scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Modeling and Production of Robot Trajectories Using the Temporal Parametrized Self Organizing Maps

2003 ◽  
Vol 13 (02) ◽  
pp. 119-127 ◽  
Author(s):  
Antonio Carlos Padoan ◽  
Guilherme de A. Barreto ◽  
Aluizio F. R. Araújo
Keyword(s):  
Inverse Kinematics ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Neural Architecture ◽  
New States ◽  
Simulation Results ◽  
Self Organizing

In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking

2003 ◽  
Vol 2 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Tomas Eklund ◽  
Barbro Back ◽  
Hannu Vanharanta ◽  
Ari Visa
Keyword(s):  
Performance Analysis ◽  
Pulp And Paper ◽  
The Self ◽  
Financial Data ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Period ◽  
The World ◽  
Self Organizing

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

scholarly journals Village Grouping In Southwest Maluku District Based On Poverty Characteristics Using Self Organizing Maps (SOM) Methods

2020 ◽  
Vol 5 (1) ◽  
pp. 16-20
Author(s):  
Monalisa E. Rijoly ◽  
F. L. Lumalessil ◽  
B. P. Tomasouw
Keyword(s):  
Standard Deviation ◽  
Provincial Government ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Network Method ◽  
The Government ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Self Organizing

Poverty is one of the fundamental problems that has become the center of attention of the Maluku Provincial government, especially Southwest Maluku Regency. This study aims to provide information to the government about village grouping based on poverty characteristics in Southwest Maluku Regency using the Self Organizing Map network method. In this network, a layer containing neurons will arrange itself based on the input of a certain value in a group known as a cluster. In the grouping process, 3 results were obtained with the best grouping II results because they had the smallest standard deviation ratio value.

scholarly journals Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty

2021 ◽  
Vol 1 (2) ◽  
pp. 76-83
Author(s):  
Ulimazzada Islamy ◽  
Afdelia Novianti ◽  
Freditasari Purwa Hidayat ◽  
Muhammad Hasan Sidiq Kurniawan
Keyword(s):  
Social Protection ◽  
The Self ◽  
Self Organizing Map ◽  
The Sustainable Development ◽  
Factors Affecting ◽  
Development Goals ◽  
Spatial Autoregressive ◽  
The Government ◽  
Som Clustering ◽  
Self Organizing

The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.

scholarly journals Using Self-Organizing Maps for Rural Territorial Typology

2011 ◽  
pp. 107-126 ◽  
Author(s):  
Marcos Santos da Silva ◽  
Edmar Ramos de Siqueira ◽  
Olívio Teixeira ◽  
Maria Manos ◽  
Antônio Monteiro
Keyword(s):  
Neural Network ◽  
Temporal Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Spatial Temporal Analysis ◽  
Self Organizing

This work assessed the capacity of the self-organizing map, an unsupervised artificial neural network, to aid the process of territorial design through visualization and clustering methods applied to a multivariate geospatial temporal dataset. The method was applied in the case study of Sergipe‘s institutional regional partition (Territories of Identity). Results have shown that the proposed method can improve the exploratory spatial-temporal analysis capacity of policy makers that are interested in territorial typology. A new partition for rural planning was elaborated and confirmed the coherence of the Territories of Identity.

scholarly journals Saviorganizuojančių neuroninių tinklų sistemų lyginamoji analizė

2009 ◽  
Vol 50 ◽  
pp. 334-339
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
Data Clustering ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Learning Rules ◽  
Main Target ◽  
Similarities And Differences ◽  
Graphical Presentation ◽  
Self Organizing

Straipsnyje nagrinėjamos ir lyginamos tarpusavyje trys saviorganizuojančių neuroninių tinklų (SOM) sistemos: NeNet, SOM-Toolbox ir Databionic ESOM. Pagrindinis šių sistemų tikslas yra suskirstyti duomenis į klasterius pagal jų panašumą, pateikti juos SOM žemėlapyje. Sistemos viena nuo kitos skiriasi duomenų pateikimu, mokymo taisyklėmis, vizualizavimo galimybėmis, todėl čia aptariami sistemų panašumai ir skirtumai. SOM žemėlapiams mokyti ir vizualizuoti naudojami irisų ir stikloduomenys.Comparative Analysis of Self-Organizing Map SystemsPavel Stefanovič, Olga Kurasova SummaryIn the article, we compare three systems of self-organizing maps: NeNet, SOM-Toolbox and Databionic ESOM. The main target of the usage of the systems is data clustering and their graphical presentation on the self-organizing map (SOM). The self-organizing maps are one of types of artifi cial neural networks. The SOM systems are different one from other in their interfaces, the data pre-processing, learning rules, visualization manners, etc. Similarities and differences of the systems have been highlighted here. The experiments have been carried out with two data sets: iris and glass. Quantization and topographic errors of SOMs have been estimated, too.an>

scholarly journals Tekstinių dokumentų panašumų paieška naudojant saviorganizuojančius neuroninius tinklus ir k vidurkių metodą

