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Artificial neural networks (ANNs), also known as neural networks (NNs), are a type of computing system. They are similar to biological neural networks found in animal brains. The artificial neural network used for analyzing the human brain and gathering more information. Let us check everything that what artificial neural network used for right here in this article!
What Is Artificial Neural Network (ANN)?
Artificial neural network (ANN) is a type of computer programming design which pretends the way a human brain studies and process information and data.
Moreover, this is the main element of artificial intelligence (AI). You will find the artificial neural network to solve problems quickly. It possibly accomplishes tasks that are impossible or difficult for people. Also, ANN can self-learn when more data is provided and does deliver better results.
ANN is a group of nodes (connecting units) is called artificial neurons, and they are like the nerve cell in the biological brain.
Important Takeaways
The artificial neural network (ANN) is an element of AI that simulates the human brain’s functions.
– ANNs make up the processing units and it consists of inputs and outputs. Inputs are the things that the ANN learned from the data, and the output is the result.
– There are rules on how an ANN should learn things, and it is known as backpropagation.
– Artificial neural network used for a lot of things such as education, personal communication, finance, industry, and many more.
Understanding the Artificial Neural Network (ANN)
Neuron Nodes
ANN is made similarly to the human brain, which has neuron nodes that are all interconnected like a web. You will find a human has billions of cells neurons. And the cell body makes each one of them.
These are responsible for gathering information (inputs) and gets outputs from the brain. The neurons act the same way in ANN.
Processing Units
ANN has thousands of neurons which are called processing units, and the nodes interconnect them. Output and input units also make up processing units.
Moreover, the input units of artificial neural network used for the classification of various information and data, which are based on the internal weighting system.
They develop the data one by one and learn by comparing the data classification (that is mainly arbitrary) by knowing the actual classifications of the data.
There might be some errors in the initial stage since they are doing classification/clustering all of this information. However, ANN can easily solve this.
The ANNs try to learn more about specific data that they need to produce an output constantly.
Like us, who needs rules and information to develop the results (output), an ANN also needs rules. This set of rules are called backpropagation, an acronym for backward propagation of error.
Training Phase
Firstly, an ANN goes through a training process. Here it learns to identify different types of data, whether by text, visually, or aurally.
Moreover, during this phase, the network will compare the actual output with the result that it has produced. Then the difference is adjusted by using backpropagation.
Thus, the network will work backward to correct the errors. That means it is going from output to input to adjust all the faults to produce a minimum possible error.
Also, the ANN is trained on what will be the specific outcome of the particular data. This is done using the question and answer (yes/no type) method with binary numbers.
Example
A company wants to detect their financial fraud, and they may have put four questions:
- Does a person do the transaction from a different country?
- Is the site they used is affiliated with any of the companies?
- Did they take more than Rs. 10 lakhs?
- Is the person’s name registered in our company?
Let us assume the answer is: Yes, Yes, No, No. Then the binary format will be 1 1 0 0. The network’s actual output might be 1 0 0 1. But it will adjust the result until the outcome is 1 1 0 0.
After a lot of training processes, the ANN will make fewer errors.
Artificial Neural Network Used for Practical Applications
ANNs are a life-changing application that can be used in all areas of the economy. Moreover, Artificial intelligence (AI) manufactured on the artificial neural network is disrupting all the outdated methods of doing things.
AI can perform all sorts of tasks and makes the services accessible at a minimum cost. They can be the virtual assistant who will order for you online or can translate different posts in different languages, or with the help of chatbots, they solve the problems. ANNs can be applied in all areas.
Few industries that use the artificial neural network (ANN)
– For emails, it can detect spam emails and will delete those emails from the inbox.
– To know about the company’s stock and what direction they should take, asset managers, use AAN.
– Deep learning algorithms use it to forecast the possibility of the event.
– For language processing, chatbots are also developed with ANNs.
– Most of the e-commerce sectors use ANN so that it can personalize recommendations for each user.
– To improve the credit scoring techniques, credit rating businesses also use them.
The list of the usage of ANN has been across multiple industries, businesses, countries, and sectors.
Pros of Artificial Neural Network (ANN)
Parallel processing skill
What is the artificial neural network used for? Well, it is used to perform multiple tasks at the same time. It can do more than one job at a time.
Storing information on the whole network
All the data and information used for the traditional programs are stored on the entire network and not only on the database.
Moreover, the loss of few pieces of information does not affect the ANN from working.
Ability to work with partial information
After the ANN is trained, it can give the perfect outcome even with insufficient data. However, it also depends on how much data the ANN has. If the loss of data is too much, then you won’t get the desired result.
Memory distribution
For the artificial neural network to learn about everything, it is vital to regulate the samples and encourage them to put the desired results by demonstrating these samples to the ANN.
