Industry use case on Artificial Neural Network

The developers always try to relate the technology with the real world i.e the artificial neural networks have been created by studying the human brain that how we as humans take input and process the information in our brain and resembling the neurons( which are the basic unit of our brain) the tech guys created the artificial neural networks which helps the machine to take accurate decisions.

What are neural networks?

“Artificial Neural Networks or ANN is an information processing paradigm that is inspired by the way the biological nervous system such as brain process information. It is composed of large number of highly interconnected processing elements (neurons) working in unison to solve a specific problem.” 

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Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

Attributes of Neural Networks

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

• Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
• Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
• Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
• Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
• Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

Industrial Use cases of Neural Networks

Artificial Neural Networks can classify information, cluster data, or predict outcomes. They can be used for a range of tasks. These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.

• By adopting Artificial Neural Networks businesses are able to optimize their marketing strategy. 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. 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.
• Developing Targeted Marketing Campaigns Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic. This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.
• Reducing Email Fatigue and Improving Conversion Rates by only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue. According to dragon360.com 61% of customers say that they are most likely to use companies that send them targeted content.
• Improving Search Engine Functionality
• 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 network models analyze location, historical data sets, as well as weather forecasts, models and other pieces of relevant information.
• By predicting a potential rise in demand the company is able to increase stock in store. This means that customers won’t leave empty-handed and also allows Walmart to offer product-related offers and incentives.

Neural Networks are Improving Search Engine Functionality

During 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.

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. improvements to its recommendation systems.

Conclusion

The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.

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