2013 ◽  
Vol 65 ◽  
pp. 24-33
Author(s):  
Pavel Stefanovič ◽  
Olga Kurasova
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Text Documents ◽  
Self Organizing Maps ◽  
Text Data ◽  
Document Database ◽  
Cluster A ◽  
The Republic ◽  
Sum Of Distances ◽  
Self Organizing

Straipsnyje nagrinėjama dokumentų panašumų paieška naudojant du populiarius metodus: saviorganizuojančius neuroninius tinklus (SOM) ir k vidurkių metodą. Vienas iš šių metodų tikslų – suskirstyti duomenis į klasterius pagal jų panašumą. Analizuota tekstinių dokumentų matricos sudarymo faktorių įtaka gautiems rezultatams. SOM kokybei įvertinti pasiūlyti du nauji matai, skirti klasifi kuotiems duomenims, kurių reikšmės parodo susidariusių klasterių išsidėstymą SOM žemėlapyje. Pirmasis matas parodo, kaip gerai tos pačios klasės duomenys išsidėsto žemėlapyje vienas šalia kito, antrasis matas – kaip toli yra skirtingų klasių centrai. K vidurkių metodu gautų rezultatų kokybei įvertinti skaičiuota suma nuo klasterio centro iki klasterio narių bei įvertintas klasių nesutapimas su klasteriais. Eksperimentiniams tyrimams atlikti pasirinkti tekstiniai dokumentai, paimti iš Lietuvos Respublikos Seimo dokumentų bazės.Similarity analysis of text documents by self-organizing maps and k-means Pavel Stefanovič, Olga Kurasova SummaryIn this paper, we try to fi nd similarities of different text documents by the self-organizing map (SOM) and k-means method. One of the main goals of these methods is to cluster a dataset. Using SOM, the similarities of documents can be observed visually. Both methods can be used only for numerical information, so we analyse the different options by converting text data on to numerical in order to get better results. To estimate the SOM quality, when the classifi ed data are analysed, we propose two new measures: distances between SOM cells, corresponding to data items assigned to the same class, and the distance between centres of SOM cells, corresponding to different classes. We also analyse the results of visualization by self-organizing maps. In order to estimate the k-means quality, we calculate the sum of distances between cluster centres and class members and also we estimate assignment of the data from particular classes to the clusters. The experiments have been carried out using three datasets ocquired from the document database of Seimas of the Republic of Lithuania.font-family: Calibri, sans-serif;"> 

scholarly journals Deep Self-Organizing Map of Convolutional Layers for Clustering and Visualizing Image Data

2021 ◽  
Vol 3 (4) ◽  
pp. 879-899
Author(s):  
Christos Ferles ◽  
Yannis Papanikolaou ◽  
Stylianos P. Savaidis ◽  
Stelios A. Mitilineos
Keyword(s):  
Neural Networks ◽  
Image Data ◽  
Building Blocks ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Deep Architecture ◽  
Unsupervised Deep Learning ◽  
Deep Learning Model ◽  
Self Organizing

The self-organizing convolutional map (SOCOM) hybridizes convolutional neural networks, self-organizing maps, and gradient backpropagation optimization into a novel integrated unsupervised deep learning model. SOCOM structurally combines, architecturally stacks, and algorithmically fuses its deep/unsupervised learning components. The higher-level representations produced by its underlying convolutional deep architecture are embedded in its topologically ordered neural map output. The ensuing unsupervised clustering and visualization operations reflect the model’s degree of synergy between its building blocks and synopsize its range of applications. Clustering results are reported on the STL-10 benchmark dataset coupled with the devised neural map visualizations. The series of conducted experiments utilize a deep VGG-based SOCOM model.

Credit Rating Classification Using Self-Organizing Maps

2002 ◽  
pp. 140-153 ◽  
Author(s):  
Roger P.G.H. Tan ◽  
Jan van den Berg ◽  
Willem-Max van den Bergh
Keyword(s):  
Credit Rating ◽  
Point Of View ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Self Organizing

In this case study, we apply the Self-Organizing Map (SOM) technique to a financial business problem. The case study is mainly written from an investor’s point of view giving much attention to the insights provided by the unique visualization capabilities of the SOM. The results are compared to results from other, more common, econometric techniques. Because of limitations of space, our description is quite compact in several places. For those interested in more details, we refer to Tan (2000).

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