Redes neuronales (RN) aplicadas a la valoración de locales comerciales

Tabales, Julia Margarita Núñez; Rey-Carmona, Francisco José; Ocerin, J. M.a Caridad y

doi:10.3989/ic.15.053

Cited by 11 publications

(10 citation statements)

References 13 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ANN presents a better degree of adjustment than other extrapolate methodologies such as hedonic pricing method, as stated in [62,63]. Although in these last studies, ANN is implemented in price determination, ANNs of MLP type had been also used in other kinds of estimations based on surveys [64][65][66].…”

Section: Rumelhart and Mcclellandmentioning

confidence: 99%

Peer-to-Peer Tourism: Tourists’ Profile Estimation through Artificial Neural Networks

Cuadra

Sánchez

López‐Guzmán

et al. 2021

JTAER

View full text Add to dashboard Cite

Peer-to-peer tourism is one of the great global trends that is transforming the tourism sector, introducing several changes in many aspects of tourism, such as the way of travelling, staying or living the experience in the destination. This research aims to determine the relationship between the sociodemographic characteristics of tourists interested in peer-to-peer accommodation and the importance they give to various motivational factors about this type of tourism in a “cultural-tourism” city. The methodology used in this research is an artificial neural network of the multilayer perceptron type to estimate a sociodemographic profile of the peer-to-peer accommodation tourist user based on predetermined input values consisting of the answers to the Likert-type questions previously carried out using a questionnaire. Thus, the model developed, through a customized set of answers to these questions, allows the presentation of a “composite picture” of a peer-to-peer tourist based on sociodemographic characteristics. This function is especially interesting for adapting the peer-to-peer hosting offer according to the preferences of potential users.

show abstract

Section: Rumelhart and Mcclellandmentioning

confidence: 99%

Peer-to-Peer Tourism: Tourists’ Profile Estimation through Artificial Neural Networks

Cuadra

Sánchez

López‐Guzmán

et al. 2021

JTAER

View full text Add to dashboard Cite

show abstract

“…Los resultados muestran un R 2 de 0,93 y errores absolutos medios muy bajos, se concluye que las RNA predicen de forma más precisa los precios de los inmuebles en comparación con los métodos tradicionales. Así mismo, en [52] se busca obtener un modelo basado en Perceptrón Multicapa de 5 neuronas en la capa de entrada, 6 neuronas en la capa oculta y una neurona en la capa de salida, para predecir el precio mensual de alquiler de manera automatizada en la ciudad de Córdova, España. Los resultados muestran un R de 0,9352, con lo que se concluyó que las RNA predicen el precio del inmueble de acuerdo con la realidad del mercado.…”

Section: Redes Neuronales Artificiales Aplicadas a La Valorización De Inmueblesunclassified

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Purizaca

Barturen

Pérez

et al. 2020

ITECKNE

View full text Add to dashboard Cite

Artificial neural networks (ANN) have a relevant role nowadays; several areas apply this technique due to the advantages they have to solve complex problems with many constraints compared to traditional methods, which are becoming outdated. Very little is known about this technique and its application in different branches of Civil Engineering. For this reason, the present research aims to conduct a systematic review of the literature to identify the use of this technique and to determine the results of the application of ANN models in civil engineering. A total of 41 scientific articles were included, distributed as follows: 6 in Scopus, 1 in ScienceDirect, 23 in ProQuest, 7 in Google Scholar, 2 in DialNet, 2 in SciELO. It was found that ANNs are used to predict or forecast variables associated with the fields of study in civil engineering; 8 applications of ANN were found for concrete properties, 11 for soil properties, 5 for seismic analysis, 9 for hydraulics, 7 for real estate valuation and 1 for bridge design. Likewise, it was found that the multilayer Perceptron is the most used ANN model, achieving an average R2 of 0.99, which shows advantages to solve problems with precision, in shorter times, with missing data in the data sets, as well as the reduction of the error factor.

show abstract

“…Para viviendas urbanas en grandes y medianas ciudades el factor más influyente es la superficie total construida seguida de la ubicación y en tercer lugar la antigüedad (18), (19). Para locales comerciales también prevalece la superficie sobre el resto de variables, destacando en segundo lugar el índice de conservación y en tercer lugar la ubicación (33).…”

Section: Conclusiones Y Discusiónunclassified

Estimación de los costes de construcción de viviendas rústicas mediante Redes Neuronales Artificiales

2019

View full text Add to dashboard Cite

La Administración Pública y el promotor privado buscan tener un conocimiento previo de los costes de cualquier edificación. Existen en la literatura numerosas metodologías que permiten realizar la valoración de un inmueble, pero siempre se ha realizado para viviendas urbanas en ciudades grandes o medianas. Esta investigación propone el uso de Inteligencia Artificial para el estudio de viviendas rústicas en pequeñas ciudades como la de Cáceres (España). La investigación propone un procedimiento de Redes Neuronales Artificiales (RNA) para conseguir por un lado estimar a través de un método automático el coste de construcción de la vivienda rústica y por otro, identificar los atributos más determinantes en su precio final. La RNA diseñada establece como variables más influyentes en el precio final del inmueble los espacios húmedos (baños y cocina), la superficie construida y la antigüedad por este orden, diferenciándose de las variables más determinantes en el precio de las viviendas urbanas en grandes o medianas ciudades que son la superficie construida y su ubicación.

show abstract

Redes neuronales (RN) aplicadas a la valoración de locales comerciales

Cited by 11 publications

References 13 publications

Peer-to-Peer Tourism: Tourists’ Profile Estimation through Artificial Neural Networks

Peer-to-Peer Tourism: Tourists’ Profile Estimation through Artificial Neural Networks

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Estimación de los costes de construcción de viviendas rústicas mediante Redes Neuronales Artificiales

Contact Info

Product

Resources

About