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Titlebook: Artificial Intelligence: Theory and Applications; Proceedings of AITA Harish Sharma,Antorweep Chakravorty,Rajani Kumari Conference proceed

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21#
發(fā)表于 2025-3-25 05:49:53 | 只看該作者
22#
發(fā)表于 2025-3-25 09:07:38 | 只看該作者
Bernhard Badura,Antje Ducki,Markus Meyer methods and detection methods based on parameters such as accuracy, F1 score, precision, and recall. A comprehensive survey, observations drawn from the existing research, and its analysis are presented in this paper. The survey will lead directions for future research.
23#
發(fā)表于 2025-3-25 15:41:30 | 只看該作者
24#
發(fā)表于 2025-3-25 16:16:43 | 只看該作者
25#
發(fā)表于 2025-3-25 21:17:14 | 只看該作者
26#
發(fā)表于 2025-3-26 03:24:22 | 只看該作者
Henning Staar,Jochen Gurt,Monique Janneckveness of the proposed model, a comprehensive comparative analysis is presented, utilizing various performance measures on publicly available datasets. The experimental results conclusively demonstrate the superiority of the proposed model when compared to currently best-performing methods. By devel
27#
發(fā)表于 2025-3-26 04:50:01 | 只看該作者
28#
發(fā)表于 2025-3-26 08:34:23 | 只看該作者
A. Waltersbacher,K. Zok,J. Klosetire text and framing it on its own. The findings of this study can help researchers and practitioners in the field of NLP to choose the most suitable text summarization method for their particular application.
29#
發(fā)表于 2025-3-26 14:31:04 | 只看該作者
Thomas Afflerbach,Katharina Gl?senerachine learning algorithms to predict if the post will go viral or not. This research paper also finds and verifies the key determinants that contribute to a tweet’s success. This paper also concludes with the most appropriate machine learning model for predicting the Virality of a tweet.
30#
發(fā)表于 2025-3-26 17:59:32 | 只看該作者
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