What You Need to Know About Artificial Neural Networks

What You Need to Know About Artificial Neural Networks

Without a doubt, nature is the best engineer and developer ever. That’s why scientists often incorporate the principles and structures of natural objects in their inventions. One such example is neural networks. They might make decisions based on prior experience rather than just following prescribed algorithms and formulas. Modern cutting-edge technology relies heavily on neural networks. According to statistics, over the past few years, the use of artificial neural networks in business has increased by 270%.

What is Artificial Neural Network (ANN)?

An artificial neural network (ANN), also known as a neural network (NN), is an attempt to replicate the network of neurons in the brain so that a computer can learn and make decisions like a human. They are computational models, or what is commonly referred to as algorithms. These NNs are created by programming conventional computers to mimic the behavior of nerve cells. They copy some aspects of the physical structure and information processing of the brain by being composed of numerous, connected, and layered simple processing elements.

Artificial neural networks are built on top of artificial neurons. ANNs are composed of nodes and connections between nodes, much like biological neural networks, which contain neurons and synapses. As the ANN processes a lot of data, it develops new connections and gains the ability to handle challenging tasks or solve challenging problems.

The history of ANN goes back to the mid-50s. In 1958, Frank Rosenblatt, an American psychologist, created one of the first prototype models of an artificial neural network called Perceptron. It was developed to mimic how the human brain processes information and acquired the ability to recognize different objects.

How Do Artificial Neural Networks Work and Learn?

Animal neural networks served as a model for artificial neural networks (ANNs), which can “learn” and develop to solve issues like pattern recognition. A mathematical function known as the artificial neuron simulates biological neurons in some ways.

The artificial neurons take in information and use it to generate output or data. Similar input and output signals are present in biological neurons. However, ANN connects each artificial neuron to form the artificial neural network using mathematical equations.

Additionally, scientists are exploring various approaches to integrating biological communication components into artificial neural networks. In 2022 researchers created an artificial neuron that closely resembles a biological neural network and can communicate in a variety of ways, including chemically and through electric pulses.

Why Is ANN So Useful?

Neural networks are the best choice for today’s big data-based systems because of their ability to mimic human behavior and work with massive amounts of data generated by businesses, people, and devices.

Neural networks’ capabilities include the following:

  • Recognition of patterns: By learning to recognize patterns in facial features and expressions, neural networks excel at the challenge of identifying faces. This ability qualifies them for applications such as security systems that require the analysis of live video footage.
  • Real-time operation: As with self-driving cars and drone navigation, neural networks can provide much-needed real-time responses.
  • Adaptive learning: Like humans, neural networks represent non-linear and complicated interactions while also building on prior knowledge. Adaptive learning is used in software, for example, to teach children math and languages.
  • Forecasting: The ability of ANNs to predict based on a model allows it to be applied to a wide range of services, including, but not limited to, weather and traffic forecasts.
  • High fault tolerance: Because neural networks are fault-tolerant, they can continue to function even if one or more nodes fail. When significant portions of a network are lost or missing, neural networks can fill in the gaps.

The real-life examples of using ANN.

Neural networks have a wide range of business applications and have assisted companies in automating processes that were previously carried out manually.

Marketing and eCommerce 

Big Data has become increasingly important as a marketing tool. Marketers can find and reach customers more successfully by utilizing sophisticated tools and data-driven insights.

Neural networks can help them in:

  • consumer behavior predictions
  • content creation
  • developing the more complex buyer segments
  • automating marketing

The customization of product recommendations on e-commerce websites like Amazon is an illustration of applying ANN for marketing purposes. To provide a more thorough marketing strategy, the system examines the user’s previous behavior and purchases for more suitable recommendations, sales, etc.


Retail and sales companies can use neural networks for:

  • demand forecasting that identifies when a product or service will be needed by customers and shows how to deliver goods on schedule and at the right time.
  • forecasting for sales helps to determine when and what a customer is most likely to purchase. As a result, businesses are better able to recognize and comprehend the elements that boost retail sales and forecast future sales numbers.
  • retailers’ required inventory levels can be determined by an ANN-powered system. They can therefore focus on increasing their profits.

Finance and Banking 

Neural networks can also be applied to automate processes in banking and finance, for:

  • business failure predictions
  • debt risk assessment
  • approval of loans and mortgages
  • fraud detection

A neural network that Citibank has already set up is intended to find credit card fraud. Millions of consumer transactions from a large database were used to train this neural network.


Neural networks are also useful for security purposes. For instance. they can help in:

  • fraud, malware, and virus detection and prevention
  • spam detection
  • content moderation
  • DDoS attacks prevention etc


Insurance is another sector that makes use of the benefits offered by NNs. Neural networks are used by insurance companies to forecast future loss ratios and modify premiums. Their profit margin consequently rises as a result. Allstate offers a concrete illustration of how neural networks are being applied to the insurance industry. To identify “accident-prone” drivers and assign them a fair rate, they use neural networks.


From packaging to shipping, neural networks are being used in logistics to:

  • routing to help determine the best route for a truck driver
  • identify defects in the production line
  • balance out an assembly line by assigning jobs to workers based on their skill sets.

Wise Systems is an illustration of how neural networks are used in logistics. This autonomous system enables users to create custom real-time shipping routes using predictive functionalities, as well as plan and monitor routes.

Artificial neural networks operate in the same way that biological neural networks do. These networks produce incredibly accurate predictions thanks to data training. Neural networks may be beneficial to a variety of companies and can be viewed as the future of business.

If you are in the market for help with your IT, cloud, and cybersecurity needs, reach out to Klik Solutions and get started with amazing support!

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