Artificial Neural Network applications display the wide use of Machine Learning techniques. Moreover, it is inspired by the human nervous system and operates the same way as neurons.
Further, ANN leverages brain power to develop and create algorithms. It also processes complex patterns and speculates bottlenecks.
According to a study by Seed Scientific,
- “The amount of data in the world was estimated to be 44 zettabytes at the dawn of 2020.
- By 2025, the amount of data generated each day is expected to reach 463 exabytes globally.
- Google, Facebook, Microsoft, and Amazon store at least 1,200 petabytes of information.
- The world spends almost $1 million per minute on commodities on the Internet.
- Electronic Arts process roughly 50 terabytes of data every day.
- By 2025, there would be 75 billion Internet-of-Things (IoT) devices in the world.
- By 2030, nine out of every ten people aged six and above would be digitally active.”
Further, a report by Markets and Markets states, “The Big Data Market size is projected to grow from USD 162.6 billion in 2021 to 273.4 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 11.0% during the forecast period. The Big Data industry is driven by sharp increase in data volume.”
Therefore, with the increase in data volumes and the Big Data market, Artificial Neural Networks is the answer we are looking for.
Hence, this article will describe the various Artificial Neural Network applications and their future in technology.
Real-world Applications of Artificial Neural Network!
What are Artificial Neural Networks?.
According to Techopedia, “An artificial neuron network (neural network) is a computational model that mimics the way nerve cells work in the human brain. Artificial neural networks (ANNs) use learning algorithms that can independently make adjustments – or learn, in a sense – as they receive new input. This makes them a very effective tool for non-linear statistical data modeling.”
That is to say, ANNs are a suite of integrated nodes, that work as artificial neurons. Moreover, the neural networks in the human brain inspire ANNs. It also mimics the biological using artificial neurons to perform tasks.
Further, uses arbitrary statistical models that complicate the correlation between inputs and outputs. It also adapts and learns from simple data sets. As a result, it helps discover newer patterns in order to execute various tasks.
Artificial Neural Network Learning Techniques:
- Supervised Learning: The learning technique refers to training models using categorized data. That is to say, specific data sets are marked with correct answers and are used to compare standards for other data sets similar to supervising.
- Unsupervised Learning: Further, this model does not depend on supervision and processes uncategorized data. Moreover, it enables models to set their own standards and categorize the data according to requirements.
Key Features of Artificial Neural Networks:
- Firstly, ANNs are mathematical and algorithmic models that execute neural functions similar to a human brain.
- Moreover, it incorporates various cognate components that analyze data using neurons.
- Further, the neurons store and manage information to weigh its interconnections to other neurons.
- The input signals also process components to create connections between categories.
- It also develops the function to learn, remember, and establish standards from the data according to various categories.
How do Artificial Neural Networks work?
ANNs impersonate the biological neural networks of the human brain. Moreover, the brain consists of neurons that signal each other through synapses that build connections. Further, it creates a pattern where the neurons signal other neurons to transfer information.
Similarly, artificial neurons simulate these functions while adding value to the inputs. Therefore these inputs work as training sets that identify and recognize the information. For instance, when an ANN learns to identify an object, it trains itself through multiple sets of data. As a result, it is shown another set of images where it recognizes the object to help users understand how well-trained the network is.
It may also incorrectly recognize the object, in that case, the training sets are adjusted using back-propagation. Here, back-propagation refers to the rectification of the error-based ability of connections in an ANN unit. Hence, developing the ANN to easily recognize the object without errors is continuous.
- Input Layer: An input layer includes neurons that execute the input functions within ANN.
- Hidden Layer: The hidden layers are found between input and output layers. Moreover, the number of hidden layers differs and depends on application requirements.
- Output Layer: The output layer refers to the neurons that use classification and prediction analysis to process the results.
Types of Artificial Neural Networks
Feedforward Neural Network:
Feedforward neural networks are standard ANNs. Moreover, it processes the data and inputs in a single direction. Further, it integrates using the input layer and provides results using the output layer.
Recurrent Neural Network:
Recurrent Neural Network conserves the outputs of layers and enters the output in the input layer to determine the results.
Convolutional Neural Network:
Convolutional Neural Networks are similar to feedforward neural networks. It creates the connections between various units to influence and affect other units. Although a CNN includes one or more convolutional layers it uses it for convolution operations.
Modular Neural Network:
Modular neural networks incorporate various neural networks that function individually to procure results. Moreover, it mitigates interaction between networks to reduce bottlenecks and disintegrate broad and compound processes.
