A review of systematic evaluation and improvement in the big data environment

Yang, Feng; Wang, Manman

doi:10.1007/s42524-020-0092-6

Cited by 34 publications

(16 citation statements)

References 122 publications

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“…Secondly, in order to make ants find the optimal solution faster, the pheromone concentration on the current optimal solution path is only updated when the pheromone is globally updated in the ant colony system. e global update rule is formula (7):…”

Section: Construction Of Financial Management Early Warning Model Based On Neural Network Improved By Ant Colony Algorithmmentioning

confidence: 99%

[Retracted] Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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With the advent of the era of economic globalization, the world capital market is also facing financial risks. It is necessary to have a corresponding financial management early warning model to reduce economic losses. This paper uses the combination of ant colony algorithm and neural network algorithm to build a neural network improved by ant colony algorithm model. By setting relevant assumptions, the financial statements and annual report texts are predicted and analyzed and compared with the original static data forecasting model. Compared with traditional methods, the time series sequencing analysis used in this paper makes the result prediction more accurate. This allows one year’s data to be used to predict the data for the next two years. This research can provide a corresponding reference for the optimization of financial management early warning system.

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Section: Construction Of Financial Management Early Warning Model Based On Neural Network Improved By Ant Colony Algorithmmentioning

confidence: 99%

[Retracted] Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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“…The study found that the used neural network system does not show stable profits. In [17], a review of big data media is carried out, as well as decision-making methods using such data. Various methods of analytics and modeling based on the use of big data are considered.…”

Section: Computer Sciencesmentioning

confidence: 99%

Creation of a Neural Network Algorithm for Automated Collection and Analysis of Statistics of Exchange Quotes Graphics

Skuratov

Kuzmin²,

Nelin

et al. 2020

EUREKA: Physics and Engineering

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“…Single-cell RNA-sequencing (scRNA-seq) technology has been widely used in many biological investigations, including the elucidation of cell subtype heterogeneity ( Zeisel et al 2015 ; Goolam et al 2016 ), construction of gene regulatory networks ( Darmanis et al 2015 ), profiling of cell development and differentiation ( Deng et al 2014 ; Liu et al 2017 ), and depiction of disease in an immunoresponsive environment ( Guo et al 2018 ; Zhang et al 2018 ). The analysis of scRNA-seq data contains, but is not limited to, quality control ( Chen et al 2016 ), data normalization ( Cole et al 2019 ), unsupervised clustering ( Kiselev et al 2017 ; Wang et al 2017 ; Wolf et al 2018 ; Yang and Wang 2020 ), trajectory construction ( Wolf et al 2019 ), and differentially expressed gene identification ( Soneson and Robinson 2018 ). As a fundamental step of scRNA-seq data analysis, cell clustering determines the results of subsequent downstream analyses to a certain extent, but is often inaccurate and misconstrues analyses.…”

Section: Introductionmentioning

confidence: 99%

Single-cell data clustering based on sparse optimization and low-rank matrix factorization

Chen

et al. 2021

G3 Genes|Genomes|Genetics

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Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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A review of systematic evaluation and improvement in the big data environment

Cited by 34 publications

References 122 publications

[Retracted] Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

[Retracted] Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

Creation of a Neural Network Algorithm for Automated Collection and Analysis of Statistics of Exchange Quotes Graphics

Single-cell data clustering based on sparse optimization and low-rank matrix factorization

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