scholarly journals Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yue Shen ◽  
Yixin Ren
Keyword(s):  
Decision Making ◽  
Markov Chain ◽  
Panel Data ◽  
Markov Chain Model ◽  
Vital Role ◽  
Chain Model ◽  
Continuous Increase ◽  
Cross Border ◽  
Economic Decision Making ◽  
Purchase Quantity

With the deepening of globalization, cross-border industrial e-commerce has increasingly become a mainstream means of trade exchanges, however, with the continuous increase in business volume. How to accurately estimate the procurement volume of cross-border materials is a significant issue, which plays a vital role in quantifying economic efficiency. To this end, this study proposes a financial decision-making algorithm for cross-border industrial e-commerce material procurement based on the Markov chain. The algorithm first preprocesses the original logistics data, including data cleaning, missing data filling, and noisy data removal, represents the data as structured panel data, finally builds a Markov chain model based on the panel data, and then makes predictions on the new data. We verified the effectiveness of the proposed model on a simulated dataset and an actual dataset.

Microfinance Institution (MFIs) and Survival of Micro and Small Enterprises (MSEs): Empirical Evidence of TraderMoni Scheme Beneficiaries in South-Western Nigeria.

2021 ◽  
Vol 8 (3) ◽  
pp. 195-215
Author(s):  
Wole Adamolekun ◽  
J. A Obadeyi ◽  
Sunday Oseiweh Ogbeide ◽  
A. A. Akande
Keyword(s):  
Markov Chain ◽  
Panel Data ◽  
Empirical Evidence ◽  
Markov Chain Model ◽  
Regulatory Policy ◽  
Small Enterprises ◽  
Chain Model ◽  
Informal System ◽  
Microfinance Institution ◽  
Micro And Small Enterprises

Deregulation in Microfinance Institution (MFIs) in accordance with regulatory policy architecture since 2005 has not fully stimulated sustainability towards the informal system due to the inability of MFIs to access funds and government to judiciously administer credits to beneficiaries of various schemes; this has led to the partial collapse of some schemes in Nigeria; despite Government good intentions of creating employment and alleviating poverty. In view of this, this study assessed Microfinance Institution (MFIs) and Survival of Micro and Small Enterprises (MSEs): Empirical evidence of tradermoni scheme beneficiaries in South-Western Nigeria. The study adopted Tedeschi model (2006) that examined incentives available for borrowers to repay loans. Furthermore, reference was made to Markov Chain model to investigate the response of individual borrower as an applicant and beneficiary of tradermoni scheme in the context of this study. Eighteen MFIs were sampled from 2009 – 2020. Panel data was adopted for the study. The result showed mixed influences of MFIs on survival of MSEs. We are hopeful that findings of this paper would help to fill the existing gap on the influence of MFIs on the survival of MSEs.

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.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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