The success of the network is directly proportional to the particular samples. But if the inputs cannot appear to the ANN, then it will produce wrong outputs.
Fault tolerance
Some cells might not be working correctly in the ANN. However, it can still produce the desired results. Thus, even if it has some problems, artificial neural networks are used for many things.
Slow corruption
Every machine slows down over time, and ANN does go under relative degradation. However, ANN’s problem doesn’t destroy instantly.
Capability to do machine learning
ANN learns actions and makes choices by mentioning similar actions.
Cons of Artificial Neural Network (ANN)
Assurance of suitable network structure
Determining the structure of an ANN is difficult as there are no set guidelines. Moreover, to have the appropriate structure, the ANN goes through a lot of experiences.
Unknown behavior of the ANN
It is probably a vital issue of the network. When the artificial neural network is used to produce a solution, the ANN does not provide how and why the desired result formed. Thus, it reduces trust in the ANN.
Hardware dependence
All ANN needs a processor that has similar processing power, like their structure. Hence the recognition of the device is dependent.
The difficulty of presenting the problem to the ANN
An artificial neural network can work with when that data is in binary form. Hence all the difficulties have to be transformed into numerical before they are introduced to the network.
You will find the performance mechanism to determine its effectiveness directly by the performance of the ANN. It depends on the user’s skills.
The duration of the ANN is unknown
The ANN is reduced to an exact rate of errors. Hence this value sometimes does no give the best results.
Artificial Neural Network Example
A variety of ANN depends upon the biological brain neuron and the functions, and an ANN completes tasks in the same way.
Moreover, most of them have few similarities with the complex biological partner, and they are very effective at their tasks. Here are few examples of ANN:
Feedback ANN
These ANN the output will return to the network to achieve the best results internally. Moreover, the feedback ANN feeds data back to itself, and they are most suitable to resolve optimization issues.
Feed-Forward ANN
These are the essential ANN comprising one layer of neurons, an input layer, and an output layer.
They can calculate the output by reviewing the inputs. Also, you will observe the intensity of the ANN on the behavior of the related neurons. And this way, the output will be decided.
The best part of these ANN is that they can determine how to identify and evaluate the input forms.
Multilayer Perceptron
This ANN has a minimum of 3 layers. They are used for the agreement of information and will not be linearly divided. Also, this type of ANN is fully linked because each node in every layer is lined with the nodes of the next layer.
They use a nonlinear simulation function. You will find the multilayer perceptron ANN in use for appliance conversion and for language recognition technologies.
Convolutional Neural Network
This is an alternative form of the multilayer perceptron. They contain mainly one layer (sometimes more than that) that might be united or completely interconnected.
Convolutional neural networks are suitable for semantic parsing, signal processing, image classification, and paraphrase detection.
Recurrent Neural Network (RNN) or Long Short-Term Memory
These are the type of ANN where one of the layer’s output is saved, and after that, it is sent back to input units. Thus, it helps in expecting the result of a layer. Also, the formation in one layer will be the same as they are in the feedforward network.
The RNN activates with the front propagation. However, it will remember all the data that may be needed for future use.
You will find them to mainly use it for text-to-speech translation technology.
Artificial Neural Network Used for Pattern Recognition
Pattern recognition is a method of discovering similarities and parallels facts of machine learning data.
Moreover, the artificial neural network used for pattern recognition classification/clustering all of these similarities will be found in the historical data, statistical analysis, or the knowledge gained by the machine itself.
It is a constancy in the world and/or in abstract concepts. For instance, for sports discussion, the type of the sports will be the pattern. If someone is watching videos related to football, then YouTube wouldn’t suggest videos related to cricket.
Examples
Speaker identification, automatic medical diagnosis, multimedia document recognition (MDR), and Speech recognition.
Steps before searching a pattern
Before looking for patterns, few steps need to be taken. The first one being is to collect information from earth. Now, this information is needed to be pre-processed and filtered so that the ANN can take the features from the information.
Now, based on the type of information, the system will select the algorithm among regression, regression to identify the pattern, and classification.
Classification:
Here the algorithm allocates labels to create information from the predefined features. It is an example of supervised learning.
Clustering:
The algorithm will split the info into a lot of clusters which is similar to the features. It is an example of unsupervised learning.
Regression:
This algorithm finds the relation between the variable quantities and then predicts unknown dependent variable quantities based on the data. It is an example of supervised learning.
Classification of Pattern Recognition
- The accuracy and speed are high for familiar things.
- Can identify any unfamiliar substances.
- it also has the skill to identify different outlines and things from all perspectives.
- Moreover, it can also recognize the substances and objects even if they are partially hidden.
- During the examination, it swiftly knows the patterns and objects with automaticity.
Final Thoughts
People use the artificial neural network for a lot of things. It can solve various kinds of problems that are not humanly possible for us to do or will take human beings a lot of time.
