scholarly journals Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

2018 ◽  
Vol 42 (0) ◽  
Author(s):  
Wesly Jeune ◽  
Márcio Rocha Francelino ◽  
Eliana de Souza ◽  
Elpídio Inácio Fernandes Filho ◽  
Genelício Crusoé Rocha
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Multinomial Logistic Regression ◽  
Digital Mapping

scholarly journals East Slavic indefinite pronouns: a corpus-based approach

2021 ◽  
Author(s):  
Yana Penkova ◽  
Achim Rabus
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Statistical Methods ◽  
Multinomial Logistic Regression ◽  
Random Forest Analysis ◽  
Functional Distribution ◽  
Different Sources ◽  
National Corpus ◽  
Indefinite Pronouns

AbstractThe paper focuses on the development and functional distribution of indefinite pronouns in Old East Slavic, taking into account different sources, genres and registers. All the examples in the collected dataset were taken from the historical modules of the Russian National Corpus. They were tagged for type of indefinite marker, source (including originality and date), type of reference of the indefinite marker, semantics, type of discourse, and the degree of formality (formal or informal) present in the context. We then applied both descriptive and inferential statistical methods such as Random Forest analysis as well as multinomial logistic regression. Our analysis enabled us to identify the primary and secondary predictors of the choice of a particular indefinite marker and to trace the functional distribution of indefinite markers according to these factors.

scholarly journals Statistical Analysis for Selective Identifications of VOCs by Using Surface Functionalized MoS2 Based Sensor Array

2021 ◽  
Vol 5 (1) ◽  
pp. 35
Author(s):  
Uttam Narendra Thakur ◽  
Radha Bhardwaj ◽  
Arnab Hazra
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Sensor Array ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Breath

Disease diagnosis through breath analysis has attracted significant attention in recent years due to its noninvasive nature, rapid testing ability, and applicability for patients of all ages. More than 1000 volatile organic components (VOCs) exist in human breath, but only selected VOCs are associated with specific diseases. Selective identification of those disease marker VOCs using an array of multiple sensors are highly desirable in the current scenario. The use of efficient sensors and the use of suitable classification algorithms is essential for the selective and reliable detection of those disease markers in complex breath. In the current study, we fabricated a noble metal (Au, Pd and Pt) nanoparticle-functionalized MoS2 (Chalcogenides, Sigma Aldrich, St. Louis, MO, USA)-based sensor array for the selective identification of different VOCs. Four sensors, i.e., pure MoS2, Au/MoS2, Pd/MoS2, and Pt/MoS2 were tested under exposure to different VOCs, such as acetone, benzene, ethanol, xylene, 2-propenol, methanol and toluene, at 50 °C. Initially, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to discriminate those seven VOCs. As compared to the PCA, LDA was able to discriminate well between the seven VOCs. Four different machine learning algorithms such as k-nearest neighbors (kNN), decision tree, random forest, and multinomial logistic regression were used to further identify those VOCs. The classification accuracy of those seven VOCs using KNN, decision tree, random forest, and multinomial logistic regression was 97.14%, 92.43%, 84.1%, and 98.97%, respectively. These results authenticated that multinomial logistic regression performed best between the four machine learning algorithms to discriminate and differentiate the multiple VOCs that generally exist in human breath.

scholarly journals Digital Mapping of Soil Classes Using Ensemble of Models in Isfahan Region, Iran

Soil Systems ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 37 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Budiman Minasny ◽  
Norair Toomanian ◽  
Mojtaba Zeraatpisheh ◽  
Alireza Amirian-Chakan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Area Under The Curve ◽  
Support Vector ◽  
Ensemble Model ◽  
Soil Maps ◽  
Soil Class ◽  
Digital Maps ◽  
Digital Mapping ◽  
The Individual

