Neural Network & Its Use Cases
Neural Network and its Use Cases in Current IT Industry
Greeting Everyone!!! Herewith another blog on Neural Network's information and their current use cases in the IT Industry affected our lifestyle.
We all have used computers, and we have seen how much power they are. Computers are capable of doing many things that a human brain finds difficult or time taking. They are not smart, but they are faster than a human brain. Computers are built and programmed and designed by humans to make the task faster. We can say that computers are just designed and developed to do a certain programmed task. What if computers start working as a human brain? What if the computers can also learn like the human brain does and make or pass the decision based on what they have learned?
The answer to the above questions is “Yes, it is possible.” We have made so much improvement in technology that we can now program computers and let them start working as a human brain. The processor technology is called NEURAL NETWORKS.
How Human Brain Works?
Let me make this more clear. First, let's see how the human brain works. The human brain contains billions of nerve cells called Neurons arranged in patterns that coordinate thought, emotion, behavior, movement, and sensation. A complicated highway system of nerves connects your brain to the rest of your body so that communication can occur in split seconds. The neuron is the brain's basic working unit, a specialized cell designed to transmit information to other nerve cells, muscle, or gland cells. Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells.
Guess this much Biology is enough to understand that neurons are the cells that help us learn and understand what we see, feel, touch, smell, taste. This is why we have been learning everything from our childhood, and till now, we are learning. When we learn about something for the first time, I might be confused to decide or interpret it, but when our brain(neurons) learns the same thing repeatedly, we can easily pass an output when we see a similar object.
When we use the same concepts in the computer program and learn more about some data, then computers can also help us predict similar data. The reason we are using Computers is not that they will learn better. They will learn faster, and they can work with a larger amount of information that is impossible for human beings.
What are Artificial Neural Networks?
Artificial neural networks, usually 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. Source -Wikipedia
In simple words, we can say that a neural network comprises many perceptron layers; that’s why it has the name ‘multi-layer perceptron. These layers are also called hidden layers of dense layers. They are the primary unit that works together to form a perceptron layer. These neurons receive information in the set of inputs.
Why we need Artificial Neural Network?
As the world is growing bigger and the technology is also getting bigger. We are producing a large amount of data and information. We have advanced in many fields of development. We have been tackling many problems in the world with technology. We have been using Machine Learning to track all the problems with big data and find something out of the problem. We use traditional ML algorithms, but nowadays, those algorithms are not good enough to understand and solve the problem we are getting nowadays. So there comes *
- ANN is using it is very powerful. ANNs can learn and model non-linear and complex relationships, which is really important because, in real life, many of the relationships between inputs and outputs are non-linear and complex. In real-world data, we have many features, and feature selection will be harder if we are domain experts. ANN automatically detects the useful features and passes the one that doesn’t help in the building model.
- ANN assign random weight to all the link and start the algorithm building. It then compares the weights and works on changing until they met the convergence criterion.
- Artificial Neural Networks (ANN) have many different coefficients, which they can optimize. Hence, it can handle much more variability as compared to traditional models.
Use Case of ANN in Current Industry
Here are some of the industry use case of ANN and how they are using it:
Ecommerce
We have eCommerce used in various places in the industry. We can see many tech giants with branches of e-commerce like Amazon, Flipkart, AliExpress, eBay, and many more. Ecommerce has a vast audience, and they have a well-optimized service. Neural Network has been implemented in eCommerce for many years, and it has become advance in recent years. They have been using Neural Network to understand the deep knowledge and ideas behind the customer's shopping behavior. It helps the industry to predict the new order required and next year's sales. The NN is also used to increase stocks in the required time and decrease it as per the season. They have also been using the NN is understanding and calculating the profit on every offer given, that also by maximizing the profit and selling the customers all the product. Moreover, they use NN to understand and predict the customer buying behavior and then remind them with mail or SMS to repurchase the product.
Finance Sector
Neural Network is also suitable for the finance industry. There is a large amount of data in the finance industry, and when there is a lot of data, then the Neural Network will surely come into use. The Neural networks are most useful here because the amount of data is a lot, and NN is accuracy will be almost 100% which is a must required for the financial sectors. The more data we can provide, the more accurate result we can achieve. Neural networks are good in future predictions. Companies like Bridgewater and MJ Futures used the NN and got a return of around 199% with NN's help.
Neural Network has also been helping the banking sectors in evolving towards the next level of banking. The banks now have been understanding their customer's behavior and are going for a better analysis. This has been helping banks to make proper decisions for themselves and the clients. The NN has also helped banks to understand the customer based on Credit and Loans. They have been studying the customer's previous transaction pattern and predict the amount of credit that can be given and save themselves from credit risk.
Crime and Fraud Detection
Neural Network has also been developed in advance to fight crime and server a safe society. Sometimes back an ATM was designed to detect the person's face, and they can withdraw money with the face. The interesting thing is that company also trained the model to check for the faces of the people searching by police on such ATM robbery, and it helps detect the face of those and send the alarm. The Idea of a neural network can use this idea in cutting the crime and robbery rate. Neural Network has been helping increase the revenue in the financial sector, but it has been keeping it safe also. The application power with NN or Deep Learning can identify any fraudulent activity or any unusual behavior in the transaction and alert or shut down the application for a certain period of time. They can also understand the user timing and study the behavior and location of the transaction.
CONCLUSION
In conclusion, I would like to say that neural network is limited to these use cases and applications. It has a wide reach in many applications like self-driving cars, facial recognition, business ideas, health sectors, research and medicals, and many more. There is always an area of development in every application with the involvement of neural networks.
I hope I have provided some insights on the neural network and some of the use cases. Any suggestion on the blog would be highly entertained, and feel free to connect with me on my LinkedIn.
Thank You for your time. Have a Good Day.
