What is Artificial Neural Network (ANN)

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ANN is a soft-computing tool that can learn patterns and predicts. Learn more in: Biometric Identification System Using Neuro and Fuzzy Computational Approaches

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An artificial neural network defines a mathematical model for the simulation of a network of biological neurons (e.g. human nervous system). It simulates different aspects related to the behavior and capacity of the human brain, such as: intelligent information processing; distributed processing; high level of parallelism; faculty of learning, generalization and adaptation; high tolerance to inaccurate (or wrong) information. Learn more in: Artificial Neural Networks Tutorial

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An artificial neural network (ANN) is a piece of a computer system programmed to replicate the way the human brain analyzes and processes information. The foundation of artificial intelligence (AI) solves problems that, by human or mathematical criteria, would be impossible or complicated. ANNs have the capability of self-learning, meaning that more evidence is needed to obtain improved outcomes. Learn more in: Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity

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Artificial neural networks (ANN) are the pieces of a computing system designed to simulate the way the human brain analyzes and processes information. ANN has self-learning capabilities that enable them to produce better results. Learn more in: Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

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A computing system made up of a number of simple, highly interconnected processing elements, which mimics the biological neural network in order to process information by their dynamic state response to external inputs. Learn more in: Demographic Characterization of Heart Rate Variability (HRV)

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A mathematical model inspired in biological neural networks. The units are called neurons connected in various input, hidden and output layers. For a specific stimulus (numerical data at the input layer) some neurons are activated following an activation function and producing numerical output. Thus ANN is trained, storing the learned model in weight matrices of the neurons. This kind of processing has demonstrated to be suitable to find nonlinear relationships in data, being more flexible in some applications than models extracted by linear decomposition techniques. Learn more in: Automatic Classification of Impact-Echo Spectra I

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ANN represents a set of algorithms which are designed for pattern recognition. It is modelled after the human brain. Learn more in: Adaptive Neuro-Fuzzy Inference System in Agriculture

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It is a soft computing tool that resembles the operation of biological neural network of brain. The network trains itself with the process of learning such that it can map a test data with a known data from a given set of known dataset. According to the learning process it may be of two types: (a) supervised learning and (b) unsupervised learning. Learn more in: Development of a Stop-Line Violation Detection System for Indian Vehicles

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Artificial neural networks (ANNs) are a type of computing system that is inspired by biological neural networks present in the animal brain. Learn more in: Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture

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An artificial neural network is a data analysis method which operation resembles a network of biological neurons. ANNs are composed of a system of nodes (equivalent to neurons of a human brain) which are interconnected by weighted links (equivalent to synapses between neurons). The outcome of the ANN is altered by changes of the links’ weights. The data is fed to the input layer and the result of the network is displayed by the output layer. The input nodes represent the independent or predictor variables that are used for predicting the dependent variables, i.e., the output neurons. Learn more in: Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach

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is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Learn more in: Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products

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A machine learning technique that can be used to learn historical patterns and make future predictions. It works as a simulation of our brain nervous system, each node in ANN represents a neuron, and one or more nodes in each layer. Learn more in: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network

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A computational model that aims to simulate the behavior of the human brain. In order to do this, it combines the capabilities of simple computing elements, which correspond to biological neurons, highly interconnected and organized in layered structures. The ANNs are part of the family of the learning by example (LBE) techniques. The ANN can be suitably trained via examples (input-output pairs) and, after that, it learns how to generalize; then it applies what it has learned to new inputs that are not used during the training phase. Learn more in: EM-Source Localization in Indoor Environments by Using an Artificial Neural Network Performance Assessment and Optimization

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An important tool to set up linkage between provided input and required output and it is developed on the basis of the set up of communicating nervous system of human being. Learn more in: A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network

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Usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Learn more in: Computer Intelligence in Healthcare

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Is biologically inspired computer program designed to simulate the way in which the human brain processes information. ANN gathers its knowledge by detecting the patterns and relationships in data and learns (or is trained) through experience, not from programming. Learn more in: Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

