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37 results on '"Dogra, Varun"'

2. Analyzing DistilBERT for Sentiment Classification of Banking Financial News

Author

Dogra, Varun, Singh, Aman, Verma, Sahil, Kavita, Jhanjhi, N. Z., Talib, M. N., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Peng, Sheng-Lung, editor, Hsieh, Sun-Yuan, editor, Gopalakrishnan, Suseendran, editor, and Duraisamy, Balaganesh, editor

Published
2021
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3. Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification

Author

Dogra, Varun, Singh, Aman, Verma, Sahil, Kavita, Jhanjhi, N. Z., Talib, M. N., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Peng, Sheng-Lung, editor, Hsieh, Sun-Yuan, editor, Gopalakrishnan, Suseendran, editor, and Duraisamy, Balaganesh, editor

Published
2021
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12. AAPNA library

Author

Dogra, Varun, primary, Kumar, Amit, additional, Hitesh, Hitesh, additional, Yadav, Krishna, additional, Madhavan, Peeyush K, additional, and Thakur, Rajkumar, additional

Published
2022
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13. The Pattern of Antimicrobial Sensitivity in Patients of Gastrointestinal Perforation in Adult Population: A Multi-Centric Study from a Developing Country.

Author

Ganai, Abdul Rashid, Rashid, Arshad, Ahmed, Rizwan, Bali, Rajandeep Singh, Dogra, Varun, Gupta, Shyam K., and Neogi, Sushanto

Subjects
INTESTINAL perforation, ASCITIC fluids, DRUG resistance in bacteria, ADULTS, MEROPENEM, TERTIARY care
Abstract

Introduction: Amidst the growing scare of antibiotic resistance, the institution of appropriate antibiotic therapy is imperative in the proper management of perforation peritonitis. Objective:In this study, the objective was to find out the common microbial involvement in cases of gastrointestinal perforation along-with their sensitivity to commonly used antibiotics. Methods: This was a retrospective, multi-centric observational study conducted in four tertiary care hospitals of a developing country over a period of 12 years. The records of all the patients operated for gastrointestinal perforation peritonitis during this period were retrieved and analysed. Only those patients were included in the study in which the culture/sensitivity reports of peritoneal fluid were available. Results: A total of 1312 patients were included, among which, 854 (65.1%) were males and 458 (34.9%) were females. The mean age of the patients was 47.1 ± 13.8 years. Upper gastrointestinal perforations were identified in 362 (27.7%) patients and lower gastrointestinal perforations were seen in 943 (71.9%) patients. A single bacterium was isolated in 665 (59.9%) patients, and two bacteria were isolated in 245 (22.1%) patients. The most common organism to be cultured was Escherechia coli [644 (58.1%) patients]. The frequency of sensitivity for each microbe was highest for meropenem (P<0.05). Conclusion: We conclude that Gramnegative Enterobacteriaceae are the usual suspects in cases of perforation peritonitis, and are usually sensitive to meropenem. [ABSTRACT FROM AUTHOR]

Published
2023

24. A Complete Process of Text Classification System Using State-of-the-Art NLP Models.

Author

Dogra, Varun, Verma, Sahil, Kavita, Chatterjee, Pushpita, Shafi, Jana, Choi, Jaeyoung, and Ijaz, Muhammad Fazal

Subjects
*DEEP learning, *NATURAL language processing, *MACHINE learning, *TEXT mining, *INFORMATION technology, *CLASSIFICATION, *SUPERVISED learning
Abstract

With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest. [ABSTRACT FROM AUTHOR]

Published
2022
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