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What is Artificial Neural Network (ANN)

Chapter 18
Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
It is based on the belief that the working of the human brain by making the right connections can be imitated using silicon and wires as living neurons and dendrites.
Published in Chapter:
Skin Cancer Lesion Detection Using Improved CNN Techniques
Sourav Kumar (Birla Institute of Technology and Science, India), Garima Jaiswal (Amity University, Noida, India), and Keshav Sinha (University of Petroleum and Energy Studies, India)
Copyright: © 2023 |Pages: 23
DOI: 10.4018/978-1-6684-4755-0.ch018
Abstract
The mechanized melanoma detection in dermoscopy pictures is highly challenging. This is because of the low differentiation of skin sores, the tremendous interclass variety of melanomas, the severe level of visual comparability among melanoma and non-melanoma injuries, and the current of numerous ancient rarities in the picture. The authors propose an improved technique for melanoma acknowledgment for profound convolutional neural networks (CNNs) to address these difficulties. With existing strategies utilizing low-level hand-created highlights or CNNs with shallower designs, this essentially more profound organizations can accomplish more extravagant and discriminative elements for more acknowledgment. To make the most of deep organizations, the authors propose many viable preparation and learning plans under restricted information. The authors apply the leftover figuring out how to adapt to the debasement and overfitting issues that happen when an organization goes further. Analyzed the texture features of a region within the skin lesion boundary. The results obtained from the technique are also compared.
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Chapter 16
Biometric Identification System Using Neuro and Fuzzy Computational Approaches
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Chapter 626
Artificial Neural Networks Tutorial
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.
Published in Chapter: Artificial Neural Networks Tutorial; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 9
Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
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.
Published in Chapter: Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity; From: Handbook of Research on Knowledge and Organization Systems in Library and Information Science
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Chapter 2
Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network
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.
Published in Chapter: Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network; From: Handbook of Research on Smart Power System Operation and Control
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Chapter 39
Demographic Characterization of Heart Rate Variability (HRV)
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.
Published in Chapter: Demographic Characterization of Heart Rate Variability (HRV); From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 30
Automatic Classification of Impact-Echo Spectra I
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.
Published in Chapter: Automatic Classification of Impact-Echo Spectra I; From: Encyclopedia of Artificial Intelligence
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Chapter 8
Adaptive Neuro-Fuzzy Inference System in Agriculture
ANN represents a set of algorithms which are designed for pattern recognition. It is modelled after the human brain.
Published in Chapter: Adaptive Neuro-Fuzzy Inference System in Agriculture; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 8
Development of a Stop-Line Violation Detection System for Indian Vehicles
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.
Published in Chapter: Development of a Stop-Line Violation Detection System for Indian Vehicles; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 20
Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture
Artificial neural networks (ANNs) are a type of computing system that is inspired by biological neural networks present in the animal brain.
Published in Chapter: Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture; From: Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture
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Chapter 17
Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach
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.
Published in Chapter: Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach; From: Handbook of Research on Computational Science and Engineering: Theory and Practice
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Chapter 18
Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products
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.
Published in Chapter: Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 9
Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
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.
Published in Chapter: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 5
EM-Source Localization in Indoor Environments by Using an Artificial Neural Network Performance Assessment and Optimization
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.
Published in Chapter: EM-Source Localization in Indoor Environments by Using an Artificial Neural Network Performance Assessment and Optimization; From: Handbook of Research on Advanced Trends in Microwave and Communication Engineering
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Chapter 5
A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network
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.
Published in Chapter: A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network; From: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
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Chapter 28
Computer Intelligence in Healthcare
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.
Published in Chapter: Computer Intelligence in Healthcare; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 12
Mind Uploading in Artificial Intelligence
A machine learning method inspired by the biological neural networks that constitute animal brains. It consists of a series of algorithms that discover underlying patterns in a dataset through a process that mimics the way the human brain operates.
Published in Chapter: Mind Uploading in Artificial Intelligence; From: Philosophy of Artificial Intelligence and Its Place in Society
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Chapter 1
Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System
A model inspired by the biological neural networks and a subset of machine learning to mimic the operations of neurons in the human brain.
Published in Chapter: Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System; From: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
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Chapter 1
Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
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.
Published in Chapter: Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers; From: Automated Systems in the Aviation and Aerospace Industries
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Chapter 18
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
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.
Published in Chapter: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 4
Machine Learning in Healthcare: Introduction and Real-World Application Considerations
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.
Published in Chapter: Machine Learning in Healthcare: Introduction and Real-World Application Considerations; From: Quality Assurance in the Era of Individualized Medicine
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Chapter 107
Computer-Aided Diagnosis of Cardiac Arrhythmias
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.
Published in Chapter: Computer-Aided Diagnosis of Cardiac Arrhythmias; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 14
The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance
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.
Published in Chapter: The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance; From: Handbook of Research on New Challenges and Global Outlooks in Financial Risk Management
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Chapter 9
Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry
ANN seeks to mimic the network of neurons that make up the human brain, allowing the computer to learn and make decisions in a human-like manner. Traditional programming computers create ANNs that operate like interconnected brain cells.
