Artificial Neural Networks 101: The Business Applications of ANN
The Business Applications of Artificial Neural Networks
Today, companies worldwide can offer their customers state-of-the-art goods and services that would have been unimaginable even a few decades ago because of the widespread adoption of AI and ML.
Nowadays, artificial intelligence (AI) and machine learning algorithms are used in almost every facet of business, from the largest corporations to small retailers specializing in hard-to-find items.
Because of their unique architecture and comprehensive set of features, artificial neural networks (ANNs) have been instrumental in developing a wide range of game-changing technologies and services.
According to the findings of recent studies, the use of artificial neural networks in business has seen a phenomenal growth of 270% over the last few years.
Face recognition programs, stock market forecasting, weather prediction, healthcare, and signature verification/handwriting recognition are all examples of areas where ANNs are employed extensively by professionals today.
In this article, we will explain in detail what an artificial neural network is, how neural networks and deep learning work, and some real-life business applications of neural networks.
What is the Artificial Neural Networks?
First, we need to know what neural networks are. A neural network is an algorithm that can learn itself. An artificial neural network (ANN) is an effort to mimic the human brain's network of neurons so that a computer can acquire knowledge and make decisions with human-level accuracy. ANN simply imitates the way the human brain processes information.
Creating ANNs involves reprogramming traditional computers to mimic the behavior of interconnected neural cells. They are designed to function similarly to how the brain processes information, to the point of being built up of many essential processing pieces linked and stacked on top of one another.
The result is an adaptive system that allows computers to correct their errors and improve over time. Artificial neural networks are an effort to enhance the efficiency with which complex issues such as document summarization and facial recognition can be solved.
Neural Networks and Deep Learning: How They Work
Nodes in a neural network are linked to one another in various ways. The input layer is usually at the very bottom of the network, while the output layer is at the very top. In most cases, the neural network will include many layers.
Most neural networks consist of three different types of neurons: input, hidden, and output.
Likewise, developers use multi-layer neural networks to divide up processing into layers that may operate in parallel.
- The human brain's neurons are a good analogy for the input layer of a neural network.
- Like the synapses in the brain, this hidden layer plays a crucial role in processing information before it is sent to the next layer. Synaptic weight is the amplitude or strength of a link between nodes; artificial neurons in the hidden layer analyze a collection of inputs using this measure.
- The output in the output layer results from applying a transfer function to the weighted input. What you and your customers will see is this.
Check this article out for further details on the value of deep learning to a company.
Why Do We Use Neural Networks?
Neural networks are perfect for extensive data-based systems because they can act like people and solve many problems.
Since ANNs can make sense of unclear, conflicting, or incomplete information, they can use controlled procedures when no exact models are available.
Statista says that global data volumes reached almost 100,000 petabytes each month in 2017 and are expected to reach 181 zettabytes by 2025.
Organizations, people, and devices generate a massive amount of data, which neural networks can now use to find meaning.
- Changed Learning
Neural networks, like people, have complex and non-linear interactions and build on what they already know. Software, for example, can teach kids math and language arts through adaptive learning.
- Big Data Analysis
Neural networks can help a lot when it comes to analyzing big data sets. Neural networks use algorithms to find patterns and relationships between variables that might not be obvious with other tools for analyzing data.
Neural networks are perfect for dealing with complex visual problems in medical imaging because they can group and sort a lot of data. People often have trouble understanding these images, but a neural network can learn to automatically group the different body parts.
- Operating in Real-Time
Neural networks can sometimes give solutions in real-time, as can be seen with self-driving cars and drones that can find their way around.
- Pattern Recognition
Neural networks are very good at recognizing faces because they can learn to recognize patterns in facial features and expressions. Because of this, they are perfect for security systems that need to look at the live video footage.
What Real-Life Business Applications Do Neural Networks Have?
Many forms of data, such as pictures, videos, documents, and databases, can be evaluated using neural networks. They can also take any information without needing to be specifically programmed to understand it.
Since neural networks have a broad, generic view of addressing problems, their potential uses are almost boundless. Today, neural networks are widely used in fields as diverse as image/pattern recognition, self-driving car trajectory prediction, face identification, data mining, email spam filtering, medical diagnostics, and cancer research. These days, neural networks can be used in various situations, and their widespread acceptance directly results.
Neural networks are used everywhere, from marketing and social media to finance. Let's look at some real-world examples of how ANN can be used in business:
Neural Networks in Law and Order
Facial recognition systems are growing in popularity as a powerful monitoring method, despite legal restrictions on their usage in certain areas. These methods include comparing digital photographs of people's faces to an extensive database. This neural networking service helps facilitate selective admission to secure facilities, such as an office, and for notifying authorities of the presence of fugitives and enforcing mask regulations.
The most popular kind of neural network for this task is the convolutional kind since it is well-suited to handling images. The database stores many photos that are later analyzed during the learning process. Practical assessments are guaranteed by using sample layers in the neural network. In doing so, the models are optimized, and reliable outcomes are ensured.
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Neural Networks in Marketing and eCommerce
Using large datasets for neural network training is a cutting-edge area of data science. While the technology behind this has been known for decades, it was only with the advent of Big Data that its true marketing potential became apparent.
Most applications of ANNs can be found in predictive analytics. The use of neural networks in business can help marketers predict the success of campaigns by identifying trends in the outcomes of previous campaigns.
For instance, eCommerce sites like Amazon and AliExpress employ AI to display relevant and suggested items to customers. Data is compiled based on how users interact with the system. The system uses an algorithm to determine which things are most like the ones you've searched for. Alternatively, it can identify users' tastes and interests and display relevant products.
The following are some of how marketers can profit from the usage of technologies to discover and contact clients more effectively:
- Predictions of Consumer Behavior
- Learning how to identify and cater to increasingly nuanced consumer subsets
- Incorporating marketing automation.
- Content generation
Neural Networks in Finance
Risks in the financial markets were far more difficult to foresee in the past. This is no longer the case due to neural networks, which have significantly reduced the historically high volatility of financial markets. Financial executives use multi-layer perceptron neural networks for precise, real-time stock market forecasting. These methods draw on stock prices, loss-to-income ratios, and annualized returns from the past to arrive at the correct conclusions.
Neural Networks in Social Media
Social media permeates almost every aspect of human existence. Many social media users are impressed by the platform's apparent ability to "read their thoughts," but this is the work of neural networks.
An everyday use case for neural network resources is analyzing user behavior. Neural networks process and evaluate vast amounts of user-generated material at an unprecedented velocity. All user actions inside the app will be analyzed to extract the most relevant data. With this data, advertisers can provide highly relevant messages to users based on their interests, preferences, and previous purchases.
Neural Networks in Security
The field of security and safety is also beginning to use neural networks.
This means that they can be put to use in:
- Preventing fraud
- Detecting and containing malware and viruses
- Prioritize notifications for relevant people.
- Detecting spam
- Content moderation
Content moderation is an example of how neural networks are used in business security.
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As we've seen, neural networks are systems or hardware meant to imitate the functioning of human neurons. In addition, we conducted research to determine the function of neural networks and their practical use in the business world.
Neural networks and deep learning are the future of every industry, as you can see. These technological advancements facilitate decision-making, workflow automation, fraud detection, and other crucial activities. And they will constantly be evolving.