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Artificial neural Network

Real-Life Applications of Artificial Neural Networks

A subset of machine learning, also known as artificial neural network (ANN), are models that use computation algorithms. Neural networks possess the unique capability to deduce meaning from complex or inexact data to identify and discern patterns that are too complex for humans or other techniques used by computers. Neural networks have given us greater convenience in many ways, including applications for ridesharing, Gmail intelligent sorting, and suggestions from Amazon.

The most revolutionary feature of neural networks is that once established, they can learn by themselves. This is because they mimic human brains composed of neurons, the primary elements of humans, and neural networks—data transmission.

An artificial intelligence firm that uses neural networks to develop their natural language processing applications that include an application that automatizes grammar correction, Perfect Tense. “In both cases, the neurons continuously alter how they respond to the stimulus. If you’ve done something right, you’ll receive positive feedback from your neurons, making them much more likely to occur in the subsequent instance. In contrast, if neurons receive negative feedback, they will become more likely not to trigger the future instance.”

The use of Artificial Neural networks:

Handwriting Recognition

The concept of feeding forward networks to detect handwritten characters is simple. A bitmap of the handwritten letter is input, with the right note or digit being the output. The user has to help train the network by providing it with handwritten patterns.

Two typical applications for handwriting recognition include:

  • Optical character recognition for data entry
  • Signature validation on a bank cheque

Feed-forward networks possess these features:

  • First, they organize perceptron into layers, in which the top layer takes in inputs, and the final layer provides outputs. The middle layer has no connection to the outside world, so we refer to them as hidden layers.
  • Each perceptron in one layer is linked to each perceptron on the layer next. This means that the information gets “fed forward” from one layer to the following continuously. This is the reason we refer to these networks as feed-forward networks.
  • There is no link between perceptron and one layer.

Traveling Salesman Problem

The traveling salesman problem is about finding the shortest route to reach every city in a region, and we can employ Neural Networks to solve this issue.

The neural network algorithms, such as an algorithm that is genetics, begin by randomizing the direction of the network to tackle the problem. The algorithm picks the city randomly every time and locates the nearest city, and this process repeats many times. Each time one alters the network form, the network becomes an elongated ring around all cities.

The algorithm is designed to minimize any lengths of rings. With this method, we can estimate the travel problem.

Image Compression

A Neural Network used for image compression comprises an equal amount of output and input layers. In the intermediate layer, it is smaller in size. The ratio between the layer that is the input and the intermediary layer determines the compression rate of the network.

We can calculate the comparison ratio of image compression by using the following formula.

Comparison Ratio = Input Layer / Intermediate Layer

The concept of neural networks that compress data is to store, decrypt and recreate the original image. In the network, we can utilize inputs for training purposes and training purposes.

Stock Exchange Prediction

The accuracy of a prediction made by neural networks has helped them be helpful in the forecast of the stock market. For large businesses making predictions about the stock market is a common practice. One can accomplish this by using the latest trends, the political climate, public opinion, and economic experts’ recommendations.

We can also utilize neural networks to predict currency and business failure prediction—debt risk assessment and credit acceptance.

Online Shopping

It often happens while online shopping when you were planning to buy an item but end up purchasing a long list of things, thanks to some super-awesome recommendations? Yes, blame neural networks for that. Through the use of neural networks and their knowledge, they are developing Artificial Intelligence systems that know you better than you do.

  • Companies like MJ Futures have claimed excellent 199.2 percent returns over two years using their neural network prediction techniques.
  • Dean Barr and Walter Loick at LBS Capital Management achieved promising results using a simple neural network. It was based on six financial indicators that were used as inputs. They comprise ADX, the value currently of the S&P 500, and the net change of the S&P 500 value over the previous five days. ADX represents the average direction of change over the last 18 days.

That’s everything about Artificial Neural Network Applications. We hope you enjoyed our explanation.


In today’s manufacturing environment, new approaches and techniques are constantly developing. Artificial neural networks have been a hot topic in recent years. Artificial neural networks offer many advantages in manufacturing, such as learning and adapting, parallel distributed computation, robustness, and other desirable features. It is expected that neural network technologies will lead to truly intelligent manufacturing systems. This paper discusses the basics of neural networks as well as the current use of neural network technology in manufacturing. Some possible solutions to the problems associated with neural networks are also suggested. These guidelines and references are intended to help in the development and implementation of artificial neural networks in manufacturing.

Personal Finance/Banking

Cheque Deposits through Mobile

The majority of central banks have eliminated the requirement to deliver an unpaid cheque to the bank by allowing customers to deposit cheques using a smartphone app. The technology behind these apps uses Neural Networks to decipher and transform checks’ handwriting into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition.

Fraud Prevention

What can a financial institution do to identify the authenticity of a transaction? In most cases, the volume of daily transactions is far too high to be analysed manually. To assist with the task, one uses Artificial Intelligence to develop systems that can learn by training which types are fake (speak learning or speak to Neural Networks!)

FICO, the company which creates credit scores that serve to evaluate the creditworthiness of a person uses neural networks that drive their Artificial Intelligence to predict fraudulent transactions. Factors that impact the final output are the frequency and magnitude of the trade and the type of retailer involved.

How to Power Your Mobile Phones

One of the most popular characteristics of today’s smartphones is the conversion of voice to text. Pressing a button or saying a specific word (“Ok, Google,” for instance) will allow you to begin talking to your phone. Then your cell phone transforms the voice into text. Google uses artificial neural networks connected in a recurrent connection to enable the voice search. Microsoft claims that it has developed a speech recognition system of Neural Networks that can translate conversations with a little more accuracy than human beings.

Smart Personal Assistants

The technology of voice-to-text is becoming reliable enough that can rely on for simple conversations. It is now becoming the interface to control the modern generation of digital personal assistants. In the beginning, there were simpler telephone assistants, such as Siri or Google Now (now succeeded by the more advanced Google Assistant), which could conduct internet searches, schedule reminders, and even integrate into your schedule. Amazon further developed this concept with the launch of two additional devices and components, namely Alexa and Echo (later, Dot).

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