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What is Neural Networks

Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
AI method that mimics the function of the brain, by forming a layered network of artificial neurons organized in different layers (input, hidden, and output layers).
Published in Chapter:
Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
André S. Fialho (Massachusetts Institute of Technology, USA), Federico Cismondi (Massachusetts Institute of Technology, USA), Susana M. Vieira (Technical University of Lisbon, Portugal), Shane R. Reti (Harvard University, USA), João M. C. Sousa (Technical University of Lisbon, Portugal), and Stan N. Finkelstein (Massachusetts Institute of Technology, USA)
DOI: 10.4018/978-1-4666-3990-4.ch016
Abstract
During the last decade, modern hospitals have witnessed a growth in the amount of information acquired, stored, and retrieved more than ever before. While aimed at helping healthcare personnel in providing care to patients, this high stream of data can also have a negative impact if not delivered in a simple and organized way. In this chapter, the authors explore the current opportunities and challenges that soft computing predictive tools face in healthcare delivery, and they then present an example of how some of these tools may contribute to the decision-making of health care providers for an important critical condition in Intensive Care Units (ICU)—septic shock. Despite current challenges, such as the availability of clean clinical data, accuracy, and interpretability, these systems will likely act to enhance the performance of a human expert and permit healthcare resources to be used more efficiently while maintaining or improving outcomes.
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Challenges and Opportunities of Soft Computing Tools in Health Care Delivery
AI method that mimics the function of the brain, by forming a layered network of artificial neurons organized in different layers (input, hidden, and output layers).
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Time Series Forecasting in Retail Sales Using LSTM and Prophet
Biological-inspired computational models that mimic the brain structure with simple processing units organized in layers that are highly connected.
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Crossroads Between Cognitive Connectomics and Sociomics: Synergies and Squabbles Amidst Two Omics
Systems of neurons, either organic or artificial in nature. They are a series of algorithms that endeavor to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
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An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates
Biologically inspired computing system based on the neural structure of the brain.
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Artificial Intelligence and Investing
Programs that simulate a network of communicating nerve cells to achieve a machine learning objective
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The Summers and Winters of Artificial Intelligence
Network system, modeled after the human brain, capable of learning from data.
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Applying Artificial Intelligence to Financial Investing
Programs that simulate a network of communicating nerve cells to achieve a machine learning objective.
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An Efficient Learning of Neural Networks to Acquire Inverse Kinematics Model
Are typically composed of multiple layers and the signal traverses from the input layer to the output layer of neurons. Trained neural networks can approximate an arbitrary nonlinear function.
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PMA Supplier Selection Using the Mahalanobis Taguchi System
A network of computational nodes which are interconnected by links. Instead of capturing rules like an expert system does, neural network relies on using input/output data patterns to train the network. Its links are modified to capture the knowledge, so that after it has been adequately trained, it can provide appropriate responses to new input data.
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Analysis of Large-Scale OMIC Data Using Self Organizing Maps
Are thought to form the basal structure enabling brain function. Artificial neural networks adopt those wiring structures to solve problems in machine learning and pattern recognition on computers.
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Artificial Intelligence and Machine Learning Algorithms
It includes machine learning with deep learning methods that utilizes huge amounts of training data to detect association among various variables.
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Conditional Hazard Estimating Neural Networks
A graphical representation of a nonlinear function. Usually represented as a directed acyclic graph. 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.
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Neural Networks for Retail Sales Forecasting
Computing systems that are composed of many simple processing elements operating in parallel whose function is determined by network structure. They are used mainly to model functional relationship among many variables.
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Artificial Intelligence and Investing
Programs that simulate a network of communicating nerve cells to achieve a machine learning objective.
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Applications of Data Mining in the Healthcare Industry
Also referred to as artificial intelligence (AI), which utilizes predictive algorithms.
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Using Self Organizing Maps for Banking Oversight: The Case of Spanish Savings Banks
A powerful set of algorithms whose objective is to find a pattern of behavior. They are called neural because they are based on how biological neurons work when processing information. These networks try to simulate the way the neural network of a live being processes, recognizes and transmits the information. The implementation of neural networks in very different fields is due to their good performance relative to other methods
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Role of Artificial Intelligence in Cyber Security: A Useful Overview
It is inspired by the neural networks present in the humans and later led to Artificial Neural Networks (ANNs) which are used in Deep Learning. Neural Networks rely on data to learn and improve their accuracy but once the algorithm is finely tuned, it can be used to classify data at a very high speed.
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Prediction of Major Earthquakes as Rare Events Using RF-Typed Polynomial Neural Networks
Generally known as Artificial Neural Networks. Computational models that are built as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network.
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Data Mining Fundamental Concepts and Critical Issues
Also referred to as artificial intelligence (AI), which utilizes predictive algorithms.
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Preference Reversal Under Vulnerability: An Application of Neural Networks in Mexican Family Firms
Computational models, inspired in biological networks, that learn and make a prediction based on algorithms.
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Data Mining: Payoffs and Pitfalls
Also referred to as artificial intelligence (AI), which utilizes predictive algorithms.
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Generative AI in Higher Education
Inspired by the human brain, these are a series of algorithms that mimic the operations of the human brain to recognize patterns and solve common problems in AI, such as pattern recognition.
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Attention Facilitation via Multimedia Stimulation
A black-box nonlinear model whose main characteristics are to be composed by many nonlinear elements of the same simple kind composed in a regular structure whose parameters are identified from examples of data.
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Walking the Information Overload Tightrope
Also called “neural nets,” they are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units.
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A Comprehensive Review on AI Techniques for Healthcare
It is a bunch of connected nodes called neurons, which mimic the model of neurons like a biological brain.
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Watermarking Using Artificial Intelligence Techniques
It is an art of imitating human brain activities through artificial intelligence.
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Voice and Speech Recognition Application in Emotion Detection: A Utility for Future Trends
A neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
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Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature
A neural network is a type of artificial intelligence model that is inspired by the structure and function of the human brain. It is a collection of interconnected nodes or “neurons” that process and transmits information, allowing the network to learn from examples and make predictions or decisions based on input data. Neural networks can be trained to perform a variety of tasks, such as image recognition, speech recognition, and natural language processing.
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Applying Artificial Intelligence to Financial Investing
Programs that simulate a network of communicating nerve cells to achieve a machine learning objective.
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Neural Networks to Solve Nonlinear Inverse Kinematic Problems
Typically composed of multiple layers and the signal traverses from the input layer to the output layer of neurons. Trained neural networks can approximate an arbitrary nonlinear function.
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A Meta-Analytical Review of Deep Learning Prediction Models for Big Data
It is a set of algorithm that is used for recognizing the relationship between set of data by means of learning process.
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Adaptive Swarm Coordination and Formation Control
A set of artificial neurons with tunable gains, which are linked to estimate unknown functions or parameters.
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Advanced Methodologies Descriptions and Applications
A highly interconnected set of neurons that form a brain in animals and is often emulated in computer programs because of their power.
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A Bayesian Network Model for Probability Estimation
A class of machine learning algorithm that works on the structure of biological nervous systems. A class of machine learning algorithm consisting of multiple nodes that communicate through their connecting synapses.
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Machine Learning
Learning models based on the structure and processing of the nervous system.
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Artificial Intelligence (AI)-Integrated Biosensors and Bioelectronics for Agriculture
These are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed for machine learning tasks, utilizing algorithms to learn patterns and make predictions or classifications from data. They excel in tasks like image and speech recognition, natural language processing, and other complex pattern recognition problems.
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From Analysis to Estimation of User Behavior
A neural network is “a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use.” (Haykin, 1994).
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