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Maylin Acosta, Isabel Rodríguez-Carretero, José Blasco, José Miguel de Paz and Ana Quiñones
Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500?980 nm range over 6...
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Peter Krammer, Marcel Kvassay, Ján Moj?i?, Martin Kenyeres, Milo? Ockay, Ladislav Hluchý, Lubo? Pavlov and Lubo? Skurcák
This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent vario...
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Camille Champion, Anne-Claire Brunet, Rémy Burcelin, Jean-Michel Loubes and Laurent Risser
In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularizatio...
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Young-Rong Kim, Min Jung and Jun-Bum Park
As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operationa...
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Jin Li, Justy Siwabessy, Zhi Huang and Scott Nichol
Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics,...
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Mariano Maisonnave, Fernando Delbianco, Fernando Abel Tohmé, Ana Gabriela Maguitman
Pág. 61 - 80
Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables. This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents la...
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Massimo Guidolin and Manuela Pedio
In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchm...
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Innocent Mudhombo and Edmore Ranganai
Although the variable selection and regularization procedures have been extensively considered in the literature for the quantile regression (????)
(
Q
R
)
scenario via penalization, many such procedures fail to deal with data aberrations in the design ...
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Loann David Denis Desboulets
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Zhizhou Wu, Yunyi Liang
Pág. 1745 - 1754
Variable Message Sign (VMS) location has impacts on the total travel time of transportation network. However, drivers with various characters have different understanding on each route travel time under information conveyed through VMS, which leads to di...
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Hou-Cheng Yang, Guanyu Hu and Ming-Hui Chen
Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes. In this paper, we propose Bayesian ...
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Vera Afreixo, Ana Helena Tavares, Vera Enes, Miguel Pinheiro, Leonor Rodrigues and Gabriela Moura
In this work, we aimed to establish a stable and accurate procedure with which to perform feature selection in datasets with a much higher number of predictors than individuals, as in genome-wide association studies. Due to the instability of feature sel...
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Alessandro Araldi
Over the last two decades, a growing number of works in urban studies have revealed how micro-retail distribution is significantly related to specific properties of the urban built environment. While a wide variety of urban form measures have been invest...
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Yuna Shin, Taekgeun Kim, Seoksu Hong, Seulbi Lee, EunJi Lee, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Jungsu Park and Tae-Young Heo
Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Fo...
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Siyao Yu, Haoran Bu, Xue Hu, Wancheng Dong and Lixin Zhang
In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative dete...
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Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma and Stuart Phinn
Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been devel...
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Abdelkrim Lachgar, David J. Mulla and Viacheslav Adamchuk
One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to ...
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Qingcheng Fan, Sicong Liu, Chunjiang Zhao and Shuqin Li
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we c...
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Wasiaturrahma Wasiaturrahma, Hilda Rohmawati
Pág. 54 - 69
The demand for tourism in Indonesia continues to increase every year but cannot reach thepredetermined target. Studies on tourism demand have been done a lot, especially in Indonesia.The selection of the dependent variable in tourism demand is not proble...
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Irene Chrysafis, Georgios Korakis, Apostolos P. Kyriazopoulos and Giorgos Mallinis
Leaf area index (LAI) is a crucial biophysical indicator for assessing and monitoring the structure and functions of forest ecosystems. Improvements in remote sensing instrumental characteristics and the availability of more efficient statistical algorit...
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