What is Artificial Neural Network (ANN) and its Applications

In this article, we will try to try to explore and get a in-depth understanding of what exactly artificial neural network is. So, let’s have dive into the topics.

Table of Content

What is Artificial Neural Network?
What is a Neuron?
Characteristics of Artificial Neural Networks
How Artificial Neural Network Learns?
Applications of Artificial Neural Networks
Conclusion

What is Artificial Neural Network?

An Artificial Neural Network (ANN) is a computational model inspired by the structure and function of the human brain. It consists of a large number of “neuron”-like processing elements that are abstract, simplified, and simulated within the network. These neurons are organized into layers, including input, hidden, and output layers. Artificial neural networks can have a large number of hidden layers, enabling them to learn complex patterns and representations from data. These are the layers that are associated with Neural Network.

Input Layer: Accepts raw data
Hidden Layers: Processes and transforms data
Output Layer: Provides the final computed results

Each connection between neurons has an associated weight, which determines the strength of the signal. If a network contains multiple hidden layers, it is referred to as a Deep Neural Network (DNN). ANN systems learn from data by adjusting these weights through training.

What is Neuron?

Neuron is a central component of natural neural network. Neuron takes the input gathered by human senses, process this information and sends executable reactions to muscles. Neuron has three fundamental components.

Dendrites (Input Receivers)
Axon (Signal Transmitter)
Cell body or Soma (Information Processor)

When neuron fires, it exchanges information among each other in the form of electrical signals. Two neurons are connected with each other through axon and form a network. This network is called Natural Neural Network (NNN).

Characteristics of Artificial Neural Networks

1. Feature Extraction

Neural networks process input signals through interconnected neurons.
Information is stored as weighted linkages between neurons, allowing them to extract meaningful patterns.

2. Categorization

The network receives input signals via connections and connecting weights.
It organizes and processes data to classify information efficiently.

3. Association

Neural networks learn by adjusting weights based on input patterns.
They establish associations between inputs and outputs, enabling recognition and recall.

4. Optimization

The system fine-tunes weights to improve accuracy and efficiency.
Neural networks continuously adjust to minimize errors and enhance performance.

5. Generalization

They apply learned knowledge to new, unseen data for predictive modeling.
By learning from data, neural networks can recall and generalize patterns.

How Artificial Neural Network Learns?

ANN learns through an iterative process called training. This involves feeding data into the network, processing it through layers, and adjusting weights to minimize error. Training is achieved using:

Supervised Learning: ANN learns from labeled datasets.
Unsupervised Learning: ANN identifies patterns in unlabeled data.
Reinforcement Learning: ANN improves performance based on feedback from the environment.

For example, if an ANN is trained to recognize patients with pneumonia, it is fed multiple images of pneumonia x-rays during training. The network gradually learns distinctive features without requiring explicit programming. This method is much more efficient than manually coding identification rules.

Applications of Artificial Neural Network

Weather Prediction: ANNs forecast future weather by analyzing historical data. Over time, they can increase the accuracy of weather forecasts by taking into account a variety of factors, such as temperature, humidity, pressure, etc.
Pattern Recognition: ANNs are used to find patterns in data, including text, speech, and images. They discover uses for facial recognition, such as handwriting recognition and mobile screen lock.
Facial Recognition: It functions as a strong surveillance system. Recognition systems compare digital photos with the human face. They are employed for selective entry in offices. As a result, the systems verify a human face and compare it to the list of IDs stored in its database. For instance, mobile devices’ face screen locks, etc.
Recommendation Systems: Recommendation engines on social media sites like Facebook and Instagram, streaming services like Netflix, and e-commerce sites like Flipkart and Amazon are powered by ANNs. To recommend pertinent goods, connections, or content, they examine user behavior and preferences.
Sales and Marketing: ANNs are able to forecast consumer preferences, behavior, and purchasing trends. To increase sales through internet advertisements, they optimize targeting, marketing tactics, and product recommendations.
Healthcare: ANNs help in medical image analysis for tasks like MRI analysis, X-ray interpretation, and tumor diagnosis. They can help with diagnosis, predict the course of a disease, and customize treatment regimens.
Education: Systems for adaptive learning can make use of ANNs. To offer individualized learning resources and suggestions, they examine the behavior and performance of their students.

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Conclusion

Artificial neural networks (ANNs) have transformed AI by allowing robots to learn, adapt, and solve complicated problems effectively. As deep learning advances, ANN applications will spread across industries, influencing the future of technology. If you want to learn more about ANN and AI, consider enrolling in an industry-designed training program that will provide you practical experience with neural networks and deep learning frameworks. If you want to learn more about this, then you should definitely check out our Artificial Intelligence Course.