Digital soil maps can be used to depict the ability of soil to fulfill certain functions. Digital maps offer reliable information that can be used in spatial planning programs. Several broad types of data mining approaches through Digital Soil Mapping (DSM) have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses. We applied seven different techniques for the prediction of soil classes based on 194 sites located in Isfahan region. The mapping exercise aims to produce a soil class map that can be used for better understanding and management of soil resources. The models used in this study include Multinomial Logistic Regression (MnLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Bayesian Networks (BN), and Sparse Multinomial Logistic Regression (SMnLR). Two ensemble models based on majority votes (Ensemble.1) and MnLR (Ensemble.2) were implemented for integrating the optimal aspects of the individual techniques. The overall accuracy (OA), Cohen's kappa coefficient index (κ) and the area under the curve (AUC) were calculated based on 10-fold-cross validation with 100 repeats at four soil taxonomic levels. The Ensemble.2 model was able to achieve larger OA, κ coefficient and AUC compared to the best performing individual model (i.e., RF). Results of the ensemble model showed a decreasing trend in OA from Order (0.90) to Subgroup (0.53). This was also the case for the κ statistic, which was the largest for the Order (0.66) and smallest for the Subgroup (0.43). Same decrease was observed for AUC from Order (0.81) to Subgroup (0.67). The improvement in κ was substantial (43 to 60%) at all soil taxonomic levels, except the Order level. We conclude that the application of the ensemble model using the MnLR was optimal, as it provided a highly accurate prediction for all soil taxonomic levels over and above the individual models. It also used information from all models, and thus this method can be recommended for improved soil class modelling. Soil maps created by this DSM approach showed soils that are prone to degradation and need to be carefully managed and conserved to avoid further land degradation.

scholarly journals Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

2021 ◽  
Author(s):  
A. Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
A S Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Cartilage Repair ◽  
Multinomial Logistic Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cartilage Tissue ◽  
Random Forest Regression ◽  
Selection Of

Abstract Cartilage repair is one of the most challenging tasks for the orthopedic surgeons and researchers. The primary challenge lies on the fact that the development of the extracellular matrixes requires specialized cells known as chondrocytes which are sparse in numbers. Chondrocytes’ minimal self-renewal capacity makes it further troublesome and expensive to repair the cartilages. In designing successful substitutes for the cartilages, the selection of materials used for the scaffold fabrication plays the central role among several other important factors in order to ensure the success of the survival and proliferation of any biomaterial substitutes. Since last few decades, polymer and polymers' combination have been extensively used to fabricate such scaffolds and have shown promising results in terms of mechanical integrity and biocompatibility. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally, it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate their physical, mechanical and biological properties that might be suitable for cartilage tissue engineering. With the advancement of machine learning, material design may experience a significant reduction in experimental time and cost. The objective of this study is to implement an inverse design approach to select the best polymer(s) or composites for cartilage repair by using the machine learning algorithms, such as random forest regression (i.e., regression trees) and the multinomial logistic regression. In these algorithms, the mechanical properties of the polymers, which are similar to the cartilages, are considered as the input and the polymer(s)/composites are the predicted output. According to the random forest regression and multinomial logistic regression, the polymer(s)/composites (i.e., the output) having the closest characteristics of the articular cartilages were found to be a composite of polycaprolactone and poly(bisphenol A carbonate) and a blend of polyethylene/polyethylene-graft-poly(maleic anhydride), respectively. These composites exhibit similar biomechanical properties of the natural cartilages and initiate only minimal immune responses in the body environment.

Public knowledge, perception and communication behavior surrounding COVID-19 in Malaysia

2020 ◽  
Author(s):  
Khayriyyah Mohd Hanafiah ◽  
Chang Da Wan
Keyword(s):  
Logistic Regression ◽  
Employment Status ◽  
Multinomial Logistic Regression ◽  
Movement Control ◽  
Education Level ◽  
Public Knowledge ◽  
Communication Behavior ◽  
Positive Communication ◽  
The Face ◽  
Control Order

The COVID-19 pandemic is the first to occur in an age of hyperconnectivity. This paper presents results from an online anonymous survey conducted in Malay, English, and Chinese, during the first week of the Movement Control Order in Malaysia (n=1075), which aimed to examine public knowledge, perception and communication behavior in the Malaysian society in the face of a sudden outbreak and social distancing measures. Although the level of public knowledge, risk perception and positive communication behavior surrounding COVID-19 was high, a majority of respondents reported receiving a lot of questionable information. Multinomial logistic regression further identified that responses to different items varied significantly across respondent survey language, gender, age, education level and employment status.

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