Industry Use-Cases of Neural Network!!!
Welcome back guys to my Blog. In this Blog I tried my best to explain the Industry Use-Cases a Neural Network can have. So let’s get going…
What is Neural Network??
There are two ways to think of a neural network. First is like a human brain. Second is like a mathematical equation.
A network starts with an input, somewhat like a Sensory organ. Information then flows through layers of neurons, where each neuron is connected to many other neurons. If a particular neuron receives enough stimuli, then it sends a message to any other neuron is it connected to through its axon. Similarly, an artificial neural network has an input layer of data, one or more hidden layers of classifiers, and an output layer. Each node in each hidden layer is connected to a node in the next layer. When a node receives information, it sends along some amount of it to the nodes it is connected to. The amount is determined by a mathematical function called an activation function, such as sigmoid .
Thinking of a neural network like a mathematical equation, a neural network is simply a list of mathematical operations to be applied to an input. The input and output of each operation is a tensor(or more specifically a vector or matrix). Each pair of layers is connected by a list of weights. Each layer has several tensors stored in it. An individual tensor in a layer is called a node. Each node is connected to some or all of the nodes in the next layer by a weight. Each node also has a list of values called biases. The value of each layer is then the out of the activation function of the values of the current layer (called X) multiplied by the weights.
The Basics of Neural Networks
Neural networks are typically organized in layers. Layers are made up of a number of interconnected ‘nodes’ which contain an ‘activation function’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. The hidden layers then link to an ‘output layer
How do Neural Networks work?
A neural network is a bundle of neurons connected by synapses. The role of neurons are played by the units that perform calculations. Each of these “neurons”:
- receives data from the input layer;
- processes it performing simple calculations with it;
- and then transmits it to another “neuron”.
Usually, neural networks consist of three types of neurons:
- Input;
- Output;
- Hidden.
Types of Neural Network:
- Artificial Neural Networks (ANN)
- Convolution Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
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:
So, let’s look at some examples of neural network applications in different areas.
Neural Network in e-Commerce
The most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, 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.
This means that many companies can improve their website search engine functionality. This allows customers with only a vague idea of what they want to easily find the perfect item. Amazon has reported sales increase of 29% following improvements to its recommendation systems.
Neural Network in Financial Services
In this industry, there are neural network applications for fraud detection, management, and forecasting. 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.
Neural Networks in the pharmaceutical industry
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.
Neural Network for 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.
Neural Network in 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.
Facebook uses Neural Network for Facial Recognition Software
Technology companies have long been working toward developing reliable facial recognition software. One company leading the way is Facebook. For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs. They have also developed Deep Face.
Deep Face
Deep Face is a form of facial recognition software-driven by Artificial Neural Networks. It is capable of mapping 3D facial features. Once the mapping is complete the software turns the information into a flat model. The information is then filtered, highlighting distinctive facial elements. To be able to do this Deep Face implements 120 million parameters. This technology hasn’t just emerged overnight. Deep Face has been trained with a pool of 4.4 million tagged faces. These images were taken from4,000 different Facebook accounts.
During the training process, tests were carried out presenting the system with side-by-side images. The system was then asked to identify if the images are of the same person. In these tests, Deep Face returned an accuracy rating of 97.25% .Human participants taking the same test scored, on average, 97.5%.
Neural Network that Keeps Customers Loyal to Company
Artificial Neural Networks can also identify customers likely to switch to a competitor. By knowing which customers are most likely to defect you are able to target them with tailored marketing campaigns. Offering incentives, or friendly reminders about your company, will encourage customers to stick around.
This predictive use of Artificial Neural Networks is already benefiting FedEx.
Forbes reports that FedEx can predict which customers are likely to leave with an accuracy of 60–90%. By applying Artificial Neural Networks in this way we can enhance and personalize the consumer’s experience. Encouraging repeat custom and helping to build a relationship between your business and your customers.
Google uses Neural Network for Improving Search Engine Functionality
Google revealed they were working on improving their search engine. These improvements are powered by a 30 layer deep Artificial Neural Network. This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colors.
Using an Artificial Neural Network allows the system to constantly learn and improve. This allows Google to constantly improve its search engine. Within a few months, Google was already noticing improvements in search results. The company reported that its error rate had dropped from 23% down to just 8%. Google’s application shows that neural networks can help to improve search engine functionality.
Neural networks systems to Identify Credit Risks
HSBC is just one bank using Artificial Neural Networks to transform how loan and mortgage applications are processed. The company uses neural networks to analyze customers with previous behavior patterns. This information can highlight personality traits that mark an applicant out as a credit risk.
Neural Network in Optimizing the Store Layout
Artificial Neural Networks can also improve physical store layouts. Their ability to quickly analyze and monitor stock levels allows companies to see which products are selling well and which aren’t. Poorly performing products can then be placed on offer or moved to a more eye-catching position in the store. These systems also allow companies to see which products are frequently purchased together. Placing commonly purchased products close together encourages people buying one item to purchase the other. You can then surround these products with other possible purchases. Not only does this cut the waste of perishable products but it can also help to prevent a backlog building in the warehouse. Fashion giants H&M are looking to these applications to transform their business model. It’s been reported that the retailer is using Artificial Neural Networks to do everything from warehouse management to store layout.
Paying With Your Face
Recently, the Macau district in China has introduced ATM’s that are capable of reading the user’s face. This negates the need for cards and pin numbers. If proved to be successful it could lead to the end of paying with plastic.
Meanwhile, companies such as Facefirst are developing software capable of identifying shoplifters. When implemented this can cut loss to crime, saving money, and making stores safer. The company is also looking to roll out its systems at airports and other public areas.
Microsoft and Nvidia are just two of the companies working with Facefirst technology. Finally at the 2019 CES Proctor and Gamble revealed their idea of the store of the future. Here cameras driven by Artificial Neural Networks recognize customer’s face. The system then makes product suggestions based on the customer’s past history and information.
Conclusion
The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.
🙏Thank You for Reading!!
