What is Artificial Neural Networks (ANN)

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AI method based on models of biological neurons, formed by a layered network of nodes organized on an input layer, hidden layers and an output layer. Learn more in: Challenges and Opportunities of Soft Computing Tools in Health Care Delivery

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AI method based on models of biological neurons, formed by a layered network of nodes organized on an input layer, hidden layers and an output layer. Learn more in: Challenges and Opportunities of Soft Computing Tools in Health Care Delivery

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They are based on the concept of neurons in the brain and are a set of nodes connected together through weighted edges that help in recognizing patterns to develop artificially intelligent systems. Learn more in: Recent Developments in Chatbot Usability and Design Methodologies

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An artificial neural network is a massive parallel distributed processor made up of simple processing units. It has the ability to learn from experiential knowledge expressed through interunit connections strengths, and can make such knowledge available for use. Learn more in: Bankruptcy Prediction through Artificial Intelligence

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It is a computational technique inspired by the human brain. It consists of nodes (neurons) and connections (also known as synapses) between them, to exchange and transfer data. The network learns automatically according to the flow of the data. Learn more in: Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant

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Computing systems that use networks of interconnected nodes to process and gain knowledge from training data, then apply the knowledge to make predictions. Learn more in: Intelligent Log Analysis Using Machine and Deep Learning

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Intelligent systems that mimic processing of information by human brain neurons. They are capable of learning attributes, generalizing, parallel processing of information and error minimization. As a result, they are capable to model and solve complex systems. Learn more in: Modeling Stock Market Industrial Sectors as Dynamic Systems and Forecasting

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intelligent systems that mimic the processing of information by human brain neurons. They are capable of learning attributes, generalizing, parallel processing of information, and error minimization. Learn more in: Modeling and Forecasting Electricity Price Based on Multi Resolution Analysis and Dynamic Neural Networks

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Also called a simulated neural network (SNN) or just a neural network (NN), is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. Learn more in: Different Roles and Definitions of Spatial Data Fusion

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Computational models inspired by the properties of biological nervous systems. Usually composed of layers of highly interconnected simple processing units, they are characterised by learning capabilities and can be implemented in software and hardware. Learn more in: The Bees Algorithm as a Biologically Inspired Optimisation Method

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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 various applications such as robotics, speech recognition, signal processing, medical diagnosis, or power systems. Learn more in: Voltage Instability Detection Using Neural Networks

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Computational models inspired by brain's nervous systems which are capable of machine learning and pattern recognition. ANN are composed by simple, and highly interconnected processing elements that process information by their dynamic state response to external inputs. Learn more in: Machine Learning Approaches to Automated Medical Decision Support Systems

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These are the pieces of computing system designed to simulate the way the human brain analyzes and process information. Learn more in: Contemporary Biometric System Design

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An artificial neural network, often just called a “neural network” (NN), is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. Knowledge is acquired by the network from its environment through a learning process, and interneuron connection strengths (synaptic weighs) are used to store the acquired knowledge. Learn more in: Adaptive Neuro-Fuzzy Systems

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ANN is a pattern matching technique that uses training data to build a model and uses the model to predict unknown samples. It consists of input, output, and hidden nodes and connections between nodes. The weights of the connections are iteratively adjusted in order to get an accurate model. Learn more in: Data Mining in Tourism