The Use of a Robust-Adaptive Self Organizing Map to Enhance the Prediction Performance of Clinical Datasets

2019 ◽  
Author(s):  
Naim Ajlouni ◽  
Alaa Ali Hameed ◽  
Ali Güneş ◽  
Adem Özyavaş ◽  
Zeynep Orman
Keyword(s):  
Prediction Performance ◽  
Self Organizing Map ◽  
Robust Adaptive ◽  
Self Organizing

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

The Automatic Method of EEG State Classification by Using Self-Organizing Map

2010 ◽  
Vol 130 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Kazuhiro Tamura ◽  
Takamasa Shimada ◽  
Yoichi Saito
Keyword(s):  
Self Organizing Map ◽  
Automatic Method ◽  
State Classification ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing

Spherical Self-Organizing Map Detects MYBL 1 As Candidate Gene for Triple-Negative Breast Cancer

2015 ◽  
Vol 3 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Masashi Ikeda ◽  
Kazuki Kumon ◽  
Kazuya Omoto ◽  
Yuh Sugii ◽  
Akifumi Mizutani ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Candidate Gene ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Optimal Design of District Metered Areas in a Water Distribution Network Using Coupled Self-Organizing Map and Community Structure Algorithm

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 836
Author(s):  
Xuan Khoa Bui ◽  
Malvin S. Marlim ◽  
Doosun Kang
Keyword(s):  
Community Structure ◽  
Optimal Design ◽  
Water Distribution ◽  
Distribution Network ◽  
Coupled Model ◽  
Water Distribution Network ◽  
Self Organizing Map ◽  
Structure Algorithm ◽  
Demand Variability ◽  
Self Organizing

Operation and management of a water distribution network (WDN) by district metered areas (DMAs) bring many benefits for water utilities, particularly regarding water loss control and pressure management. However, the optimal design of DMAs in a WDN is a challenging task. This paper proposes an approach for the optimal design of DMAs in the multiple-criteria decision analysis (MCDA) framework based on the outcome of a coupled model comprising a self-organizing map (SOM) and a community structure algorithm (CSA). First, the clustering principle of the SOM algorithm is applied to construct initial homologous clusters in terms of pressure and elevation. CSA is then coupled to refine the SOM-based initial clusters for the automated creation of multiscale and dynamic DMA layouts. Finally, the criteria for quantifying the performance of each DMA layout solution are assessed in the MCDA framework. Verifying the model on a hypothetical network and an actual WDN proved that it could efficiently create homologous and dynamic DMA layouts capable of adapting to water demand variability.

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.

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