Application of Long-Short Memory Neural Networks in Semantic Search Engines Development

Klimov, Valentin; Balandina, Anita; Chernyshov, Artyom

doi:10.1016/j.procs.2020.02.234

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…In ANN, it is assumed that the output only depends on the current input, which is not true in the real world (Gu et al, 2019). LSTM allows us to infer the potential relationships among the content of the context, because it is a recurrent neural network (RNN)-the output depends on the current input and memory (Klimov et al, 2020;Huang et al, 2021;Li et al, 2022;Gorgij et al, 2023). The basic idea of RNN is to build a hidden state for acquiring the information at the previous time point and the global parameters are calculated from the current time and all previous memories.…”

Section: The Learning Processesmentioning

confidence: 99%

Machine learning brings new insights for reducing salinization disaster

Wang²,

Chen³

et al. 2023

Front. Earth Sci.

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This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO2 concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (R2, root-mean-square error, RPD) values at the training stage, validation stage and testing stage are (0.9924, 0.0123, and 8.282), (0.9931, 0.0872, and 7.0918), (0.9826, 0.1079, and 6.0418), respectively. Based on the underlining mechanisms, we conjecture that subterranean CO2 sequestration could reduce salinization disaster in deserts.

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Section: The Learning Processesmentioning

confidence: 99%

Machine learning brings new insights for reducing salinization disaster

Wang²,

Chen³

et al. 2023

Front. Earth Sci.

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The Application of Transformer Model Architecture for the Dependency Parsing Task

Chernyshov

Klimov

Balandina

et al. 2021

Procedia Computer Science

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Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long-Short Term Memory Model

Liu

Fan

2022

Mathematical Problems in Engineering

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In modern market economy, corporate financial frauds emerge one after another, which have a huge impact on the stock market and triggered an unprecedented credit crisis. Therefore, it is particularly important to identify financial frauds. Improving the efficiency, accuracy, and coverage of fraud identification in financial reports through digital and intelligent means is one of the important links to improve credit risk control of securities companies and also for securities companies to accurately price related financial products of target companies. Traditional recognition methods based on artificial rules can cover relatively limited indicators; rule parameters are set randomly. Besides, it is difficult to make rules based on high-dimension indicators and to dig the hidden deep relationships between indicators. This paper summarizes the relevant indicators of financial fraud identification from the perspective of financial and nonfinancial characteristics. Then, the identification and early warning of financial fraud risk based on bidirectional long-short term memory model are proposed. This method uses the idea of ensemble learning, weights the probability of financial key indicators, and uses the optimal transfer probability to solve the financial fraud risk results. The results show that the industry-specific modeling can significantly improve the accuracy of the financial fraud identification model and it also can effectively help the government regulatory departments, investors, and audit departments to correctly identify the financial fraud of listed companies.

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Application of Long-Short Memory Neural Networks in Semantic Search Engines Development

Cited by 3 publications

References 5 publications

Machine learning brings new insights for reducing salinization disaster

Machine learning brings new insights for reducing salinization disaster

The Application of Transformer Model Architecture for the Dependency Parsing Task

Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long-Short Term Memory Model

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