Artificial Neural Network (ANN) is an algorithm that processes and analyzes data like the human brain. The term “Neural” refers to how the algorithm is inspired by our central nervous system and modeled to mimic human learning methods. Artificial Neural Networks is similarly structured like that of an animal brain. It consists of multiple layers of connected units interwoven with non-linear activation functions. These units are called Artificial Neurons. The ANNs are random algorithms. Hence, they may train various models every time they run with the same data, resulting in varying results.
Applications of Artificial Neural Networks
An Artificial Neural Network is an interconnected network of artificial nodes or neurons that process and transmit data like the human brain. ANN is indispensable in Artificial Intelligence, enabling machines to process data and make decisions based on it.
ANN is used in:
1. Speech and image recognition
ANN trains networks to recognize and categorize images and sounds using a larger audio and visual data dataset.
2. Fraud detection
Using historical fraud data, networks learn to recognize fraudulence patterns, enabling more accurate and reliable fraud detection as more data is added.
3. Predictive modeling
ANNs use historical data to train networks to identify trends and patterns and make near-accurate predictions about what will happen in the future or how people will behave.
4. Handwriting recognition
ANN algorithms are trained to recognize handwritten characters such as alphabets and numbers.
5. Signature verification
Artificial neural networks can be trained to identify if signatures are genuine or forged during the verification process.
6. Face recognition
Using a person’s image, ANN compares the facial features with an existing database of faces to find a match that identifies the person. The pre-existing database is a collection of processed images that are used to train neural networks for face recognition.
7. Medical diagnosis
In the medical field, ANNs help doctors diagnose diseases and understand complex clinical information across multiple medical applications like electronic signal analysis, radiology, detection of cancer cells, and analysis of scans and x-rays.
The overall purpose of Artificial Neural Networks is to combine multiple ML algorithms to process large amounts of data. Even in the absence of specific instructions, these neural networks can learn to perform tasks by observing examples.
