NEURAL NETWORK AND ITS WORKING AND INDUSTRY USECASES

Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

How does Neural Networks work?

Artificial Neural Networks can be best viewed as weighted directed graphs, where the nodes are formed by the artificial neurons and the connection between the neuron outputs and neuron inputs can be represented by the directed edges with weights. The Artificial Neural Network receives the input signal from the external world in the form of a pattern and image in the form of a vector. These inputs are then mathematically designated by the notations x(n) for every n number of inputs.

Each of the input is then multiplied by its corresponding weights (these weights are the details used by the artificial neural networks to solve a certain problem). In general terms, these weights typically represent the strength of the interconnection amongst neurons inside the artificial neural network. All the weighted inputs are summed up inside the computing unit (yet another artificial neuron).

If the weighted sum equates to zero, a bias is added to make the output non-zero or else to scale up to the system’s response. Bias has the weight and the input to it is always equal to 1. Here the sum of weighted inputs can be in the range of 0 to positive infinity. To keep the response in the limits of the desired value, a certain threshold value is benchmarked. And then the sum of weighted inputs is passed through the activation function.

The activation function, in general, is the set of transfer functions used to get the desired output of it. There are various flavors of the activation function, but mainly either linear or non-linear sets of functions. Some of the most commonly used set of activation functions are the Binary, Sigmoidal (linear) and Tan hyperbolic sigmoidal (non-linear) activation functions. Now let us take a look at each of them, to certain detail:

Binary:

The output of the binary activation function is either a 0 or a 1. To attain this, there is a threshold value set up. If the net weighted input of the neuron is greater than 1 then the final output of the activation function is returned as 1 or else the output is returned as 0.

Sigmoidal Hyperbolic:

The Sigmoidal Hyperbola function in general terms is an ‘S’ shaped curve. Here tan hyperbolic function is used to approximate output from the actual net input. The function is thus defined as:

The Architecture of Artificial Neural Networks

To understand the architecture of an artificial neural network, we need to understand what a typical neural network contains. In order to describe a typical neural network, it contains a large number of artificial neurons (of course, yes, that is why it is called an artificial neural network) which are termed units arranged in a series of layers. Let us take a look at the different kinds of layers available in an artificial neural network:

Input layer:

The Input layers contain those artificial neurons (termed as units) which are to receive input from the outside world. This is where the actual learning on the network happens, or recognition happens else it will process.

Output layer:

The output layers contain units that respond to the information that is fed into the system and also whether it learned any task or not.

Hidden layer:

The hidden layers are mentioned hidden in between input layers and the output layers. The only job of a hidden layer is to transform the input into something meaningful that the output layer/unit can use in some way.

Most of the artificial neural networks are all interconnected, which means that each of the hidden layers is individually connected to the neurons in its input layer and also to its output layer leaving nothing to hang in the air. This makes it possible for a complete learning process and also learning occurs to the maximum when the weights inside the artificial neural network get updated after each iteration.

Tasks Neural Networks Perform

Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

• Classification: NNs organize patterns or datasets into predefined classes.
• Prediction: They produce the expected output from given input.
• Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
• Associating: You can train neural networks to "remember" patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics. As neural networks become smarter and faster, we make advances on a daily basis.

Real-World and Industry Application of Neural Networks

Here’s a list of other neural network engineering applications currently in use in various industries:

• Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
• Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
• Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
• Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
• Mechanics: Condition monitoring, systems modeling, and control
• Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
• Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)

Business Applications of Neural Networks:

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

 So, let’s look at some examples of neural network applications in different areas. Mostly, in:

• eCommerce;
• Finance;
• Healthcare;
• Security;
• Logistics.

eCommerce

This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, AliExpress, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.

  Amazon shows Related Products

AliExpress shows the recommended products basing on the items viewed by the user

 As for more complicated applications of neural networks in eCommerce, there is a very interesting startup called PixelDTGAN. This product is developed to help sellers save the budget on photographers’ services. There is no need to organize photo sets as the special algorithm automatically makes the pictures of the clothes worn by models. All is needed to do is to resize the images of the items to 64*64, and get the result.

  Examples of PixelIDTGAN work results

Finance

In this industry, there are neural network applications for fraud detection, management, and forecasting. Let’s look at some samples.A great example of neural network finance applications is SAS Real Time Decision Manager. It helps banks to find solutions for business issues (for instance, whether to give credit to a certain person) analyzing risks and probable profits.

 The screenshot of SAS Real Time Decision Manager

 As for financial forecasting, there are plenty of solutions that predict the exchange rate changes. For example, the startup Finprophet is the software that uses a neural network of deep learning for giving the forecast about a wide range of financial instruments like currencies, cryptocurrencies, stocks, futures.

Finprophet is giving the forecast about Bitcoin - US Dollar currency pair

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible.

IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.

 This is the original version of IBM Watson, which includes 2800 processor cores and 15 terabytes of memory.

 Doctors can use the abilities of IBM Watson with the help of tablets with cloud connection.

Security

Neural networks are widely used for protection from computer viruses, fraud, etc.One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.

  ICSP Neural scanning station 

 One more sample of using AI and ML for security purposes is Shape security which provides several finance solutions.

  The wide range of solutions for defense from fraud by Shape security

Logistics

This industry needs a lot of management that is to be done manually by employees of many companies. But nowadays, neural networks are capable of routing and dispatching.

For example, Wise Systems is an autonomous system which lets a user:

• plan routes and monitor them;
• customize shipping routes in real-time with the help of predictive features.

Screenshot of Wise Systems

 One more solution is FourKites. This is a visibility program that works in a real-time mode. It helps to plan and monitor routes and predict the time of delivery.

 The interface of FourKites on laptop and mobile phone

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

 Here is what Tesla Autopilot sees

This article will give up the breif explanation upon the Neural Network and Its Industry Usecases. Hope it Will Be Beneficiary in terms of Understanding the Neural Network.

Thankyou.