An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

Jove, Esteban; González-Cava, José M.; Casteleiro‐Roca, José‐Luis; Pérez, José Andrés Moreno; Calvo-Rolle, José Luís; Juez, Francisco Javier de Cos

doi:10.1007/978-3-319-67180-2_48

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…The Multilayer Perceptron (MLP) is one of the most frequently used shallow neural network architectures. The good performance of this kind of artificial neural network has been proven in similar works such as [31,32,33]. Previous research [34] proved how this technique is capable of providing satisfactory results in the context of much larger amounts of data than those used in the case of study.…”

Section: Fault Detection and Recovery (Fdr) Approach—used Techniquesmentioning

confidence: 79%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

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This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

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Section: Fault Detection and Recovery (Fdr) Approach—used Techniquesmentioning

confidence: 79%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The algorithm assigned each sample to the cluster whose centroid was closest. The Euclidean distance is the most typical one [37,43,44]. The algorithm only needs to know how many clusters it needs to create (the parameter K defined by the user), and the dataset.…”

Section: K-means Algorithmmentioning

confidence: 99%

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

et al. 2020

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This work deals with the prediction of variables for a hydrogen energy storage system integrated into a microgrid. Due to the fact that this kind of system has a nonlinear behaviour, the use of traditional techniques is not accurate enough to generate good models of the system under study. Then, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the power, the hydrogen level and the hydrogen system degradation. In this research, a hybrid intelligent model was created and validated over a dataset from a lab-size migrogrid. The achieved results show a better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption/generation with a mean absolute error of 0.63% with the test dataset respect to the maximum power of the system.

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“…An intelligent hybrid topology has been used to create the model. First, clustering [40][41][42] has been performed, with the objective of obtaining data groups with similar behavioral characteristics. Then, regression techniques [43][44][45][46] have been applied over each of the obtained groups.…”

Section: Introductionmentioning

confidence: 99%

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

et al. 2020

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Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.

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An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

Cited by 16 publications

References 23 publications

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

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