Artificial Intelligence Neural Network

What is Artificial Intelligence?

Artificial intelligence is a computer science field that deals with the building of machines that mimic human being's behavior after programming them to have the simulation of human intelligence. Most machines follow human traits like learning, perception, problem solving, e.t.c. They work on different actions to hit certain goals. There are several branches of artificial intelligence like machine learning, robotics, deep learning, e.t.c.

There are many emerging technologies when dealing with artificial intelligence. There is a lot of research and enthusiasm to improve innovation around the field. Many individuals fear that extensive use of AI will negatively affect society. You can apply AI in different sectors from manufacturing, healthcare, education, e-commerce, social media, military, marketing, e.t.c. 

There are many beliefs that AI may soon outdo human beings. Most AI researchers promise that AI is safe and there is no need to worry about the jobs as it lacks cognitive aspects. It uses different tools like logic, search and optimization, classification, statistics, e.t.c. to make it successful.

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What is a Neural Network?

Neural network is part of machine learning that uses different names and structures by copying the human brain by mimicking how the biological neurons normally work. The neural network consists of neurons that can be organic or artificial. They rely on the input and make better results without worrying about the output criteria. They provide systems where the computer can learn from past mistakes and improve their performance.

The network consists of node layers. There are three types of node layers. The input layer collects the information from the outside world and enters it into the neural network. It is later processed by input nodes that analyze and categorize the data and push it to the next layer. The hidden layer takes the inputs from input and other hidden layers, analyzes and processes the data, and passes it to the next layer. The output layer provides the final result after data processing. It works with single and multiple nodes.

Basic Structure of Artificial Neural  Networks

Artificial neural networks follow a certain architecture. They have layers that act as the main units. They also have an Interconchangend Weight adjustment mechanism. The type of structure you choose determines the results you will get, and they are very important in working with neural networks.
One of the simple structures is the use of input and output layers. Input layers have single units, which has each unit, and the single output also has the units as input. The output layer consists of all the units connected to the input and combines the combination and transfer functions. You can use more than one output unit. When you use more than one unit, you end up with logistic regression or linear depending on the type of transfer function. The weights act as regression coefficients. 

When you add one or other hidden layer to both layers and under their units, the predictability increases, you have to ensure you use the smallest number of hidden layers. It makes the neural network not store all the information from the learning set but also provides the chance of generalizing to avoid cases of overfitting.

There are several ways you can use it to detect overfitting. When the weight can make the overall system get the learning details instead of discovering structures. It is mainly caused by small learning sizes with the model complexity relationship. Whether you work with neural networks or not, the output layer can have many units when working with any classes they have to predict.

Types of Artificial Neural Networks

There are several types of neural networks. Let us look at the details of each artificial neural network. 

1.FeedForward Artificial Neural Networks

It is one of the simplest types, and the inputs or data normally travel in one direction. It has input nodes that accept the data and output nodes that enable it to exit. This type of artificial neural network lacks hidden layers. It mainly focuses on front propagation and ensures no backpropagation by using the activation functions. They are most useful in complicated target classes, like speech recognition and computer vision. They have easy maintenance and a good response towards noise.

2. Recurrent Neural Network

It saves the output of the layers and later feeds it back to the input that will help predict the layer outcome. The first layer formed consists of weights and the total features. After the computation of the weights, the processes start where the neurons remember the details from the previous steps as it moves to the next step. Each neuron behaves like a memory cell, working on different computations. In case of wrong predictions, one can use the error correction to ensure everything is working well. 

3. Conventional Neural Network

You can look at the neuron's weight and understand the biases. It plays an essential role in computer vision by providing better signal and good image processing, making it better than OpenCV. Mostly used in signal processing and other parts of image classification. They are very accurate compared to other artificial neural networks. 

4. Modular Neural Network

They consist of a network collection that works independently and focuses more on the output. Each network has unique sets of inputs when you compare it to other networks that perform the same role of constructing and performing different sub-tasks. There is no interaction between the networks when performing different tasks. They are known for reducing complexity due to breaking large computation processes into smaller parts. It increases the computation speed and does not affect processing time as they rely on the number of neurons and how they are involved in the computing process.

5.  Radial basis function Neural Network 

It normally considers the distance of the point and how it matches with the center. The function normally consists of two layers. The first layer is where the features, together with Radial Basis Function, are found in the inner layer. The other layer is when the output of the features gets some consideration during computing the outputs.  The common application of this network is its usage in  Power Restoration Systems to handle the cases of power outages.

Applications of Artificial Neural Networks

There are many applications of artificial neural networks. Some of the applications include:

• We use them in facial recognition to improve surveillance by comparing digital images with real human faces. Most of the faces are in databases and are commonly used by offices and in crime scenes.
• Social media platforms use neural networks to analyze the user's behavior and use it to make forecasts. It uses methods like Mean Absolute Error, Mean Squared Error, e.t.c, to carry out data mining and analysis of the data.
• We use them in healthcare to conduct X-rays, ultrasound, CT scan, image processing, voice recognition, e.t.c. We normally use Convolutional Neural Networks and Recurrent Neural networks (RNN).
• Used in the airspace to secure the aircraft systems, detect faults, improve performance through auto-piloting,e.t.c. It uses methods like Time Delay Neural Networks to recognize any patterns.
• They are used in signature verification by verifying the identity of the users. Mostly used by financial institutions and banks to avoid fraud forgery. It also improves the authenticity of the signed documents. It also analyses handwriting during forensics to find any variations between several documents.
• Used by meteorological departments to make forecasts, it enables you to see the occurrence of any disasters. It uses Multilayer Perceptron, Recurrent Neural Networks, and Convolutional Neural Network (CNN).
• Countries defense use the networks to improve their defense operations by changing their strategies. The networks help in logistics, object locations, armed attack analysis, controlling the automated drones, air, and maritime patrols, e.t.c. They commonly use Convolutional Neural Networks(CNN).
• Stock market predictions. Many people have invested in stocks, they use the networks to make profits after analyzing the stock prices, returns, and past performances. It makes use of Multilayer Perceptron MLP, which has different node layers.

 Conclusion
For years, there has been a lot of research in the artificial intelligence field. There are many frustrations and improvements with the advancement of neural networks, which have made many changes in different sectors. Most techniques are proven and work well with the systems to provide a better approach. 

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