2022
DOI: 10.1002/int.22839
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SALSTM: An improved LSTM algorithm for predicting the competitiveness of export products

Abstract: As international trade has been developing at an unprecedented rate, export product competitiveness is significant in a country's trade system. At present, neural network algorithms are extensively used in economic forecasting. Scholars have verified the effectiveness of neural networks, especially long short term memory (LSTM) model, compared with other nonlinear prediction methods. However, there are still research blanks in forecasting economic indicators related to export product competitiveness, and there… Show more

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Cited by 6 publications
(7 citation statements)
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“…As an example, managers can deploy AI-enabled computer vision to detect production defects early and achieve real-time production adjustment. Furthermore, managers can equip assembly lines with AI to constantly monitor the production process and autonomously, which aids in reducing operational costs and automating product testing (Yu, Fletcher, & Buck, 2022;Yu, Liu, et al, 2022).…”
Section: Autonomy Optimizes International Operationsmentioning
confidence: 99%
“…As an example, managers can deploy AI-enabled computer vision to detect production defects early and achieve real-time production adjustment. Furthermore, managers can equip assembly lines with AI to constantly monitor the production process and autonomously, which aids in reducing operational costs and automating product testing (Yu, Fletcher, & Buck, 2022;Yu, Liu, et al, 2022).…”
Section: Autonomy Optimizes International Operationsmentioning
confidence: 99%
“…Previously, there were a number of machine learning methods and technologies applied in the feld of data classifcation [4][5][6]. With regard to the challenging PD diagnosis problem, these methods provide superior specifcations.…”
Section: Prior Technologies and Limitationsmentioning
confidence: 99%
“…Equation (6) shows the calculation process of the Octconv unit. Te input of Octconv consists of high-frequency feature X H and low-frequency feature X L , with the two outputs Y � Y H , Y L 􏼈 􏼉.…”
Section: Octave Convolutional Layermentioning
confidence: 99%
“…Recursive neural networks 4,5 have a feedback connection mechanism and are suitable for dynamic data processing. Recursive connections allow the network to capture temporal features in continuous inputs, and the temporal dependence of the inputs can be embedded in their dynamic behavior.…”
Section: Introductionmentioning
confidence: 99%
“…The current state depends not only on the current input but also on previous input states; these operations increase the computational cost of training the network. [1][2][3] Recursive neural networks 4,5 have a feedback connection mechanism and are suitable for dynamic data processing. Recursive connections allow the network to capture temporal features in continuous inputs, and the temporal dependence of the inputs can be embedded in their dynamic behavior.…”
Section: Introductionmentioning
confidence: 99%