Published in Chapter: Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 8
Systematic Literature Review: XAI and Clinical Decision Support
Consists of a layer of input nodes and a layer of output nodes, connected by one or more layers of hidden nodes. Input layer nodes pass information to hidden layer nodes by firing activation functions, and hidden layer nodes fire or remain dormant depending on the evidence presented. The hidden layers apply weighting functions to the evidence, and when the value of a particular node or set of nodes in the hidden layer reaches some threshold, a value is passed to one or more nodes in the output layer.
Published in Chapter: Systematic Literature Review: XAI and Clinical Decision Support; From: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
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Chapter 13
Recent Studies and Research on Sickle Cell Disease: Statistical Analysis and Machine Learning Approach
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Chapter 101
Fairness Challenges in Artificial Intelligence
ANNs are a class of machine learning algorithms and are at the heart of deep learning. ANNs are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer.
Published in Chapter: Fairness Challenges in Artificial Intelligence; From: Encyclopedia of Data Science and Machine Learning
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Chapter 2
Convolutional Neural Network
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.
Published in Chapter: Convolutional Neural Network; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 2
Deep Learning on Edge: Challenges and Trends
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.
Published in Chapter: Deep Learning on Edge: Challenges and Trends; From: Smart Systems Design, Applications, and Challenges
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Chapter 2
Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method
A machine learning algorithm that performs “black box” non-linear fitting, similarly to the inter-connected neurons in human brain.
Published in Chapter: Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method; From: Handbook of Research on Power and Energy System Optimization
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Chapter 4
Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence
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.
Published in Chapter: Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence; From: Advanced Robotics and Intelligent Automation in Manufacturing
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Chapter 34
Basic Cellular Neural Networks Image Processing
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.
Published in Chapter: Basic Cellular Neural Networks Image Processing; From: Encyclopedia of Artificial Intelligence
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Chapter 8
IoT-Based Health Risk Prediction by Collecting and Analyzing HIIT Data in Real Time Using Edge Computing
Artificial neural networks (ANNs) use learning algorithms that can modify or learn on their own when new information is received. As a result, they're an excellent tool for non-linear statistical data modeling.
Published in Chapter: IoT-Based Health Risk Prediction by Collecting and Analyzing HIIT Data in Real Time Using Edge Computing; From: Convergence of Big Data Technologies and Computational Intelligent Techniques
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Chapter 16
Arrhythmia Detection and Classification Using Wavelet and ICA
An interconnected group of artificial neurons that uses a mathematical model, or computational model, for information processing, based on a connectionist approach to computation.
Published in Chapter: Arrhythmia Detection and Classification Using Wavelet and ICA; From: Encyclopedia of Healthcare Information Systems
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Chapter 184
Reverse Logistics Design with Neural Networks
Neural network is a system which works as human brain and learn from past experiences. It is mostly used to solve complex, uncertain problems.
Published in Chapter: Reverse Logistics Design with Neural Networks; From: Encyclopedia of Business Analytics and Optimization
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Chapter 6
Image Processing and Post-Data Mining Processing for Security in Industrial Applications: Security in Industry
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.
Published in Chapter: Image Processing and Post-Data Mining Processing for Security in Industrial Applications: Security in Industry; From: Handbook of Research on Intelligent Data Processing and Information Security Systems
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Chapter 10
U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression
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.
Published in Chapter: U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 14
Artificial Intelligence a Driver for Digital Transformation
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.
Published in Chapter: Artificial Intelligence a Driver for Digital Transformation; From: Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy
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Chapter 7
Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
A computational model similar to the structure and functions of biological neural networks.
Published in Chapter: Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture; From: Modern Techniques for Agricultural Disease Management and Crop Yield Prediction
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Chapter 119
Intelligent Systems to Support Human Decision Making
Artificial intelligence method that is composed of a collection of highly interconnected processing units called neurons that are used together to solve a problem.
Published in Chapter: Intelligent Systems to Support Human Decision Making; From: Encyclopedia of Business Analytics and Optimization
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Chapter 21
Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance
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.
Published in Chapter: Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 5
Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)
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.
Published in Chapter: Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM); From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 19
Learning Aided Digital Image Compression Technique for Medical Application
Is non-parametric tool that learns from the surroundings, retains the learning and uses it subsequently.
Published in Chapter: Learning Aided Digital Image Compression Technique for Medical Application; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 5
Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments
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.
Published in Chapter: Robotics E-Learning Supported by Collaborative and Distributed Intelligent Environments; From: Revolutionizing Education in the Age of AI and Machine Learning
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Chapter 16
Classification of Product Backlog Items in Agile Software Development Using Machine Learning
An artificial neural network computation technique creates several processing units based on interconnected connections. It consists of various processing components that process inputs and produce outputs in accordance with predetermined activation functions.
Published in Chapter: Classification of Product Backlog Items in Agile Software Development Using Machine Learning; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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