Neural Network Applications

Neural networks are being used in every field of life. The applications of neural networks are endless. Neural networks are being used in data science, cybersecurity, transport, aviation, education, manufacturing, marketing, etc. Artificial neural networks or simply ANNs bring a degree of intelligence to computational models. ANNs can continuously learn and improve. ANNs are very useful when the algorithms become very complex and are difficult to implement. For some tasks, ANNs presents a cost-effective alternative. ANNs learns from data, so the extent of preprogramming is minimal. ANN implementations are often faster and more accurate. Some of the more widely used ANN applications are listed below.

Speech Recognition

Humans are more adapted to speech than text. Speech and sound are everywhere. If we are fully able to communicate with the computer using speech, then most people would have easy access to technology. Speech recognition systems are getting better, but the data required to train these types of systems is enormous. There are billions of people, and there are numerous types of background noises. To recognize a wide variety of speech, a neural network model needs to distinguish speech from noise in different environmental settings. For example, Google recently used convolutional neural networks to detect whales’ presence in the ocean using an audio recording. Recurrent multilayer networks and Kohonen self-organizing feature maps are ideal for speech recognition.

Face Recognition

Face recognition technology is being used extensively. It has become an integral part of governmental IDs, smartphone security, cybersecurity, and other biometric verification systems. A properly trained neural network can detect whether an image has a face or not. A more advanced neural network can further detect whose face it is. For the training of the neural network for facial recognition, a large number of input pictures are needed. These pictures are preprocessed to reduce their size and make their dimension uniform. For the reduction of dimensionality, principal component analysis (PCA) is employed. Consequently, the faces are first recognized and then categorized by a fully connected feedforward neural network using back-propagation training algorithms.

Signature Recognition

In legal transactions, signatures are one of the most useful ways to authorize and validate an individual. The first stage in signature recognition is image processing. The geometrical features are extracted from the image of the signature. These features are used in the training of the neural network. Once trained, the neural networks can validate a signature and are categorized as being authentic or forged.

Character Recognition

It is an essential type of pattern recognition. Nowadays, digital transformation is happening in every field of life. Books and handwritten notes are being converted to digital formats. Previously, the images of the manuscripts were used. However, it is now possible to automatically convert typed or handwritten characters, numbers, and letters using neural networks. An example of a character recognition system is optical character recognition or simply OCR. This system employs a multilayer perceptron neural network classifier. Back-propagation and neocognitron algorithms are also used widely for training neural networks for character recognition.

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