scholarly journals An interactive tool for semi-automatic feature extraction of hyperspectral data

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Zoltán Kovács ◽  
Szilárd Szabó
Keyword(s):  
Spectral Data ◽  
Spectral Reflectance ◽  
Regression Models ◽  
Hyperspectral Data ◽  
Spectral Indices ◽  
Automatic Feature Extraction ◽  
Attribute Data ◽  
Data Analyst ◽  
Different Types ◽  
Qualitative Variables

AbstractThe spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in – Hyperspectral Data Analyst (HypDA) – for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing;

scholarly journals Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Chenjie Lin ◽  
Yueming Hu ◽  
Zhenhua Liu ◽  
Yiping Peng ◽  
Lu Wang ◽  
...  
Keyword(s):  
Spectral Data ◽  
Spectral Reflectance ◽  
Regional Scale ◽  
Hyperspectral Data ◽  
Gradient Boosting ◽  
Cultivated Land ◽  
Least Squares Regression ◽  
Land Quality ◽  
Land Protection ◽  
Dry Soil

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

scholarly journals Potential of Root Crops as Source of Electrical Energy

2013 ◽  
pp. 22-39
Author(s):  
Daniel Leslie Tan ◽  
Julie Tan ◽  
Mark Anthony Atanacio ◽  
Ruel Delantar
Keyword(s):  
Fuel Cell ◽  
Microbial Fuel Cell ◽  
Regression Models ◽  
Energy Recovery ◽  
Electrical Energy ◽  
Galvanic Cell ◽  
Waste Waters ◽  
Light Emitting ◽  
Root Crops ◽  
Different Types

Energy from edible and inedible root crop roots and tubers using galvanic cell and processing waste waters through microbial fuel cell (MFC) technology was harnessed. Electrolyte in the roots and tubers was tapped for galvanic cell and the microorganisms from waste waters act as catalyst in MFC. In galvanic cell, the optimized responses of badiang, cassava and sweetpotato were greatly affected by the surface area and distance between anode and cathode electrodes. An increase of nata-de-coco membrane size in MFC increased the voltage and current by 4.94 and 11.71 times, respectively. Increasing the width of anode also enhanced the responses. Different types of microorganisms were isolated from the biofilm anode of MFC. Their growth and proliferation which corresponded to the generation of electricity were also demonstrated in this study. A total of 54 bacterial isolates were collected from the biofilm at the anode of single-chamber MFC (SCMFC). The generated electricity observed using light emitting diodes (LED) showed potential both for galvanic and microbial fuel cell. The generated regression models are reliable tools in predicting desired outputs for future applications. These promising results demonstrated basic information on the electrical energy recovery from rootcrop waste waters and roots/tubers.

Fear in Bureaucracy: Comparing Public and Private Sector Workers’ Expectations of Punishment

2018 ◽  
Vol 52 (2) ◽  
pp. 233-264 ◽  
Author(s):  
Jiwon Jung ◽  
Barry Bozeman ◽  
Monica Gaughan
Keyword(s):  
Regression Models ◽  
Robust Regression ◽  
Risk Propensity ◽  
Government Employees ◽  
Public And Private ◽  
Different Types ◽  
Major Predictor ◽  
New Ideas ◽  
Public And Private Sector ◽  
Organizational Rules

When employees fear punishment for taking initiative, organizations are likely to be less effective and, equally important, such fear extracts a human toll, often contributing to a variety of manifestations of unhappiness including diminished health. We focus on two different types of fears of punishment, fear of being punished for presenting new ideas and for bending organizational rules. Employing Mechanical Turk crowdsourcing data from 1,189 participants in the 2015 survey of National Administrative Studies Project Citizen, we test hypotheses about possible differences in fear of punishment according to sector (government vs. business), general risk propensity, views about coworkers, job clarity, gender, and whether respondents are members of an underrepresented racial or ethnic minority. Using nested robust regression models, we find that the two different types of fear of punishment are predicted by different variables. Sector has no bearing on fear of punishment for presenting new ideas but is a major predictor of differences in fear of bending the rules, with government employees being more fearful. While gender has no significant effects, being a racial minority is closely related to fear of presenting new ideas. Having a negative view of one’s fellow workers, particularly one’s supervisor, is associated with greater fear of punishment from both rule bending and presenting new ideas. Those with a clear organization mission and job clarity are less likely to be afraid of punishment for proposing innovative ideas but not necessarily for bending rules. We suggest that the results have implications for managerial practice and human resource reform.

