Applications of Neural Networks

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Neural networks are supporting humans in the current period to endure new age changes in the education, banking, aerospace, and automobile industries. However, before understanding how they are promoting various industries, it is necessary to first grasp the fundamental concepts of neural networks and deep learning.

Deep Learning

Deep learning, which is a subset of artificial intelligence, includes neural networks. A set of algorithms inspired by the human brain is known as neural networks. Artificial neural networks are another name for these systems (ANN). The human brain is made up of sensors, motor neurons, and interneurons. Artificial neurons are used to create a brain model that is nearly identical to that of a human (i.e. a neural network).

Artificial Neural Network (ANN)

An Artificial Neural Network (ANN) is a system made up of interconnected units (nodes). Artificial neurons are these interconnected units. These units have a lot in common with the original neurons in the human brain. Each node is built from a combination of inputs, weights, and a bias value. The hidden layers contain the neural network's weights. Weights and biases are machine learning model learning parameters that are changed during neural network training. 

Applications of Neural Networks

Finance, healthcare, and the automotive industries are all regulated by neural networks. Because these artificial neurons work in a similar fashion to human neurons. They may be used to recognise images, characters, and make stock market predictions. Let's have a look at the various applications of neural networks.

1. Facial Recognition 

Facial Recognition Technologies (FRS) are useful surveillance tools. Recognition systems compare and match human faces with computer pictures. They are used in offices to allow only specific persons in. As a result, the technologies authenticate human faces by comparing them to a database of IDs.

For facial identification and image processing, Convolutional Neural Networks (CNN) are utilised.

For the purpose of training a neural network, a large number of images are supplied into the database. The photos are then further processed for training purposes. For effective evaluations, CNN uses sampling layers. The models are fine-tuned to produce accurate recognition results.

2. Stock Market Prediction

A Multilayer Perceptron MLP (class of feedforward artificial intelligence algorithm) is used to create a successful stock forecast in real time. MLP is made up of numerous layers of nodes, each of which is fully connected to the nodes above it. The MLP model is built using past stock performance, annual returns, and non profit ratios.

3. Social Media 

Artificial Neural Networks are used to study social media user behaviour. Data exchanged daily through virtual interactions is gathered and reviewed for competitive analysis. The activities of social media users are mimicked by neural networks. Individual behaviours can be connected to spending habits once data is evaluated via social media networks. Data from social media applications is analysed using Multilayer Perceptron ANN.

MLP uses several training methods such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) to forecast social media trends (MSE).

MLP considers a variety of parameters, including the user's favourite Instagram pages, bookmarked options, and so on. These variables are utilised as inputs during the MLP model's training.

Artificial neural networks can surely work as the best fit model for user data analysis in the ever-changing dynamics of social media apps.

4.  Healthcare

In the healthcare industry, Convolutional Neural Networks are used for X-ray detection, CT scans, and ultrasound.

The medical imaging data received from the aforementioned tests is evaluated and assessed using neural network models, as CNN is used in image processing.

In the development of voice recognition systems, the recurrent neural network (RNN) is also used.

These days, voice recognition technology are employed to maintain track of the patient's information. Researchers are also using Generative Neural Networks to discover new drugs. Matching diverse kinds of medications is a difficult undertaking, but generative neural networks have simplified the process. They may be utilised to combine various ingredients, which is the basis for medicine development.

5. Weather Forecasting

Prior to the implementation of artificial intelligence, the meteorological department's forecasts were never correct. Weather forecasts are now also utilised to anticipate the likelihood of natural disasters in the modern day. For weather forecasting, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Networks (RNN) are utilised. Traditional ANN multilayer models can also be used to forecast weather 15 days ahead of time. To anticipate air temperatures, a combination of different types of neural network design can be utilised.