Using the Term Frequency-Inverse Document Frequency for the Problem of Identifying Shrimp Diseases with State Description Text

نویسندگان

چکیده

With the increasing demand for research on shrimp disease recognition to assist far-off farmers who need proper assistance their farming, prediction is still in initial stage. Most current methods utilize vision-based models, which mainly face challenges: symptom detection and image quality. Meanwhile, there are few researches language-based get over issues. In this study, we will experiment with natural language processing based recognizing diseases; descriptions of status. This study provides an efficient solution classifying multiple diseases shrimp. We compare different machine learning models deep (SVM, Logistic Regression, Multinomial Naive Bayes, (a4) Bernoulli Random forest, DNN, LSTM, GRU, BRNN, RCNN) terms accuracy performance. The also evaluates TF-IDF technique feature extraction. Data were collected 12 types 1,037 descriptions. Firstly, data preprocessed standardised Vietnamese accent typing, tokenized words, converted lowercase, removed unnecessary characters stopwords. Then, utilized express text weight. Machine learning-based trained. experimental results show that forest (F1-Score micro: 98%) DNN (Validation accuracy: 84%) most models.

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ژورنال

عنوان ژورنال: International Journal of Advanced Computer Science and Applications

سال: 2023

ISSN: ['2158-107X', '2156-5570']

DOI: https://doi.org/10.14569/ijacsa.2023.0140577