What is Artificial Neural Networks

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Are mathematical or computational models that are inspired by biological neural networks such as the brain. Modern neural networks are non-linear statistical data modeling tools and are usually used to model complex relationships between inputs and outputs or to find patterns in data. Learn more in: Soft Methods for Automatic Drug Infusion in Medical Care Environment

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Are mathematical or computational models that are inspired by biological neural networks such as the brain. Modern neural networks are non-linear statistical data modeling tools and are usually used to model complex relationships between inputs and outputs or to find patterns in data. Learn more in: Soft Methods for Automatic Drug Infusion in Medical Care Environment

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Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model, that represents an input/output relation, Learn more in: ANN Development with EC Tools: An Overview

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A set of basic processing units which communicate to each other by weighted connections. These units give rise a parallel processing with particular properties such as the ability to adapt or learn, to generalise, to cluster or organise data, to approximate non-linear functions. Each unit receives an input from neighbours or external sources and uses it to compute an output signal. Such signal is propagated to other units or is a component of the network output. In order to map an input set into an output one a neural network is trained by teaching patterns, changing its weights according to proper learning rules. Learn more in: AVI of Surface Flaws on Manufactures I

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Computer systems (hardware and software) that “learn” by training on inputs, largely with pattern recognition as their basis of operation. Learn more in: Telemedicine and Information Technology for Disaster Medical Scenarios

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Artificial neural networks are models are derived from animal central nervous systems and can be regarded as systems of internally connected neurons which are capable of machine learning and pattern recognition. Learn more in: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

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It mimics animal neural networks and useful in taking some action by observing some example instead of being explicitly programmed. Learn more in: An Overview of Biomedical Image Analysis From the Deep Learning Perspective

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Composed of several units called neurons, connected through synaptic weights, which are iteratively adapted to achieve the desired response. Each neuron performs a weighted sum of its inputs, which is then passed through a nonlinear function that yields the output signal. ANNs have the ability to perform a non-linear mapping between their inputs and outputs, which is learned by a training algorithm Learn more in: Hierarchical Neuro-Fuzzy Systems Part I

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Computer models of interconnected neurons that can be trained to carry out pattern recognition and other low-level cognitive functions through supervised or unsupervised of learning. Learn more in: Stochastic Neural Network Classifiers

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A network of many simple processors that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis Learn more in: A Hybrid System for Automatic Infant Cry Recognition I

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Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model, that represents an input/output relation, Learn more in: Evolving Graphs for ANN Development and Simplification

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A type of artificial intelligence that attempts to imitate the way a human brain works. Rather than using a digital model, in which all computations manipulate zeros and ones, a neural network works by creating connections between processing elements, and the organization and weights of the connections determine the output. Learn more in: Neural Networks for Intrusion Detection

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is a computational model based on biological neural networks which consists of an interconnected group of artificial neurons that have been practically used in prediction, classification, control problem and approximation. In other words, ANNs are an adaptive system that changes its structure based on internal or external information that flows through the network during the learning phase. ANNs consist of a set of nodes (input, hidden and output neurons) connected by weights. Learn more in: Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness

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They are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. Learn more in: Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization

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A network of simple processing elements which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters. Learn more in: Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)

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Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Learn more in: Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things

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They are nonlinear predictive models that learn through training and resemble biological neural networks in structure. Learn more in: Predictive Data Mining: A Survey of Regression Methods

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Artificial neural networks are mathematical models that attempt to simulate and mimic the operation of the human brain. Learn more in: Design of an Agribusiness Innovative and Autonomous Robot System for Chemical Weed Control for Staple Food Crops Production in Sub-Saharan Africa

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Computational models inspired by the brain that are capable of machine learning and pattern recognition. Learn more in: Studying Individualized Transit Indicators Using a New Low-Cost Information System

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Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Learn more in: Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment

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Machine learning methods consisting of interconnecting artificial neurons that simulate the properties of biological neural networks. Learn more in: Computational Models for the Analysis of Modern Biological Data

