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Artificial Neural Network

 

Introduction: 

Artificial Neural Network (ANN) is a computing system designed to simulate how the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI), which solves problems that cannot be proven or difficult to solve by humans or statistical standards. Artificial neural networks have self-learning capabilities, allowing them to produce better results when more data is available. Artificial neural networks are built like a human brain, and neuron nodes are connected like a network. The human brain has hundreds of billions of cells called neurons. Each neuron consists of a cell body responsible for processing information by transmitting it to (input) and away from the brain (output). 

An artificial neural network has hundreds or thousands of artificial neurons called processing units connected through nodes. These processing units consist of input and output units. The input unit receives various forms and structures of information based on an internal weighting system, and the neural network tries to understand the presented information to generate an output report. Just as humans need rules and guidelines to arrive at results or outputs, artificial neural networks also use a set of learning rules (short for backpropagation errors) called backpropagation to refine their output. 

The artificial neural network will initially go through a training phase in which it will learn to recognize patterns in the data from the visual, auditory, or textual aspects. In this supervision phase, the network compares the actual output with the expected output, the expected output. The difference between the two results can be adjusted by backpropagation. This means that the network will reverse, from the output unit to the input unit, to adjust the weight of the connection between the units until the difference between the actual result and the expected result produces the smallest possible error. 

 

Why do we Artificial neural networks? 

Neural networks have human-like attributes and can complete tasks in infinite permutations and combinations, so they are very suitable for today's big data-based applications. Because neural networks also have a unique ability (called fuzzy logic) to understand ambiguous, contradictory, or incomplete data, they can use controlled processes when no exact model is available. 

According to a report released by Statista, in 2017, the amount of global data reached nearly 100,000 PB (i.e., one million GB) per month; it is expected that by 2021, they will reach 232,655 PB. Like businesses, individuals, and devices generate large amounts of information, all this big data is valuable, and neural networks can sense this information. 

 

Attributes of Artificial Neural Networks: 

  • Adaptive learning: Like humans, neural networks model nonlinear and complex relationships and build on prior knowledge. For example, the software uses adaptive learning to teach mathematics and language arts. 
  • Self-organization: The ability to cluster and classify large amounts of data makes neural networks particularly suitable for organizing complex visual problems caused by medical image analysis. 
  • Fault tolerance: When an important part of the network is lost or missing, the neural network can fill the gap. This function is beneficial in space exploration, where electronic equipment may always malfunction. 

 

Tasks Artificial Neural Network Perform: 

  • Classification: NN organizes patterns or data sets into predefined classes. 
  • Prediction: They produce the expected output from a given input.  
  • Clustering: They identify the data's unique features and classify them without any prior knowledge of the data. 
  • Association: You can train the neural network to "remember" mode. When you display an unfamiliar version of a pattern, the network will associate it with the most comparable version in memory and revert to the latter. 

 

Applications of Artificial Neural Networks: 

  • Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations 
  • Robotics: forklift robots, manipulator controllers, trajectory control, and vision systems. 
  • Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken languages, and pattern recognition (face recognition, Objects, fingerprints, semantic analysis, spell checking, signal processing, and speech recognition). 
  • Education: Adaptive learning software, dynamic prediction, education system analysis and prediction, student performance modeling. 
  • Medical: Cancer cell analysis, ECG and EEG analysis, hospital system cost reduction and quality improvement, transplantation process optimization. 

 

  •  January, 15, 2021
  • P.S.S. Sushmita
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