References
[1]. Devlin, J., et al., BERT: Pre-training of deep bidirectional transformers for language under-standing. arXiv preprint arXiv:1810.04805, 2018.
[2]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E.. Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems., 30(1), pp. 111-123,2020.
[3]. Sun, C., et al. How to fine-tune BERT for text classification? in China national conference on Chinese computational linguistics. 2019. Springer.
[4]. Lee, J., et al., BioBERT: a pre-trained biomedical language representation model for biomedi-cal text mining. Bioinformatics, 2020. 36(4): p. 1234-1240.
[5]. Munikar, M., S. Shakya, and A. Shrestha. Fine-grained sentiment classification using BERT. in 2019 Artificial Intelligence for Transforming Business and Society (AITB). 2019. IEEE.
[6]. Li, X., et al., Enhancing BERT representation with context-aware embedding for aspect-based sentiment analysis. IEEE Access, 2020. 8: p. 46868-46876.
[7]. Jain, P.K., et al., Employing BERT-DCNN with sentic knowledge base for social media senti-ment analysis. Journal of Ambient Intelligence and Humanized Computing, 2022: p. 1-13.
[8]. Nezhad, Z.B. and M.A. Deihimi, Twitter sentiment analysis from Iran about COVID 19 vac-cine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2022. 16(1): p. 102367.
[9]. Singh, M., A.K. Jakhar, and S. Pandey, Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 2021. 11(1): p. 1-11.
[10]. Shah, B.K., et al. Sentiments Detection for Amazon Product Review. in 2021 International Conference on Computer Communication and Informatics (ICCCI). 2021. IEEE.
[11]. Lehečka, J., et al. BERT-based sentiment analysis using distillation. in International Confer-ence on Statistical Language and Speech Processing. 2020. Springer.
[12]. Sarma, P.K., Y. Liang, and W.A. Sethares, Shallow domain adaptive embeddings for sentiment analysis. arXiv preprint arXiv:1908.06082, 2019.
[13]. Devlin, J., et al., BERT: Pre-training of deep bidirectional transformers for language under-standing. arXiv preprint arXiv:1810.04805, 2018. [2] Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E.. Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems., 30(1), pp. 111-123,2020. [3] Sun, C., et al. How to fine-tune BERT for text classification? in China national conference on Chinese computational linguistics. 2019. Springer. [4] Lee, J., et al., BioBERT: a pre-trained biomedical language representation model for biomedi-cal text mining. Bioinformatics, 2020. 36(4): p. 1234-1240. [5] Munikar, M., S. Shakya, and A. Shrestha. Fine-grained sentiment classification using BERT. in 2019 Artificial Intelligence for Transforming Business and Society (AITB). 2019. IEEE. [6] Li, X., et al., Enhancing BERT representation with context-aware embedding for aspect-based sentiment anal
[14]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
[15]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand pre-diction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.
[16]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.
[17]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.
[18]. Sathishkumar, V. E., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., Venkatesh, B., & Santhoshkumar, S. (2021). Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment. Arabian Journal for Science and Engineering, 1-9.
[19]. Sathishkumar V E., Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.
[20]. Sathishkumar Easwaramoorthy., Sophia, F., & Prathik, A. (2016, February). Biometric Authen-tication using finger nails. In 2016 international conference on emerging trends in engineer-ing, technology and science (ICETETS) (pp. 1-6). IEEE.
Cite this article
Deepa,D.;Yaswanth,R.;Suganth,C. (2023). Prediction of Skin Cancer Using Pre-Trained Language Models from Patient Symptoms. Applied and Computational Engineering,2,649-656.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Devlin, J., et al., BERT: Pre-training of deep bidirectional transformers for language under-standing. arXiv preprint arXiv:1810.04805, 2018.
[2]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E.. Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems., 30(1), pp. 111-123,2020.
[3]. Sun, C., et al. How to fine-tune BERT for text classification? in China national conference on Chinese computational linguistics. 2019. Springer.
[4]. Lee, J., et al., BioBERT: a pre-trained biomedical language representation model for biomedi-cal text mining. Bioinformatics, 2020. 36(4): p. 1234-1240.
[5]. Munikar, M., S. Shakya, and A. Shrestha. Fine-grained sentiment classification using BERT. in 2019 Artificial Intelligence for Transforming Business and Society (AITB). 2019. IEEE.
[6]. Li, X., et al., Enhancing BERT representation with context-aware embedding for aspect-based sentiment analysis. IEEE Access, 2020. 8: p. 46868-46876.
[7]. Jain, P.K., et al., Employing BERT-DCNN with sentic knowledge base for social media senti-ment analysis. Journal of Ambient Intelligence and Humanized Computing, 2022: p. 1-13.
[8]. Nezhad, Z.B. and M.A. Deihimi, Twitter sentiment analysis from Iran about COVID 19 vac-cine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2022. 16(1): p. 102367.
[9]. Singh, M., A.K. Jakhar, and S. Pandey, Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 2021. 11(1): p. 1-11.
[10]. Shah, B.K., et al. Sentiments Detection for Amazon Product Review. in 2021 International Conference on Computer Communication and Informatics (ICCCI). 2021. IEEE.
[11]. Lehečka, J., et al. BERT-based sentiment analysis using distillation. in International Confer-ence on Statistical Language and Speech Processing. 2020. Springer.
[12]. Sarma, P.K., Y. Liang, and W.A. Sethares, Shallow domain adaptive embeddings for sentiment analysis. arXiv preprint arXiv:1908.06082, 2019.
[13]. Devlin, J., et al., BERT: Pre-training of deep bidirectional transformers for language under-standing. arXiv preprint arXiv:1810.04805, 2018. [2] Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E.. Visiting Indian Hospitals Before, During and After Covid. Interna-tional Journal of Uncertainty, Fuzziness and Knowledge-Based Systems., 30(1), pp. 111-123,2020. [3] Sun, C., et al. How to fine-tune BERT for text classification? in China national conference on Chinese computational linguistics. 2019. Springer. [4] Lee, J., et al., BioBERT: a pre-trained biomedical language representation model for biomedi-cal text mining. Bioinformatics, 2020. 36(4): p. 1234-1240. [5] Munikar, M., S. Shakya, and A. Shrestha. Fine-grained sentiment classification using BERT. in 2019 Artificial Intelligence for Transforming Business and Society (AITB). 2019. IEEE. [6] Li, X., et al., Enhancing BERT representation with context-aware embedding for aspect-based sentiment anal
[14]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
[15]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand pre-diction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.
[16]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.
[17]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.
[18]. Sathishkumar, V. E., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., Venkatesh, B., & Santhoshkumar, S. (2021). Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment. Arabian Journal for Science and Engineering, 1-9.
[19]. Sathishkumar V E., Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.
[20]. Sathishkumar Easwaramoorthy., Sophia, F., & Prathik, A. (2016, February). Biometric Authen-tication using finger nails. In 2016 international conference on emerging trends in engineer-ing, technology and science (ICETETS) (pp. 1-6). IEEE.