EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01056 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01056
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202343001056
Published online 06 October 2023
  1. P. Mehta, Creating Google Chrome Extensions. Berkeley, CA: Apress, 2016, ch. 1, doi: 10.1007/978-1-4842-1775-7_1.K. Murai and V. Klyuev, (2015). [Google Scholar]
  2. M. Frisbie, Berkeley, CA: Apress, (2023). https://doi.org/10.1007/978-1-4842-8725-5_6 . [Google Scholar]
  3. “YouTube-Transcript-API,” PyPI. [Online]. Available: https://pypi.org/project/youtube-transcript-api/ . [Google Scholar]
  4. F. Aslam, H. Mohammed, and P. Lokhande, “in IJARCS, vol. 6, (2015) [Google Scholar]
  5. A. Mani, “Building Restful Apis with Flask in Python,” Atma's Blog, Oct. 20, 2019. [Online]. Available: https://atmamani.github.io/blog/building-restful-apis-with-flask-in-python/ . [Google Scholar]
  6. J. Chan, R. Chung, and J. Huang, Packt Publishing Ltd, (2019). [Google Scholar]
  7. T. Wolf et al., arXiv:1910.03771 [cs], Jul. 14, (2020). [Google Scholar]
  8. T. M. Mitchell, Machine Learning, vol. 1., New York: McGraw-Hill, (2007). [Google Scholar]
  9. L. Tunstall, L. Von Werra and T. Wolf,O'Reilly Media, Inc., (2022). [Google Scholar]
  10. T5, Hugging Face Transformers documentation, [Online]. Available: https://huggingface.co/docs/transformers/model_doc/t5 . [Google Scholar]
  11. “Exploring Transfer Learning with T5: The Text-to-Text Transfer Transformer,” Google AI Blog, 20-Feb-2020. [Online]. Available: https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html . [Google Scholar]
  12. U. Khandelwal et al., arXiv:1905.08836 [cs], May 21, (2019). [Google Scholar]
  13. U. Kapoor, “DSpace @ Delhi Technological University: SUMMARISATION OF YOUTUBE VIDEO USING TRANSFORMERS AND API, 01-May-2022. [Online]. Available: http://dspace.dtu.ac.in:8080/jspui/handle/repository/19635 . [Google Scholar]
  14. P. Theron, J. Dentan, and L. Gautier,May (2022), doi: 10.13140/RG.2.2.20076.85125. [Google Scholar]
  15. C. Raffel et al., arXiv preprint arXiv:1910.10683, (2020). [Google Scholar]
  16. H. K. Dhalla, “A Performance Analysis of Native JSON Parsers in Java, Python, MS.NET Core, JavaScript, and PHP,” 2020 16th International Conference on Network and Service Management (CNSM), Izmir, Turkey, (2020), doi: 10.23919/CNSM50824.2020.9269101. [Google Scholar]
  17. A. Nafies, Medium, The Startup, 25-Oct-2020. [Online]. Available: https://medium.com/swlh/parsing-rest-api-payload-and-query-parameters-with-flask-better-than-marshmallow-aa79c889e3ca . [Google Scholar]
  18. Y. Agrawal et al., EasyChair Preprint (5404), (2021). [Google Scholar]
  19. P. Dwivedi, Towards Data Science, Sep. 9, (2020). [Online]. Available: https://towardsdatascience.com/fine-tuning-a-t5-transformer-for-any-summarization-task-82334c64c81 . [Google Scholar]
  20. J. Briggs,” Towards Data Science, 02-Sep-2021. [Online]. Available: https://towardsdatascience.com/the-ultimate-performance-metric-in-nlp-111df6c64460 . [Google Scholar]
  21. T. Berg-Kirkpatrick, D. Burkett, and D. Klein, “An empirical investigation of statistical significance in NLP,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Jeju Island, Korea: Association for Computational Linguistics, Jul. (2012). [Google Scholar]
  22. R. Wang and J. Li, “Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, (2019). [Google Scholar]
  23. Sokolova, Marina & Japkowicz, Nathalie & Szpakowicz, Stan, “Beyond Accuracy, FScore and ROC: A Family of Discriminant Measures for Performance Evaluation,” Australasian Joint Conference on Artificial Intelligence. Vol. 4304. 1015-1021. 10.1007/11941439_114. [Google Scholar]
  24. G. Kavita, “An Intro to Rouge, FreeCodeCamp.org, 24-Oct-2017. [Online]. Available: https://www.freecodecamp.org/news/what-is-rouge-and-how-it-works-for-evaluation-of-summaries-e059fb8ac840/ . [Google Scholar]
  25. P. Shruti et al.,arXiv:1906.07901 [cs.CL], Jun 19, (2019). Australasian Joint Conference on Artificial Intelligence [Google Scholar]
  26. Conference Paper Deep Learning Framework for Liver CT Image Segmentation and Risk Prediction, Gundavarapu, M.R., Saginala, R., Varma, M.A., ... Bodduluri, A.S., (2023), 645 LNNS, pp. 189–201 [Google Scholar]
  27. Dusa, D., Gundavarapu, M.R. 8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, (2022), pp. 1023–1028 [Google Scholar]
  28. Tejaswini Priyanka, Y. Reshma Reddy, D. Vajja, G. Ramesh and S. Gomathy (2023). A Novel Emotion based Music Recommendation System using CNN. 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 592-596, doi: 10.1109/ICICCS56967.2023.10142330. [Google Scholar]
  29. Ramesh, G., Reddy, K.S.S., Ramu, G., Reddy, Y.C.A.P., Somasekar, J. (2023). An Empirical Study on Discovering Software Bugs Using Machine Learning Techniques. [Google Scholar]
  30. In: Buyya, R., Hernandez, S.M., Kovvur, R.M.R., Sarma, T.H. (eds) Computational Intelligence and Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-19-3391-2_14 . [Google Scholar]
  31. Chandrika Lingala, and Karanam Madhavi, “A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms”, E3S Web of Conferences 309, 01043 (2021), ICMED 2021. https://doi.org/10.1051/) e3sconf/202130901043 SCOPUS [Google Scholar]
  32. Chandra Sekhar Reddy P, Sakthidharan G, Kanimozhi Suguna S, Mannar Mannan J, Varaprasada Rao P, IJEAT. 8, (2019) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.