scholarly journals A new hybrid PSO algorithm based on a stochastic Markov chain model

2015 ◽  
Vol 90 ◽  
pp. 127-137 ◽  
Author(s):  
N. Di Cesare ◽  
D. Chamoret ◽  
M. Domaszewski
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Pso Algorithm ◽  
Chain Model ◽  
Stochastic Markov Chain

scholarly journals Aplicação do Modelo Estocástico Cadeia de Markov a Dados Diários de Precipitação dos Estados da Bahia e Sergipe (Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe)

2012 ◽  
Vol 5 (3) ◽  
pp. 509 ◽  
Author(s):  
Winicius Santos Araújo ◽  
Francisco De Assis Salviano de Sousa ◽  
José Ivaldo Barbosa de Brito ◽  
Lourivaldo Mota Lima
Keyword(s):  
Spatial Distribution ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Winter Season ◽  
Coastal Region ◽  
Daily Rainfall ◽  
Summer Season ◽  
High Availability ◽  
Chain Model ◽  
Stochastic Markov Chain

O objetivo desta pesquisa foi fornecer uma distribuição espacial e computar as probabilidades incondicionais e condicionais de primeira ordem das precipitações dos Estados da Bahia e Sergipe. Para tanto, foram utilizados dados diários pluviais referentes a um período de 47 anos (1960-2006) de 75 postos e/ou estações meteorológicas fornecidos pela antiga rede de postos da SUDENE através do DCA. Os resultados mostram que a zona oeste da área pesquisada é a mais favorecida com a precipitação na estação verão, ocorrendo o oposto disto na estação inverno; o leste é o mais beneficiado no inverno, o sul na primavera, e o norte no outono. Foi obtido que a probabilidade incondicional, P(C), na região costeira, é influenciada pela alta disponibilidade de umidade do Atlântico e pela geração de sistemas que provocam precipitação devido ao contraste de temperatura da superfície oceano-continente, particularmente durante os meses de outono e primavera. No verão o efeito oceânico não é percebido devido à alta disponibilidade de umidade sobre a área pesquisada e a alta persistência observada da precipitação diária está associada com os núcleos de máximas precipitações que se destacam na estação verão, enquanto que no inverno a baixa persistência foi predominante.Palavras - chave: distribuição espacial, cadeia de markov, precipitação diária. Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe ABSTRACTThe objective of this research was to provide a spatial distribution and compute the probabilities conditional and unconditional first order of precipitation of the States of Bahia and Sergipe. It had been used daily rainfall data relating to a period of 47 years (1960-2006) of 75 stations and/or meteorological stations provided by the former station network SUDENE by DCA. The results show that the area west of the area searched is more favored with rainfall in the summer season, the opposite occurring in this winter season, the east is the most improved in the winter, spring in the south, and north in the autumn. It was obtained that the unconditional probability, P(C), the coastal region, is influenced by the high availability of moisture from the Atlantic and the generation of systems that cause precipitation due to the contrast of surface temperature of ocean-continent, particularly during the autumn months and Spring. In summer the ocean effect is not perceived due to the high availability of moisture over the area surveyed and observed high persistence of daily rainfall is associated with the nuclei of maximum precipitation that stand out in the summer season, while in winter the low persistence prevailed.Keywords: spatial distribution, markov chain, daily precipitation.

scholarly journals A Stochastic Markov Chain Model to Describe Lung Cancer Growth and Metastasis

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34637 ◽  
Author(s):  
Paul K. Newton ◽  
Jeremy Mason ◽  
Kelly Bethel ◽  
Lyudmila A. Bazhenova ◽  
Jorge Nieva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Cancer Growth ◽  
Chain Model ◽  
Stochastic Markov Chain

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

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