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Neural Networks and its Applications

Neural networks are connections between the neurons in the brain, which are responsible for memory, cognition, learning, and intellectual activities.

These neurons receive sensory input and form connections between them, which become stronger with the inputs received over time. These are called human neural networks and which form the basis for Artificial Neural Networks on computers.

Artificial Neural Networks contain artificial neurons, which are called units. Artificial Neural Networks (ANN) replicate this functioning of the human brain to allow computers to analyze data or do complex mathematical calculations.

This technology has a myriad range of applications, from facial recognition to aerospace. In this blog, we will discuss what neural networks are and its applications…

What are Artificial Neural Networks?

Artificial Neural Networks (ANN) are biologically inspired simulations on computers to perform tasks like clustering, pattern recognition, and classification.

An ANN is a collection of connected units, called nodes, which are artificial neurons. These units are very similar to the neurons of the human brain. Every node has a set of inputs, weights, and a bias value.

Source: bianoti.com

Types of Artificial Neural Networks

      Feedforward Neural Network (FNN)

Feedforward Neural Network is the most basic form of Artificial Neural Network in which the data/input travels in a single direction. 

The data enters the ANN through the input layer and exits through the output layer.

Source: stackoverflow.com

      Recurrent Neural Network (RNN)

RNN saves the output of a layer and feeds it back to the input to better predict the outcome of the layer. 

The first layer is similar to the Feedforward Neural Network, and RNN starts once the output of the first layer is computed.

      Convolutional Neural Network(CNN)

A Convolutional Neural Network (CNN) is somewhat similar to a feed-forward neural network. In CNN, the connection between units has weights that determine the influence of one unit on another unit. It has applications in speech processing and computer vision.

Source: kdnuggets.com

      Modular Neural Network

A Modular Neural Network comprises a collection of different neural networks that work independently. The output is achieved without any interaction between them. Each neural network performs a sub-task by obtaining unique inputs as compared to other networks.

      Single Layer or Multilayer Perceptron (MLP)

Neural Networks with two input units, one output unit, and no hidden layers are called single-layer perceptrons. Multilayer Perceptron have more than one hidden layer of neurons, unlike Single Layer Perceptron, which has no hidden layers.

Multilayer Perceptron is also known as deep Feedforward neural networks.

Source: github.com/PetarV-/TikZ

Applications of Neural Networks

1.    Facial Recognition

This application is mostly used for surveillance at public places such as shopping malls, airports and similar. Digital images are compared with human faces by recognition systems. It can also be used for selective entry at offices.

This is done by Convolutional Neural Networks (CNN), which have a database of a large number of pictures. These pictures are further processed for training a neural network. 

Source: bbc.com

 2. Aerospace  

Neural Networks in aerospace engineering are used to model key dynamic simulations, secure control systems, auto-piloting, and diagnose faults. 

As passenger safety is of utmost importance in aircraft, neural network systems built algorithms ensure the accuracy of autopilot. 

3. Healthcare

Convolutional Neural Networks (CNN) are actively employed in the healthcare industry for X-Ray detection, Ultrasound, and CT scans. Medical imaging data retrieved from these tests are analyzed and assessed based on neural network models.

Recurrent Neural Networks (RNN) are being used for the development of voice recognition systems. Generative Neural Networks are used to match the different categories of drugs, which is needed for drug discovery.

Source: sanet.st

4. Sales & Marketing

AI and neural networks are also used in marketing and sales industries. You would have noticed that when you open up an eCommerce site, they suggest products based on your purchases & previous searches. Similarly, cloud kitchens give recommendations based on previous orders.

This is true across all segments, like movies, hospitality, and online bookstores. This is called personalized marketing and is done using artificial neural networks. These networks identify individuals’ likes, dislikes, and purchase histories.

5. Personal Assistants

You would have heard about personal assistants like Alexa, Siri, Cortana, etc. These assistants make use of speech recognition, Natural Language Processing to respond to users’ queries. Natural Language Processing uses artificial neural networks to understand human language, accent, and context. 

6. Stock Market

Predictions in the stock market were difficult to make before Artificial Neural Networks came into existence. Stock Market predictions are made using feedforward Artificial Intelligence algorithms and neural networks. 

Neural networks have shown success in successfully predicting market trends.

Source: hxlpartners.com

7. Weather Forecasting

Weather forecasts made by the Meteorological Department were never so accurate until Artificial Intelligence & neural networks came into the picture.

Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are the neural networks that are used for weather forecasting.

Using Artificial Neural Networks(ANN), climatic conditions can be predicted 15 days in advance. Parameters analyzed include air temperature, wind speed, solar radiation, and relative humidity.

8. Social Media

Artificial Neural Networks(ANN) are used to analyze the behavior of social media users. This is done using Data Collection and Analysis. The findings can be used for promoting products according to their likes and dislikes.  

Multilayer Perceptron(MLP) is used for this application along with different training models, which include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). 

Source: medium.com

Wrapping Up…

Neural networks function in a way that is very similar to the human brain. As the brain becomes more intelligent as it gathers information, Artificial Neural Networks also perform better over time. 

As we have seen, Neural networks have a wide range of applications. Artificial Neural Networks have simplified traditional algorithms and made Sci-Fi movies closer to reality.

For more information about Neural Networks and its applications, get in touch with us at Zesium.

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Victor Ortiz is a Content Marketer with GoodFirms. He likes to read & blog on technology-related topics and is passionate about traveling, exploring new places, and listening to music.