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A predictive computer algorithm inspired by the biology of the human brain that can learn linear and non-linear functions from data. Artificial neural networks are particularly useful when the complexity of the data or the modelling task makes the design of a function that maps inputs to outputs by hand impractical. Learn more in: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques

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An artificial neuron network (ANN) is a nonlinear statistical data process inspired by the structure and functions of biological neurons, used for pattern recognition and modeling of complex input-output relationships. An ANN “learns” (adjusts its computational parameters) as information “flows” through its node layers, based on that input and output. Learn more in: Machine Learning in Healthcare: Introduction and Real-World Application Considerations

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An interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. It has the ability to learn from knowledge, which is expressed through interunit connection strengths, and can make this knowledge available for use. Learn more in: Computer-Aided Diagnosis of Cardiac Arrhythmias

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A basic technique in machine learning used to find a pattern or trend in a large dataset. All input data are assumed to be connected through hidden layers(s), which is similar to the concept of biological neurons. Learn more in: The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance

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It is a computing system inspired by biological neural network. Learn more in: Recent Studies and Research on Sickle Cell Disease: Statistical Analysis and Machine Learning Approach

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It is a computing model based on the structure of the human brain with many interconnected processing nodes that model input-output relationships. The model is organized in layers of nodes that interconnect to each other. Learn more in: Convolutional Neural Network

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It is a computing model based on the structure of the human brain with many interconnected processing nodes that model input-output relationships. The model is organized in layers of nodes that interconnect to each other. Learn more in: Deep Learning on Edge: Challenges and Trends

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A machine learning algorithm that performs “black box” non-linear fitting, similarly to the inter-connected neurons in human brain. Learn more in: Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

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ANNs are computational networks composed of multiple nodes named neurons interacting with each other. The nodes can take input data and perform simple operations on the data. Learn more in: Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence

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A system made up of interconnecting artificial neurons or nodes (usually simplified neurons) which may share some properties of biological neural networks. They may either be used to gain an understanding of biological neural networks, or for solving traditional artificial intelligence tasks without necessarily attempting to model a real biological system. Well known examples of ANN are the Hopfield, Kohonen and Cellular (CNN) models. Learn more in: Basic Cellular Neural Networks Image Processing

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An interconnected group of artificial neurons that uses a mathematical model, or computational model, for information processing, based on a connectionist approach to computation. Learn more in: Arrhythmia Detection and Classification Using Wavelet and ICA

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Neural network is a system which works as human brain and learn from past experiences. It is mostly used to solve complex, uncertain problems. Learn more in: Reverse Logistics Design with Neural Networks

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The artificial neural network is a data mining framework able to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules. Learn more in: Image Processing and Post-Data Mining Processing for Security in Industrial Applications: Security in Industry

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One of the computational models of the machine learning methodology. Artificial Neural Network simulates neurons in human brain, learns from existing patterns and fathom artificial intelligence problems. Learn more in: U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression

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Is an algorithm that attempts to replicate the operation of the human brain through the utilization of connected neurons which are organized in layers and send information to each other. Learn more in: Artificial Intelligence a Driver for Digital Transformation

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A computational model similar to the structure and functions of biological neural networks. Learn more in: Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture

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Artificial intelligence method that is composed of a collection of highly interconnected processing units called neurons that are used together to solve a problem. Learn more in: Intelligent Systems to Support Human Decision Making

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ANN is a computing paradigm that loosely simulates cortical structures of the brain. The simplest element of ANN is called a processing element, or node. Soft computing techniques are used to develop different types of ANN models based on different processing elements. Learn more in: Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance

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Artificial neural network referred to as neural network, is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to the external inputs. Learn more in: Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)

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Is non-parametric tool that learns from the surroundings, retains the learning and uses it subsequently. Learn more in: Learning Aided Digital Image Compression Technique for Medical Application

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ANNs are computational networks composed of multiple nodes named neurons interacting with each other. The nodes can take input data and perform simple operations on the data. Learn more in: Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments