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What is Artificial Neural Networks

Chapter 43
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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.
Published in Chapter:
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Filipe Quinaz (University of Beira Interior, Portugal), Paulo Fazendeiro (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal), Miguel Castelo-Branco (University of Beira Interior, Portugal), and Pedro Araújo (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-3990-4.ch043
Abstract
The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.
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Chapter 43
Soft Methods for Automatic Drug Infusion in Medical Care Environment
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.
Published in Chapter: Soft Methods for Automatic Drug Infusion in Medical Care Environment; From: Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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Chapter 5
The Digital Transformation: Crafting Customer Engagement Strategies for Success
They are an essential feature of artificial intelligence and machine learning, especially in the deep learning discipline.
Published in Chapter: The Digital Transformation: Crafting Customer Engagement Strategies for Success; From: Digital Technologies, Ethics, and Decentralization in the Digital Era
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Chapter 19
ANN Development with EC Tools: An Overview
Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model, that represents an input/output relation,
Published in Chapter: ANN Development with EC Tools: An Overview; From: Encyclopedia of Artificial Intelligence
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Chapter 32
AVI of Surface Flaws on Manufactures I
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.
Published in Chapter: AVI of Surface Flaws on Manufactures I; From: Encyclopedia of Artificial Intelligence
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Chapter 163
Telemedicine and Information Technology for Disaster Medical Scenarios
Computer systems (hardware and software) that “learn” by training on inputs, largely with pattern recognition as their basis of operation.
Published in Chapter: Telemedicine and Information Technology for Disaster Medical Scenarios; From: Encyclopedia of Healthcare Information Systems
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Chapter 14
Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection
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.
Published in Chapter: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 8
An Overview of Biomedical Image Analysis From the Deep Learning Perspective
It mimics animal neural networks and useful in taking some action by observing some example instead of being explicitly programmed.
Published in Chapter: An Overview of Biomedical Image Analysis From the Deep Learning Perspective; From: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
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Chapter 120
Hierarchical Neuro-Fuzzy Systems Part I
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
Published in Chapter: Hierarchical Neuro-Fuzzy Systems Part I; From: Encyclopedia of Artificial Intelligence
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Chapter 26
Stochastic Neural Network Classifiers
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.
Published in Chapter: Stochastic Neural Network Classifiers; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 127
A Hybrid System for Automatic Infant Cry Recognition I
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
Published in Chapter: A Hybrid System for Automatic Infant Cry Recognition I; From: Encyclopedia of Artificial Intelligence
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Chapter 94
Evolving Graphs for ANN Development and Simplification
Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model, that represents an input/output relation,
Published in Chapter: Evolving Graphs for ANN Development and Simplification; From: Encyclopedia of Artificial Intelligence
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Chapter 447
Neural Networks for Intrusion Detection
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.
Published in Chapter: Neural Networks for Intrusion Detection; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 28
Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness
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.
Published in Chapter: Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 7
Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization
They are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition.
Published in Chapter: Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 10
The Value Proposition of Machine Learning in Construction Management: Exploring the Trends in Construction 4.0 and Beyond
Machine learning models in which hypotheses take the form of complex algebraic circuits, typically organized into many layers, with tunable connection strengths.
Published in Chapter: The Value Proposition of Machine Learning in Construction Management: Exploring the Trends in Construction 4.0 and Beyond; From: Artificial Intelligence and Machine Learning Techniques for Civil Engineering
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Chapter 46
Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)
A network of simple processing elements which can exhibit complex global behavior, determined by the connections between the processing elements and element parameters.
Published in Chapter: Current Issues and Future Trends of Clinical Decision Support Systems (CDSS); From: Encyclopedia of Healthcare Information Systems
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Chapter 7
Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Published in Chapter: Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things; From: Handbook of Research on Advancing Cybersecurity for Digital Transformation
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Chapter 495
Predictive Data Mining: A Survey of Regression Methods
They are nonlinear predictive models that learn through training and resemble biological neural networks in structure.
Published in Chapter: Predictive Data Mining: A Survey of Regression Methods; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 13
Design of an Agribusiness Innovative and Autonomous Robot System for Chemical Weed Control for Staple Food Crops Production in Sub-Saharan Africa
Artificial neural networks are mathematical models that attempt to simulate and mimic the operation of the human brain.
Published in Chapter: Design of an Agribusiness Innovative and Autonomous Robot System for Chemical Weed Control for Staple Food Crops Production in Sub-Saharan Africa; From: Opportunities and Strategic Use of Agribusiness Information Systems
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Chapter 16
Studying Individualized Transit Indicators Using a New Low-Cost Information System
Computational models inspired by the brain that are capable of machine learning and pattern recognition.
Published in Chapter: Studying Individualized Transit Indicators Using a New Low-Cost Information System; From: Handbook of Research on Embedded Systems Design
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Chapter 6
Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment
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.
Published in Chapter: Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 6
Computational Models for the Analysis of Modern Biological Data
Machine learning methods consisting of interconnecting artificial neurons that simulate the properties of biological neural networks.
