Industry use cases of Neural Networks

In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “Neural Networking” and “Deep learning”

  • In Biological the term Neural Network is the is composed of a groups of chemically connected or functionally associated neurons in the Human Brains. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. it helps our Brain to Learn and recognize many arithmetic , logical and emotional skills. Using the Same Biological concept of Humans Brains Neurons the Artificial Neuron Network came in place.
  • Artificial neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
  • The Artificial Neural Networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
  • A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.

What does a neural network consist of?

  • A typical neural network has anything from a few dozen to hundreds, thousands, or even millions of artificial neurons called units arranged in a series of layers, each of which connects to the layers on either side. Some of them, known as input units, are designed to receive various forms of information from the outside world that the network will attempt to learn about, recognize, or otherwise process. Other units sit on the opposite side of the network and signal how it responds to the information it’s learned; those are known as output units.
  • In between the input units and output units are one or more layers of hidden units, which, together, form the majority of the artificial brain. Most neural networks are fully connected, which means each hidden unit and each output unit is connected to every unit in the layers either side. The connections between one unit and another are represented by a number called a weight, which can be either positive (if one unit excites another) or negative (if one uAlthough a simple neural network for simple problem solving could consist of just three layers, as illustrated here, it could also consist of many different layers between the input and the output. A richer structure like this is called a deep neural network (DNN).

How a Neural Network Works?

A neural network has many layers. Each layer performs a specific function, and the complex the network is, the more the layers are. That’s why a neural network is also called a multi-layer perceptron.

The purest form of a neural network has three layers:

  1. The input layer
  2. The hidden layer
  3. The output layer

As the names suggest, each of these layers has a specific purpose. These layers are made up of nodes. There can be multiple hidden layers in a neural network according to the requirements. The input layer picks up the input signals and transfers them to the next layer. It gathers the data from the outside world.

The hidden layer performs all the back-end tasks of calculation. A network can even have zero hidden layers. However, a neural network has at least one hidden layer. The output layer transmits the final result of the hidden layer’s calculation.

Applications of Neural Networks in Industries

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.

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


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, Flipkart AlibabaExpress, 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.


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.
  • 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


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.


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


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 System 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 Google Maps. 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.

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

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