INDUSTRY USE CASES OF NEURAL NETWORK

What is Neural Network?

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

How Neural Network works?

Artificial Neural Networks are made up of a number of different layers. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. Usually, neural networks consist of three types of neurons:

• input
• output
• hidden

Applications of Neural Network:

Given below are domains of commercial applications of neural network technology.

Medical

-Health Care Industry

• Image analysis
• Drug development
• Resource Allocation

-Science & Engineering

• Chemical engineering
• Electrical engineering
• Weather forecasting

-Finance Industry

• Market trading
• Fraud detection
• Credit rating

Industry Use-Cases:

Improving Marketing Strategies:

By adopting Artificial Neural Networks businesses are able to optimize their marketing strategies. Systems powered by Artificial Neural Networks all capable of processing masses of information. This includes customers personal details, shopping patterns as well as any other information relevant to your business. Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation.

Finance:

There are neural network applications for fraud detection, management and forecasting. A great example of neural network finance applications is SAS Real Time Decision Manager. It helps banks to find solutions for business issues, analyzing risks and probable profits.

Cybersecurity:

ANNs can also be used to protect organizations from several types of attacks, such as DDoS and malicious software. AI is better than humans at cybersecurity because they automate the most complex processes required for detecting attacks and analyzing the best way to react to breaches. More in general, neural nets could be used to detect any change or anomaly in network traffic