Published in Chapter: Computational Models for the Analysis of Modern Biological Data; From: Handbook of Research on Systems Biology Applications in Medicine
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Chapter 17
Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya
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.
Published in Chapter: Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 232
2D-PAGE Analysis Using Evolutionary Computation
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.
Published in Chapter: 2D-PAGE Analysis Using Evolutionary Computation; From: Encyclopedia of Artificial Intelligence
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Chapter 176
Neural Network-Based Visual Data Mining for Cancer Data
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
Published in Chapter: Neural Network-Based Visual Data Mining for Cancer Data; From: Encyclopedia of Artificial Intelligence
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Chapter 11
Adaptive Network Based Fuzzy Interference System (ANFIS) Modeling of an Anaerobic Wastewater Treatment Process
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.
Published in Chapter: Adaptive Network Based Fuzzy Interference System (ANFIS) Modeling of an Anaerobic Wastewater Treatment Process; From: Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions
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Chapter 210
Solar Radiation Forecasting Model
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.
Published in Chapter: Solar Radiation Forecasting Model; From: Encyclopedia of Artificial Intelligence
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Chapter 95
Insulin Metabolism Models for Children with Type 1 Diabetes
Neural networks that include artificial intelligence.
Published in Chapter: Insulin Metabolism Models for Children with Type 1 Diabetes; From: Encyclopedia of Healthcare Information Systems
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Chapter 15
Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications
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.
Published in Chapter: Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications; From: Handbook of Research on Solar Energy Systems and Technologies
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Chapter 11
AI-Based Cyber Defense for More Secure Cyberspace
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.
Published in Chapter: AI-Based Cyber Defense for More Secure Cyberspace; From: Handbook of Research on Civil Society and National Security in the Era of Cyber Warfare
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Chapter 179
Neural Networks and HOS for Power Quality Evaluation
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.
Published in Chapter: Neural Networks and HOS for Power Quality Evaluation; From: Encyclopedia of Artificial Intelligence
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Chapter 88
Evolutionary Approaches for ANNs Design
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.
Published in Chapter: Evolutionary Approaches for ANNs Design; From: Encyclopedia of Artificial Intelligence
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Chapter 545
Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot
A method that is capable of learning, storing knowledge, and making decisions based on data it has been exposed to.
Published in Chapter: Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 2
Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection
The most important one algorithm of machine learning used in classification and clustering.
Published in Chapter: Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection; From: Handbook of Research on Threat Detection and Countermeasures in Network Security
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Chapter 22
Artificial Intelligence and Rubble-Mound Breakwater Stability
Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model representing an input/output relation.
Published in Chapter: Artificial Intelligence and Rubble-Mound Breakwater Stability; From: Encyclopedia of Artificial Intelligence
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Chapter 28
Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process
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 ).
Published in Chapter: Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process; From: Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry
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Chapter 9
Artificial Intelligence Methods and Their Applications in Civil Engineering
A data modeling tool that is able to capture and to represent complex input/output relationships.
Published in Chapter: Artificial Intelligence Methods and Their Applications in Civil Engineering; From: Handbook of Research on Recent Developments in Materials Science and Corrosion Engineering Education
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Chapter 300
Increasing the Accuracy of Predictive Algorithms: A Review of Ensembles of Classifiers
They are nonlinear predictive models that learn through training and resemble biological neural networks in structure.
Published in Chapter: Increasing the Accuracy of Predictive Algorithms: A Review of Ensembles of Classifiers; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 44
Computational Intelligence in Survival Analysis
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.
Published in Chapter: Computational Intelligence in Survival Analysis; From: Encyclopedia of Business Analytics and Optimization
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Chapter 18
What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?
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Chapter 116
Growing Self-Organizing Maps for Data Analysis
An interconnected group of units or neurons that uses a mathematical model for information processing based on a connectionist approach to computation.
Published in Chapter: Growing Self-Organizing Maps for Data Analysis; From: Encyclopedia of Artificial Intelligence
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Chapter 235
Wave Reflection at Submerged Breakwaters
Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model representing an input/output relation
Published in Chapter: Wave Reflection at Submerged Breakwaters; From: Encyclopedia of Artificial Intelligence
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Chapter 12
Artificial Intelligence and Supply Chain Management Application, Development, and Forecast
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Chapter 5
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
Information processing systems with interconnected components analogous to neurons that mimic biological nervous systems and the ability to learn through experience.
Published in Chapter: Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 6
Overview of Machine Learning Approaches for Wireless Communication
A machine learning algorithm that is created by mimicking the information transmission and problem-solving mechanism in the human brain.
Published in Chapter: Overview of Machine Learning Approaches for Wireless Communication; From: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
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Chapter 166
Multilogistic Regression by Product Units
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.
Published in Chapter: Multilogistic Regression by Product Units; From: Encyclopedia of Artificial Intelligence
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Chapter 20
ANN-Based Defects' Diagnosis of Industrial Optical Devices
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
Published in Chapter: ANN-Based Defects' Diagnosis of Industrial Optical Devices; From: Encyclopedia of Artificial Intelligence
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