The Learning, Self-Organizing School: The Self-Organizing School: Next-Generation Comprehensive School Reforms

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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Self-exciting multifractional processes

Journal of Applied Probability ◽

10.1017/jpr.2020.88 ◽

2021 ◽

Vol 58 (1) ◽

pp. 22-41

Author(s):

Fabian A. Harang ◽

Marc Lagunas-Merino ◽

Salvador Ortiz-Latorre

Keyword(s):

Stochastic Process ◽

Brownian Motion ◽

Rate Of Convergence ◽

Existence And Uniqueness ◽

Volterra Equation ◽

The Self ◽

The Past ◽

Stochastic Volterra Equation ◽

Multifractional Brownian Motion ◽

Self Organizing

AbstractWe propose a new multifractional stochastic process which allows for self-exciting behavior, similar to what can be seen for example in earthquakes and other self-organizing phenomena. The process can be seen as an extension of a multifractional Brownian motion, where the Hurst function is dependent on the past of the process. We define this by means of a stochastic Volterra equation, and we prove existence and uniqueness of this equation, as well as giving bounds on the p-order moments, for all $p\geq1$. We show convergence of an Euler–Maruyama scheme for the process, and also give the rate of convergence, which is dependent on the self-exciting dynamics of the process. Moreover, we discuss various applications of this process, and give examples of different functions to model self-exciting behavior.

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

Medicina ◽

10.3390/medicina57030235 ◽

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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