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10 Business Applications of Neural Network (With Examples!)

Jan 7, 20214 min read

Patrycja Mach

IT Marketing Expert at Ideamotive. Focused on Growth Marketing, Data Analytics and Marketing Automation.

Do you want to see some great examples of neural network business applications? Then read further, and enjoy great cases of artificial intelligence and machine learning implementation in the software for different industries.

What are Neural Networks in 2020?

There is no better engineer and developer than nature. So, scientists often copy the principles and structures of natural things in their inventions. And neural networks are one of such cases. These are mathematical models that kind of imitate the functioning of the human brain. Neural networks are the systems that can learn. They act not only in accordance with given algorithms and formulas but also on the basis of past experience. Further, we will overview their structure and the principle of their functioning a bit more detailed.

How do Neural Networks work?

A neural network is a bundle of neurons connected by synapses. Talking about the artificial one, the role of neurons are played by the units that perform calculations. Each of these “neurons”:

  • receives data from the input layer;
  • processes it performing simple calculations with it;
  • and then transmits it to another “neuron”.

Usually, neural networks consist of three types of neurons:

  • input;
  • output;
  • hidden.

Only single layer neural networks make an exception. They don’t have hidden neurons.

 

The synapses are responsible for connecting neurons with each other. Each neuron has got multiple outcoming synapses that attenuate or amplify the signal. This makes it possible for the neurons to work in the same way, but to show the different results depending on a certain situation.

 

Also, neurons are capable of changing their characteristics over a period of time.

So, a typical neural network works like this:

  • it receives certain data through the input layer of neurons;
  • the data is processed by the neurons and passed to the next layer with the help of synapses each of which has its own coefficient;
  • the next layer of neurons receive the information that is the sum of all of all data for neural networks, which are multiplied by the weight coefficients (each by its own);
  • the resulting value is substituted into the activation function, resulting in the formation of output information;
  • information is passed on until it reaches the final exit.

 

The first launch of the neural network will not give correct results, because it has not yet been trained. So, it takes some time for the neural network to be taught before releasing it to real work.

Examples of Neural Network Business Applications

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

 

So, let’s look at some examples of neural network applications in different areas. Mostly, in:

  • eCommerce;
  • Finance;
  • Healthcare;
  • Security;
  • Logistics.

eCommerce

This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, AliExpress, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.

 

Amazon shows related products


AliExpress shows the recommended products basing on the items viewed by the user

 

As for more complicated applications of neural networks in eCommerce, there is a very interesting startup called PixelDTGAN. This product is developed to help sellers save the budget on photographers’ services. There is no need to organize photo sets as the special algorithm automatically makes the pictures of the clothes worn by models. All is needed to do is to resize the images of the items to 64*64, and get the result.

 

Examples of PixelIDTGAN work results

Finance

In this industry, there are neural network applications for fraud detection, management, and forecasting. Let’s look at some samples.

A great example of neural network finance applications is SAS Real Time Decision Manager. It helps banks to find solutions for business issues (for instance, whether to give credit to a certain person) analyzing risks and probable profits.

 

The screenshot of SAS Real Time Decision Manager

 

As for financial forecasting, there are plenty of solutions that predict the exchange rate changes. For example, the startup Finprophet is the software that uses a neural network of deep learning for giving the forecast about a wide range of financial instruments like currencies, cryptocurrencies, stocks, futures.

 

Finprophet is giving the forecast about Bitcoin - US Dollar currency pair 

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible.

 

IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.

 

This is the original version of IBM Watson, which includes 2800 processor cores and 15 terabytes of memory.

 

Doctors can use the abilities of IBM Watson with the help of tablets with cloud connection.

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.

 

ICSP Neural scanning station 

 

One more sample of using AI and ML for security purposes is Shape security which provides several finance solutions.

 

The wide range of solutions for defense from fraud by Shape security

Logistics

This industry needs a lot of management that is to be done manually by employees of many companies. But nowadays, neural networks are capable of routing and dispatching.

For example, Wise Systems is an autonomous system which lets a user:

  • plan routes and monitor them;
  • customize shipping routes in real-time with the help of predictive features.

Screenshot of Wise Systems

 

One more solution is FourKites. This is a visibility program that works in a real-time mode. It helps to plan and monitor routes and predict the time of delivery.

 

The interface of FourKites on laptop and mobile phone

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

 

Here is what Tesla Autopilot sees

Wrap up

As you can see, AI and ML are the future of all the industries. These technologies help to make decisions, automate the working processes, prevent fraud, and do other important tasks. And they will continue developing. So, are you looking for Neural Network Programming experts? Or maybe you wonder how AI and ML can boost your business? Talk to us, and we will provide you with top consultants and engineers matched with your product, industry, and technology.

Patrycja Mach

Patrycja is a B2B Marketing Expert with experience in growing IT businesses since 2014. She creates and implements 360-degree marketing strategy. In her career, she focuses on lead generation based on marketing automation, collecting data, AB experiments and complex customer journeys. Business, self-development and psychology enthusiast.

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