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What is Artificial Neural Network (ANN)

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.
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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)
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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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Advanced Analytics and Machine Learning Algorithms for Healthcare Decision Support Systems: A Study
A computational version stimulated through the structure and functioning of biological neural networks, together with interconnected nodes (neurons) arranged in layers. ANNs can become aware of patterns by analyzing complicated relationships among inputs and outputs.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
A computational model similar to the structure and functions of biological neural networks.
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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.
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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.
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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.
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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.
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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.
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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.
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