Radial basis function Neural Network:
Radial basis functions run on the distance of a point that focuses on the center. Moreover, these functions have two layers: an input layer and an output layer. Further, the input layer aligns with the hidden layers and analyzes the result with the output layer.
Why do we use Artificial Neural Networks?
Let us learn by understanding the advantages and disadvantages of Artificial Neural Networks:
Advantages of ANNs:
- Firstly, it offers a parallel processing function to simultaneously execute multiple tasks.
- Moreover, it reduces bottlenecks considering the failure of one component does not impact the functions in the network. As a result, it executes functions using other robust elements and reduces downtime.
- Above all, it learns and adapts easily using its experiences and mitigates the need for reprogramming.
Disadvantages of ANNs:
- One of the biggest drawbacks for ANNs is their black box nature. Moreover, it does not provide appropriate reasons to analyze outputs. As a result, diminishes the trust in the ability of a network.
- Moreover, the extent of the development of a network is obscure.
- It also does not ensure the suitable structure of a network. That is to say, there is no conventional structure to analyze inputs.
Here are the Real-World Artificial Neural Network Applications in various Industries
There are multiple industries leveraging various technological advancements to benefit the scope of their business. Therefore, Artificial Neural Network applications describe the way it helps with multiple functions and operations. Hence here are the top applications for ANN:
Social Media is a great example of connectivity and reach of technology that connects humans to the real and virtual world. Moreover, there is a plethora of data from users and their activities that require processing. As a result, ANN offers its solutions to comprehend the potential to analyze the behaviors of users. It also helps with competitive analysis using various data sources from social media sites and their features of interactions.
The healthcare industry is a notorious sector for leveraging technological advancements to further its services. Moreover, the healthcare industry deals with a mount of data on a daily basis considering the increase in patients and their reports. Healthcare professionals and institutions also generate large amounts of data with the increase in specializations. Therefore, ANNs ensure that the data from various sources are easy to analyze and manage. Most importantly, it enables the opportunities to discover new solutions and medications to help patients and professionals.
Financial Management and Predictions:
The financial industry is an umbrella that incorporates various functions that sustain business operations. Therefore, it generates a heavy amount of data that manages real-time functions and future requirements. As a result, businesses can use ANN solutions to gain actionable insights, make predictions from accurate data, and build strategies for growth and improvement.
In light of the recent Pandemic, the eCommerce industry experienced a great change in its approach. Moreover, people are now dependent on various eCommerce websites to buy groceries, clothes, and other essential products. Further, with an increase in competition and requirements websites need to keep a track of their data. Hence, ANN plays an important role in monitoring user preferences and also making recommendations.
Vice-admiral Robert Fitzroy was an English officer of the Royal Navy and a scientist. Moreover, Fitzroy’s contribution as a pioneering meteorologist is the reason why we have weather forecasting. He built a system to gather data and weather information to ensure the safety of sailors and fishermen.
Today, we emulate the same technique that Fitzroy introduced. Although today we make weather predictions on a global level with a huge amount of data. Moreover, with solutions like CNN and RNN, weather forecasting becomes more accurate. Weather channels and institutions use an amalgamation of the two technologies to predict and forecast the weather.
Images, Signals, and Languages:
ANNs have a huge impact and usage in analyzing images and videos. Moreover, artificial neural network applications help in processing images and creating classifications. It also helps in characterizing images and videos. Hence, here are the top artificial neural network applications:
- Facial Recognition
- Character Recognition
- Image Characterization
- Object Identification
- Image Generation
ANN also builds its base on biological neural networks and plays an important role in speech recognition. Moreover, it transforms speech signals and decodes them into various formats. For example, virtual assistants and chatbots use the same prowess to help users. Google Smart home technology, Siri, Alexa, Cortana, etc, are just some of the real-life examples of ANN at work.
Further, ANN often divides its function into various requirements when it comes to languages. For instance, text classification and categorization refer to NLP using ANN solutions to convert various forms of texts and languages. Moreover, language generation and documentation functions include comprehending, paraphrasing, and summarizing documents to create reports.
In conclusion, Artificial Neural Network applications help simplify complex problems to help execute operations. Moreover, businesses benefit from the various capabilities of ANNs and reduce errors that may lead to losses.
You May Also Like to Read:
Machine Learning and Deep Learning – Know the Difference
Foundation Models in AI: A new Trend and the Future
Here are the Top Deep Learning Architectures for Computer Vision