Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure

There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO2 was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.

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Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

Sustainability ◽

10.3390/su13031328 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1328

Author(s):

Young-Su Kim ◽

U-Yeol Park ◽

Seoung-Wook Whang ◽

Dong-Joon Ahn ◽

Sangyong Kim

Keyword(s):

Decision Making ◽

Urban Areas ◽

Construction Projects ◽

Retaining Wall ◽

Multidimensional Data ◽

Self Organizing Maps ◽

High Rise ◽

Average Accuracy ◽

Som Model ◽

Self Organizing

Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%.

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On building early-warning systems for preventing the deterioration of financial institutions’ performance

Proceedings of the International Conference on Applied Statistics ◽

10.2478/icas-2019-0017 ◽

2019 ◽

Vol 1 (1) ◽

pp. 194-202

Author(s):

Adrian Costea

Keyword(s):

Financial Institutions ◽

Early Warning ◽

Warning System ◽

Early Warning Systems ◽

Capital Adequacy ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Performance Dimensions ◽

Som Model ◽

Self Organizing

Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would accurately forecast the NFIs future performance (whether they would stay or be eliminated from the NFI’s Special Register three quarters into the future). The results are promising: the model is able to correctly predict NFIs’ performance movements. Finally, we compared the results of our SOM-based model with those obtained by applying a multivariate logit-based model. The SOM model performed worse in discriminating the NFIs’ performance: the performance classes were not clearly defined and the model lacked the interpretability of the results. In the contrary, the multivariate logit coefficients have nice interpretability and an individual default probability estimate is obtained for each new observation. However, we can benefit from the results of both techniques: the visualization capabilities of the SOM model and the interpretability of multivariate logit-based model.

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Growing Self-Organizing Maps for Data Analysis

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch116 ◽

2011 ◽

pp. 781-787

Author(s):

Soledad Delgado ◽

Consuelo Gonzalo ◽

Estíbaliz Martínez ◽

Águeda Arquero

Keyword(s):

Dimensional Space ◽

Simulated Data ◽

Multidimensional Data ◽

Self Organizing Maps ◽

Research Areas ◽

Multidimensional Datasets ◽

Cell Structures ◽

Growing Cell ◽

Growing Cell Structures ◽

Self Organizing

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen selforganizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.

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Algorithm for determining the optimum number of clusters on the basis of the Kohonen neural network

Connectivity ◽

10.31673/2412-9070.2020.031316 ◽

2020 ◽

Vol 145 (3) ◽

Author(s):

O. M. Tkachenko ◽

◽

N. V. Rudenko ◽

S. R. Kufterina ◽

A. V. Lemeshko ◽

...

Keyword(s):

Neural Network ◽

Experimental Studies ◽

Optimal Number ◽

Multidimensional Data ◽

Optimum Number ◽

Self Organizing Maps ◽

Number Of Clusters ◽

General Sequence ◽

Kohonen Neural Network ◽

Self Organizing

The article discusses the possibilities of using artificial intelligence systems to solve clustering problems. The value of the optimality criterion for various combinations of the number of clusters and the number of neurons of the output network layer is determined. Self-organizing maps (SOM, Self Organizing Maps), developed by T. Kohonen and representing a powerful tool combining two important paradigms of data analysis - clustering and projecting, visualization of multidimensional data on a plane are considered. An example of the location of cluster nuclei after training the Kohonen neural network for different values of the number of neurons in the source layer is given. Comparing the speed of modern computers with the speed of the Kohonen neural network, with other types of neural networks, allows you to conduct a large number of network exercises in a short time, so you can use one of many methods to determine the maximum value of the function. The results of experimental studies to determine the criterion of optimality are presented in the article for different combinations of the number of clusters and the number of neurons in the original layer of the network. According to the method at the initial stage, a set of input vectors is formed, each of which includes three values. A general sequence of actions is formulated to calculate the optimal number of neurons in the output layer of the Kohonen network. The methodology presented in the article is a further development of teaching methods without a teacher. The technique proposed in the article avoids the need to specify the number of outputs of the Kohonen neural network and can be widely used both in solving data mining problems and in recognizing new unknown classes and situations in different fields.

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Self-Organizing Maps to Validate Anti-Pollution Policies

Logic Journal of IGPL ◽

10.1093/jigpal/jzz049 ◽

2019 ◽

Vol 28 (4) ◽

pp. 596-614

Author(s):

Ángel Arroyo ◽

Carlos Cambra ◽

Álvaro Herrero ◽

Verónica Tricio ◽

Emilio Corchado

Keyword(s):

Capital City ◽

Quality Data ◽

Self Organizing Map ◽

Atmospheric Conditions ◽

Activation Time ◽

Self Organizing Maps ◽

Pollution Levels ◽

High Pollution ◽

Air Quality Data ◽

Self Organizing

Abstract This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently evaluate the effectiveness of the taken measures, after and during the protocol activation time.

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