Industry use cases of Neural Networks

What is Neural Networks?

Neural networks are just one of many tools and approaches used in machine learning algorithms. The neural network itself may be used as a piece in many different machine learning algorithms to process complex data inputs into a space that computers can understand.

Neural networks are being applied to many real-life problems today, including speech and image recognition, spam email filtering, finance, and medical diagnosis, to name a few.

How Artificial Neural Networks Function

Use Cases of Neural Network

Pattern Recognition Neural Networks

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns.

Some examples of the pattern are — fingerprint image, a handwritten word, a human face or a speech signal.

Fuzzy Logic using Neural Networks

Fuzzy logic and Neural networks have one thing in common. They can be used to solve problems of pattern recognition and others that do not involve any mathematical model.

Systems combining both fuzzy logic and neural networks are neuro-fuzzy systems.

These systems (Hybrid) can combine the advantages of both neural networks and fuzzy logic to perform in a better way.

Fuzzy logic and Neural Networks have been integrated to use in the following applications –

  • Automotive engineering
  • Applicant screening of jobs
  • Control of crane
  • Monitoring of glaucoma

In a hybrid (neuro-fuzzy) model, Neural Networks Learning Algorithms are fused with the fuzzy reasoning of fuzzy logic.

The neural network determines the values of parameters, while if-then rules are handled by fuzzy logic.

Face Recognition using Neural Networks

The associated assignment of face detection has direct relevance to the fact that images need to be analyzed and faces identified, earlier than they can be recognized.

Neural networks in medicine

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 are Improving Marketing Strategies

This application of Artificial Neural Networks can save businesses both time and money.

Neural Networks in Optimizing Store Layout

Improving Search Engine Functionality

The company reported that its error rate had dropped from 23% down to just 8%. Google’s application shows that neural networks can help to improve search engine functionality.

Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites. This means that many companies can improve their website search engine functionality.

This allows customers with only a vague idea of what they want to easily find the perfect item.

Amazon has reported sales increases of 29% following improvements to its recommendation systems.