Learning the application of Artificial Neural Networks

 

Artificial neural networks (ANN) are basically a chain of artificially contrived neurons that are configured to perform specific tasks. Although, the neurons of these networks are inspired by the neurons that reside in our brain, but they function quite differently from that of a biological neuron. The neurons in these units act as computational units. In last few decades, ANN has become one of the most vital technologies that drive some of the most crucial industries in the world for instance- Voice recognition, financial forecasting, Fraud detection etc.

Since last few decades, ANN has been developing as a sub field of AI and it has made substantial progress. Now that we have adequate computing power to actually implement the technology, it is being embraced by numerous areas and industries.

Neural networks in Business

Business is a diversified domain with many unique fields of expertise, such as accounting or financial analysis. Approximately any application for a neural network will fit under one business field or financial analysis. There is substantial potential for exploitation of neural networks in business domains for instance- scheduling and resource allocation. In addition, neural networks can also be exploited for database mining that is searching for implicit patterns in databases. However, there is also a whole field of AI called data Science.

Speech Recognition

Speech is one of the most conspicuous traits of interaction among humans. Hence, people’ the desire for computers to have a natural interface is inevitable. Substantial progress has been made in the field of voice recognition. Although, present systems are still facing problems, since these systems have limited set of vocabulary or grammar, in addition you need to train these systems over and over for various user in various circumstances. Artificial neural networks are playing a crucial role in this problem.  Subsequent ANN have been used in speech recognition-

 

 

  • Multilayer networks
  • Multilayer networks with recurrent connections
  • Kohonen self-organizing feature map

 

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Face recognition

It’s a biometric technique for recognizing a certain face. Due to the characterization of non-face pictures, it is considered a typical task. But in case of a well trained neural network, it can be separated into two classes – images that have faces and images that do not have faces.

First of all we need to preprocess the input images. Next, we need to decrease the dimensionality of the image. And eventually it is classified with the help of a neural network algorithm. Subsequent neural networks are used in order to train the preprocessed images-

  • An interconnected multilayer feed-forward neural network trained with Back-propagation.
  • Principal Component Analysis (PCA) is used for dimensionality reduction.  

Signature Verification   

Signature verifications are one of the most efficient ways of authenticating an individual. It is a non-vision based technology.

The primary approach for this technique is to extract the features (geometric feature set) that represent the signature. With the help of these features the neural net is trained with an appropriate algorithm. Eventually in the verification stage, the trained neural net will separate the forged signature from the genuine ones.

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Ashpreet Kaur - Jul 2, 2021

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