Industry use cases of Neural Networks

NEURAL NETWORK

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

ARTIFICIAL NEURAL NETWORK

An artificial neural network is a system of hardware or software that is patterned after the working of neurons in the human brain and nervous system. Artificial neural networks are a variety of deep learning technology which comes under the broad domain of Artificial Intelligence.

Deep learning is a branch of Machine Learning which uses different types of neural networks. These algorithms are inspired by the way our brain functions and therefore many experts believe they are our best shot to moving towards real AI (Artificial Intelligence).

How do Neural Networks work?

A neural network has a large number of processors. These processors operate parallelly but are arranged as tiers. The first tier receives the raw input similar to how the optic nerve receives the raw information in human beings.

Each successive tier then receives input from the tier before it and then passes on its output to the tier after it. The last tier processes the final output.

Small nodes make up each tier. The nodes are highly interconnected with the nodes in the tier before and after. Each node in the neural network has its own sphere of knowledge, including rules that it was programmed with and rules it has learnt by itself.  The key to the efficacy of neural networks is they are extremely adaptive and learn very quickly. Each node weighs the importance of the input it receives from the nodes before it. The inputs that contribute the most towards the right output are given the highest weight.

Industry use cases of Neural Networks

 Forecasting the Behavior of Complex Systems:

It is a broad application domain for neural networks. Specific examples include: electric load forecasting, economic forecasting, and forecasting natural and physical phenomena. One of the recent applications being studied is the river-flow forecasting. It is an important application that can have significant economic impact. It can help in predicting agricultural water supply and potential flood damage, estimating loads on bridge, etc. 

Signal Processing:

Neural network approaches have been successfully combined with other signal processing techniques to produce a wide variety of applications. It can very well be argued that the commercial success of neural networks has been from its ready incorporation into other information processing approaches, such as pattern recognition and statistical inference, as well as symbolic processing.  

DNA Sequence Analysis:

The huge amount of data the human genome project (presently going on in the US with scientists all over the world participating) produced will require high performance computing and more intelligent computer algorithm for analysis and inference. Recently, the neural network model has been recognized as a promising Al technique because such approaches might well embody important aspects of intelligence not captured by symbolic and statistical methods. These knowledge-based neural networks, called expert networks in some cases, perform as well as human experts.

Improving Marketing Strategies:

By adopting Artificial 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. It can also help to increase profits.

Pharmaceutical industry:

Artificial Neural Networks are being used by the pharmaceutical industry in a number of ways. The most obvious application is in the field of disease identification and diagnosis. It was reported in 2015 that in America 800 possible cancer treatments were in the trial.

With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyze and interpret it. The IBM Watson Genomics is one example of smart solutions being used to process large amounts of data. IBM Watson Genomics is improving precision medicine by integrating genomic tumor sequencing with cognitive computing. With a similar aim in mind, Google has developed DeepMind Health. Working alongside a number of medical specialists such as Moorfields Eye Hospital, the company is looking to develop a cure for macular degeneration.

Neural computers perform very favorably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data. Furthermore, knowledge is encapsulated in a compact, efficient way that can easily be adapted to changes in business environment. As with all technologies, there is a window of opportunity for exploitation-and that window is here today. You cannot afford to ignore the fact that your competitors are already investigating the opportunities and realizing the significant business benefits that neural technology brings to a range of applications. The reason one should use neural computing technology is the competition!