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- W4298146952 abstract "This paper introduces Tunicate Swarm Algorithm-based Hierarchical Attention Network (TSA-HAN). TSA-HAN is the combination of Tunicate Swarm Optimization Algorithm (TSA) that uses jet propulsion and Swarm Intelligence and Hierarchical Attention Network (HAN) which makes use of leveled document structure. The proposed optimized algorithm is used for text classification. The performance of TSA-HAN is evaluated based on five parameters, namely accuracy, TPR, TNR, precision, and FNR. For this, purpose self-created dataset named real-time dataset consisting of 5000 documents and popular datasets, i.e., Reuters datasets and 20-Newsgroup datasets, have been utilized and the potency of the said optimized algorithm has been further compared with an existing improved sine cosine algorithm (ISCA). The comparative analysis results show that TSA-HAN performs slightly better than ISCA." @default.
- W4298146952 created "2022-10-01" @default.
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- W4298146952 date "2022-10-01" @default.
- W4298146952 modified "2023-09-27" @default.
- W4298146952 title "A Text Classification Optimization Framework for Prodigious Datasets" @default.
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- W4298146952 doi "https://doi.org/10.1007/978-981-19-3571-8_62" @default.
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