Study of agronomic exports based on deep learning and Data mining
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Abstract
Exports of agronomic products are a major source of income for many countries across the
world. Import, export, and domestic usage data, as well as the adjustments to production
and marketing that follow, may all be better predicted using monthly Agronomic Export
Forecasting. To better anticipate the growth and drop of Agronomic exportations, this study
presents a new approach called Agronomic exports time series-longshortterm memory. An
algorithm is used to train vectors of words by dividing words into groups and then using
Term Frequency -Inverse Document Frequency/word cloud to studyinformational
keywords. This study investigates whether the AETS-LSTM model can effectively use the
purchasing managers' index (PMI) of everyindustries to anticipate the increase and fall of
agronomic exports. A study of the PMI principles in the financial and insurance industries
found that using keyword vectors increased the accuracy of predicting growth and decline
in Agronomic exports by 82.61%. Combining electrical and optical keywords improves its
effectiveness in these categories.. Thus, agribusiness operators and policymakers will be
able to use the recommended approach for a more accurate assessment of local and
international output and sales.
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