Stacked Ensemble Model for the Automatic Valuation of Residential Properties in South Korea: A Case Study on Jeju Island

Autor: Woosung Kim, Jengei Hong

Publikováno v: Land, Vol 13, Iss 9, p 1436 (2024)

While the use of machine learning (ML) in automated real estate valuation is growing, research on stacking ML models into ensembles remains limited. In this paper, we propose a stacked ensemble model for valuing residential properties. By applying ou

Externí odkaz: https://doaj.org/article/9249989676ec454982df4f5b3da31afc

Stacking Approaches for the design of Soft Sensors using small data set

Autor: Salvatore Graziani, G. Napoli, Maria Gabriella Xibilia, A. Di Bella

In this paper a number of approaches to design a soft sensor for an industrial plant in case of small data set are compared. In particular different strategies to aggregate suboptimal models obtained by bootstrapped neural networks and noise injectio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9dbfb72c0dd1fd3070b705b225d74b97
http://hdl.handle.net/11570/1893466

Data driven models for a PEM fuel cell stack performance prediction

Autor: Francesco Sergi, G. Brunaccini, Vincenzo Antonucci, Marco Ferraro, G. Napoli

Publikováno v: International journal of hydrogen energy 38 (2013): 11628–11638. doi:10.1016/j.ijhydene.2013.04.135
info:cnr-pdr/source/autori:Napoli G.; Ferraro M.; Sergi F.; Brunaccini G.; Antonucci V./titolo:Data driven models for a PEM fuel cell stack performance prediction/doi:10.1016%2Fj.ijhydene.2013.04.135/rivista:International journal of hydrogen energy/anno:2013/pagina_da:11628/pagina_a:11638/intervallo_pagine:11628–11638/volume:38

The operating principles of polymer electrolyte membrane (PEM) fuel cells system involve electrochemistry, thermodynamics and hydrodynamics theory for which it is not always easy to establish a mathematical model. In this paper two different methods

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7a5511aab92b2bf8a539a1a610f6819
http://www.cnr.it/prodotto/i/286522

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