MATHEMATICAL MODELING OF TELEROENTGENOGRAPHIC PARAMETERS ACCORDING TO THE METHOD OF SCHWARZ A. M. DEPENDING ON THE BASIC CEPHALOMETRIC PARAMETERS IN UKRAINIAN YOUNG MEN AND YOUNG WOMEN WITH DIFFERENT FACE TYPES

2021 ◽  
Vol 74 (6) ◽  
pp. 1488-1492
Author(s):  
Аlla V. Маrchenko ◽  
Oleksandr S. Prokopenko ◽  
Іryna V. Dzevulska ◽  
Tatyana R. Zakalata ◽  
Igor V. Gunas
Keyword(s):  
Young Women ◽  
Regression Models ◽  
Young Men ◽  
Cone Beam ◽  
Coefficient Of Determination ◽  
Normal Occlusion ◽  
Different Types ◽  
Dental Intervention ◽  
Individualized Approach ◽  
Facial Types

The aim: Is development and analysis of regression models of teleroentgenographic indices according to Schwarz A. M., which can be adjusted during surgery depending on the parameters that usually do not change in Ukrainian young men and young women with with normal occlusion close to orthognathic occlusion and different facial types. Materials and methods: Teleroentgenographic indices were obtained using a dental cone-beam tomograph Veraviewepocs 3D Morita and studied in 49 young men and 76 young women with normal occlusion close to orthognathic. Persons were divided into groups with different face types according to the recommendations of Schwarz A. M. In the license package “Statistica 6.0”, regression models of teleroentgenographic indices were built according to Schwarz A. M. Results: For young men with orthognathic occlusion and with different types of faces according to Schwarz A. M. constructed 10 of 27 possible reliable regression models of the group of teleroentgenographic indicators, which can be corrected during surgical, orthopedic interventions in dentistry depending on the group of basic, invariable cephalometric indicators greater than 0.6 (R2 = from 0.609 to 0.996); and in young women with different face types, 8 of the 27 possible reliable regression models in which the coefficient of determination is greater than 0.6 (R2 = from 0.642 to 0.986). Conclusions: The developed regression models provide the most individualized approach in determining the method and scope of the required dental intervention.

Innovation in the aftermath of downsizing: evidence from the threat-rigidity perspective

2020 ◽  
Vol 49 (9) ◽  
pp. 1859-1877
Author(s):  
José Fernández-Menéndez ◽  
Óscar Rodríguez-Ruiz ◽  
José-Ignacio López-Sánchez ◽  
María Isabel Delgado-Piña
Keyword(s):  
Regression Models ◽  
Negative Impact ◽  
Content Type ◽  
Logistic Regression Models ◽  
Marketing Innovation ◽  
Different Types ◽  
Threat Rigidity ◽  
The Impact ◽  
Research Show ◽  
Innovation Outcomes

PurposeThe purpose of this paper is to study how job reductions affect product innovation and marketing innovation in a sample of 2,034 Spanish manufacturing firms in the period 2007–2014.Design/methodology/approachPoisson and logistic regression models with random effects were used to analyse the impact of downsizing on some innovation outcomes of firms.FindingsThe results of this research show that the stressful measure of job reductions may have unexpected consequences, stimulating innovation. However downsizing combined with radical organisational changes such as new equipment, techniques or processes seems to have a negative impact on product and marketing innovation.Originality/valueThis research has two original features. First, it explores the unconventional direction of causality from the planned elimination of jobs to innovation outputs. Secondly, the paper looks at the combined effect of downsizing and other restructuring measures on different types of innovation. Following the threat-rigidity theory, we assume that this combination represents a major threat for survivors that leads to lower levels of product and marketing innovation.

scholarly journals Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

2019 ◽  
Vol 11 (22) ◽  
pp. 2605 ◽  
Author(s):  
Wang ◽  
Chen ◽  
Wang ◽  
Li
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Soil Salinity ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Percentage Error ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Data Noise ◽  
Salt Affected Soil

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

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