.
• Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
• Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
• Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

Abilities of Neural Network

• Classification: NNs organize patterns or datasets into predefined classes.
• Prediction: They produce the expected output from a given input.
• Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
• Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Neural networks in medicine

Artificial Neural Networks (ANN) are currently a ‘hot’ research area in medicine and it is believed that they will receive an extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modeling parts of the human body and recognizing diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).

Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the ‘quantity’. The examples need to be selected very carefully if the system is to perform reliably and efficiently.

Neural Networks in business

Business is a diverted field with several general areas of specialization such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis.

There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.

Neural Networks in Security

Neural networks are widely used for protection from computer viruses, fraud, etc.

One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.