A brief review of A​rtificial neural networks (ANNs)

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A​rtificial neural networks are the basis of Artificial Intelligence (AI) to solve logical problems that are difficult for humans or statistical standards. It has self-learning capabilities like a human child that help it to provide more effective results. ​This article discusses a​rtificial neural networks with applications, their importance, and market value.

What is an Artificial Neural Network (ANN)?

Artificial Neural Networks (ANN) is a computational model inspired by structures and functions of mathematical models of biological neural networks; it is an information processing system with a collection of connected units (neurons).

ANNs are used for many applications, including image and speech recognition, Stock Market Prediction, machine translation, Aerospace analysis, medical diagnosis, Signature Verification, and Handwriting Analysis. ANNs are used in all sectors of the contemporary world with life-changing applications.

Warren McCulloch and Walter Pitts opened the subject of Artificial Neural Networks in 1943, creating a computational model for neural networks [1]. ​ Ivakhnenko and Lapa published the first functional networks with many layers in 1965 [2].

“There must be a trick to the train of thought, a recursive formula. A group of neurons starts working automatically, sometimes without external impulse. It is a kind of iterative process with a growing pattern. It wanders about in the brain, and the way it happens must depend on the memory of similar patterns.” ― Stanislaw M. Ulam, Adventures of a Mathematician

Why are Artificial Neural Networks Important?

An Artificial Neural Network is usually integrated into layers, able to solve more complex problems with the help of detection from observational data. ​Artificial Neural Networks’ purpose is to provide and process information like the human brain, and they can help computers make intelligent decisions with limited human assistance.

ANN can learn and model relationships between nonlinear and complex input and output data; this creates an adaptive system that computers use to learn from their mistakes and continuously improve. The ANNs have been applied in all the areas of operations, such as – Email service, asset management, credit rating, e-commerce services, chatbots, deep learning, etc.

“When we get business networks to function similarly to neural networks and mycelium networks, we’ll have a better world.” ― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth

“I’m not suggesting that neural networks are easy. You need to be an expert to make these things work. But that expertise serves you across a broader spectrum of applications. In a sense, all of the effort that previously went into feature design now goes into architecture design and loss function design and optimization scheme design. The manual labor has been raised to a higher level of abstraction.” ― Stefano Soatto

How are growing global Artificial Neural Networks markets?

According to Allied Market Research, the global Artificial Neural Networks market value was USD 14.35 billion in 2020. It is expected to reach USD 152.61 billion by 2030, registering a compound annual growth rate of 26.7% from 2021 to 2030 [3].

“Neural Network Market by Component (Software and Services) and Industry Vertical (Banking, Financial Services, and Insurance (BFSI), IT & Telecom, Aerospace & Defense, Public Sector, Retail, Healthcare, Manufacturing, Energy & Utilities, and Others): Global Opportunity Analysis and Industry Forecast, 2021-2030” — Allied Market Research

TechGolly editorial team led by Al Mahmud Al Mamun. He worked as an Editor-in-Chief at a world-leading professional research Magazine. Rasel Hossain and Enamul Kabir are supporting as Managing Editor. Our team is intercorporate with technologists, researchers, and technology writers. We have substantial knowledge and background in Information Technology (IT), Artificial Intelligence (AI), and Embedded Technology.

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