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A model system for processing large and complex data which uses inputs to generate outputs in the process, it simulates the working principles of neuron of a human brain. Learn more in: Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya

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System composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes. Learn more in: 2D-PAGE Analysis Using Evolutionary Computation

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Interconnected group of simple units (neurons) that, as a function of the connections between the units and the parameters, can compute complex behaviors and find nonlinear relationships in data. They are used in applications such as robotics, signal processing, or medical diagnosis Learn more in: Neural Network-Based Visual Data Mining for Cancer Data

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Artificial neural networks mimic the properties of biological neurons. Artificial neural networks are commonly used to solve non-linear problems without necessarily creating a model of a real biological system. Learn more in: Adaptive Network Based Fuzzy Interference System (ANFIS) Modeling of an Anaerobic Wastewater Treatment Process

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A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis. Learn more in: Solar Radiation Forecasting Model

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Neural networks that include artificial intelligence. Learn more in: Insulin Metabolism Models for Children with Type 1 Diabetes

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Artificial Neural Networks are an approach to machine learning which developed out of attempts to model the processing that occurs within the neurons of the brain. Learn more in: Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

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Family of statistical learning algorithms inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected “neurons” which can compute values from inputs, and are capable of machine learning as well as pattern recognition thanks to their adaptive nature. Learn more in: AI-Based Cyber Defense for More Secure Cyberspace

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A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis. Learn more in: Neural Networks and HOS for Power Quality Evaluation

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Models inspired by the working of the brain, considered as a combination of neurons and synaptic connections, which are capable of transmitting data through multiple layers, giving a system able to solve different problems like pattern recognition and classification. Learn more in: Evolutionary Approaches for ANNs Design

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A method that is capable of learning, storing knowledge, and making decisions based on data it has been exposed to. Learn more in: Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot

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The most important one algorithm of machine learning used in classification and clustering. Learn more in: Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection

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Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model representing an input/output relation. Learn more in: Artificial Intelligence and Rubble-Mound Breakwater Stability

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Are computational networks that try to simulate the decision process in the nerve cell (neurons) networks of the biological (human or animal) central nervous system. This simulation is a cell-to-cell (neuron-neuron, element-to-element) simulation. Inspired by the neurophysiological knowledge of biological neurons and networks of such biological neurons ( Graupe, 2013 ). Learn more in: Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process

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A data modeling tool that is able to capture and to represent complex input/output relationships. Learn more in: Artificial Intelligence Methods and Their Applications in Civil Engineering

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They are nonlinear predictive models that learn through training and resemble biological neural networks in structure. Learn more in: Increasing the Accuracy of Predictive Algorithms: A Review of Ensembles of Classifiers

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Mathematical models inspired by biological neural networks. They consist of several layers of artificial neurons. The structure and weights of connection between neurons are changing during learning phase. Neural networks are able to model complex relationships between input and output data. Learn more in: Computational Intelligence in Survival Analysis

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A machine learning technique, inspired by the biological nervous system. Learn more in: What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?

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An interconnected group of units or neurons that uses a mathematical model for information processing based on a connectionist approach to computation. Learn more in: Growing Self-Organizing Maps for Data Analysis

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Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model representing an input/output relation Learn more in: Wave Reflection at Submerged Breakwaters

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Composed number of nodes which correspond to biological neurons. Learn more in: Artificial Intelligence and Supply Chain Management Application, Development, and Forecast

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Information processing systems with interconnected components analogous to neurons that mimic biological nervous systems and the ability to learn through experience. Learn more in: Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images

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A machine learning algorithm that is created by mimicking the information transmission and problem-solving mechanism in the human brain. Learn more in: Overview of Machine Learning Approaches for Wireless Communication

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A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis. Learn more in: Multilogistic Regression by Product Units

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A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis Learn more in: ANN-Based Defects' Diagnosis of Industrial Optical Devices