{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:50:48Z","timestamp":1777697448444,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2022,4,18]]},"abstract":"<jats:p>The epidemic of COVID-19 has thrown the planet into an awfully tricky situation putting a terrifying end to thousands of lives; the global health infrastructure continues to be in significant danger. Several machine learning techniques and pre-defined models have been demonstrated to accomplish the classification of COVID-19 articles. These delineate strategies to extract information from structured and unstructured data sources which form the article repository for physicians and researchers. Expanding the knowledge of diagnosis and treatment of COVID-19 virus is the key benefit of these researches. A multi-label Deep Learning classification model has been proposed here on the LitCovid dataset which is a collection of research articles on coronavirus. Relevant prior articles are explored to select appropriate network parameters that could promote the achievement of a stable Artificial Neural Network mechanism for COVID-19 virus-related challenges. We have noticed that the proposed classification model achieves accuracy and micro-F1 score of 75.95% and 85.2, respectively. The experimental result also indicates that the propound technique outperforms the surviving methods like BioBERT and Longformer.<\/jats:p>","DOI":"10.3233\/idt-210058","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T12:10:31Z","timestamp":1647346231000},"page":"205-215","source":"Crossref","is-referenced-by-count":0,"title":["Attention-based bidirectional LSTM with embedding technique for classification of COVID-19 articles"],"prefix":"10.1177","volume":"16","author":[{"given":"Rakesh","family":"Dutta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Application, Hijli College, Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mukta","family":"Majumder","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Application, University of North Bengal, Siliguri, 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