Industry use cases of Neural Networks

Artificial neural networks (ANNs), usually simply called neural networks (NNs), 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. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The “signal” at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

How Artificial Neural Networks Function

ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate output through a transfer function to the output layer.

Use Cases of Neural Network

Pattern Recognition Neural Networks

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns.

Some examples of the pattern are — fingerprint image, a handwritten word, a human face, or a speech signal.

Fuzzy Logic using Neural Networks

Fuzzy logic refers to the logic developed to express the degree of truthiness by assigning values between 0 and 1, unlike traditional Boolean logic that represents 0 and 1.

Fuzzy logic and Neural networks have one thing in common. They can be used to solve problems of pattern recognition and others that do not involve any mathematical model.

Systems combining both fuzzy logic and neural networks are neuro-fuzzy systems.

These systems (Hybrid) can combine the advantages of both neural networks and fuzzy logic to perform in a better way.

Fuzzy logic and Neural Networks have been integrated to use in the following applications –

• Automotive engineering
• Applicant screening of jobs
• Control of crane
• Monitoring of glaucoma

In a hybrid (neuro-fuzzy) model, Neural Networks Learning Algorithms are fused with the fuzzy reasoning of fuzzy logic.

The neural network determines the values of parameters, while if-then rules are handled by fuzzy logic.

Face Recognition using Neural Networks

Face recognition entails comparing an image with a database of saved faces to identify the person in that input picture. The face detection mechanism involves dividing images into two parts; one containing targets (faces) and one providing the background.

The associated assignment of face detection has direct relevance to the fact that images need to be analyzed and faces identified, earlier than they can be recognized.

Neural networks in medicine

Neural Networks (ANN) is currently a ‘hot’ research area in medicine and it is believed that they will receive an extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modeling parts of the human body and recognizing diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).

Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the ‘quantity'. The examples need to be selected very carefully if the system is to perform reliably and efficiently.

Neural Networks are Improving Marketing Strategies

Neural Networks businesses are able to optimize their marketing strategy. Systems powered by Artificial Neural Networks all capable of processing masses of information. This includes customers personal details, shopping patterns as well as any other information relevant to your business. Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation. To put it another way segmentation of customers allows businesses to target their marketing strategies. Businesses can identify and target customers most likely to purchase a specific service or produce. This focusing of marketing campaigns means that time and expense isn’t wasted advertising to customers who are unlikely to engage.

This application of Artificial Neural Networks can save businesses both time and money.

Neural Networks in Optimizing 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.

Improving Search Engine Functionality

During the 2015 Google I/O keynote address in San Francisco, 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.

Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites. 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 increases of 29% following improvements to its recommendation systems.