Industry use cases of Neural Networks

what is neural network?

A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be ?1 and 1.

Applications in Deep Learning and Artificial Intelligence

Artificial neural networks are a form of deep learning.

They are also one of the main tools used in machine learning.

Consequently, ANN’s play an increasingly important role in the development of artificial intelligence.

The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.

This technique allows the system’s hidden layers to become versatile.

Adapting to situations where the outcome does not match the one originally intended.

The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.

Deep learning neural networks are networks made up of multiple layers.

This allows the system to become more versatile.

Different layers can analyze and extract different features.

This process allows the system to identify new data or images.

It also allows for unsupervised learning and more complex tasks to be undertaken.

 Artificial Neural Networks Function

Neural networks are statistical models designed to adapt and self-program by using learning algorithms 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.

Machine Learning in ANNs

As there are too many Machine learning strategies are present, let us see them one by one:

a. Supervised Learning:

 Generally, in this learning, a teacher is present to teach. That teacher must be aware of ANN. For example: The teacher feeds only example data. That teacher already knows the answers.

b. Unsupervised Learning:

If there is presently no data set. Then we need this learning technique.

c.  Reinforcement Learning:

 This Machine learning technique is based on observation. Although, if it’s negative the networks need to adjust their weights. That can make a different required decision the next time.

How do Neural Networks work?

As we have seen Artificial Neural Networks are made up of several different layers. Each layer houses artificial neurons called units. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. This knowledge includes the rules that the system was originally programmed with. It also includes any rules the system has learned for itself. This makeup allows the network to learn and react to both structured and unstructured information and data sets. Almost all artificial neural networks are fully connected throughout these layers. Each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.

The first layer is the input layer. This takes on the information in various forms. This information then progresses through the hidden layers where it is analyzed and processed. By processing data in this way, the network learns more and more about the information. Eventually, the data reaches the end of the network, the output layer. Here the network works out how to respond to the input data. This response is based on the information it has learned throughout the process. Here the processing nodes allow the information to be presented in a useful way.

Applications of neural networks in the 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 analyse 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 Moorfield’s Eye Hospital, the company is looking to develop a cure for macular degeneration.

Developing Personalized Treatment Plans:

A personalised treatment plan can be more effective than adopting a standardised approach.

Artificial Neural Networks and supervised learning tools are allowing healthcare professionals to predict how patients may react to treatments based on genetic information.

The IBM Watson Oncology is leading this approach.

It is able to analyze the medical history of a patient as well as their current state of health.

This information is processed and compared to treatment options, allowing physicians to select the most effective.

MIT’s Clinical Machine Learning Group is advancing precision medicine research with the use of neural networks and algorithms.

The aim is to allow medical professionals to get a better understanding of how disease forms and operates.

This information can help to design an effective treatment.

The team at MIT are currently working on possible treatment plans for sufferers of Type 2 Diabetes.

Meanwhile, the Knight Cancer Institute and Microsoft’s Project Hanover is using networks and machine learning tools to develop precision treatments.

In particular, they are focusing on treatments for Acute Myeloid Leukemia.

Vast amounts of information and data are required to progress precision medicine and personalised treatments.

Artificial Neural Networks and machine learning tools are able to quickly and accurately analyse and present data in a useful way.

This ability makes it the perfect tool for this form of research and development.