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Titlebook: Computational Intelligence in Data Science; 7th IFIP TC 12 Inter Mieczyslaw Lech Owoc,Felix Enigo Varghese Sicily,P Conference proceedings

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发表于 2025-3-21 19:31:55 | 显示全部楼层 |阅读模式
书目名称Computational Intelligence in Data Science
副标题7th IFIP TC 12 Inter
编辑Mieczyslaw Lech Owoc,Felix Enigo Varghese Sicily,P
视频videohttp://file.papertrans.cn/243/242249/242249.mp4
丛书名称IFIP Advances in Information and Communication Technology
图书封面Titlebook: Computational Intelligence in Data Science; 7th IFIP TC 12 Inter Mieczyslaw Lech Owoc,Felix Enigo Varghese Sicily,P Conference proceedings
描述.These two-volume set IFIP AICT 717 and 718 constitutes the refereed post-conference proceedings of the 7th International Conference on Computational Intelligence in Data Science, ICCIDS 2024, held in Chennai, India, during February 21–23, 2024...The 63 full papers and 9 short papers presented in these proceedings were carefully reviewed and selected from 259 submissions. The conference papers are organized in following topical sections:..Part I: Applications of AI/ML in Natural Language Processing; and Applications of AI/ML in Image Processing...Part II: Applications of AI/ML in KDM, Cloud Computing & Security; Data Analytics;. .and Applications of ML..
出版日期Conference proceedings 2024
关键词Artificial intelligence; Machine learning; Distributed computing methodologies; Electronic commerce; Ent
版次1
doihttps://doi.org/10.1007/978-3-031-69982-5
isbn_softcover978-3-031-69984-9
isbn_ebook978-3-031-69982-5Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2024
The information of publication is updating

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Chat Bot in Banking Sector Using Machine Learning and Natural Language ProcessingThe chat bot has advance machine learning algorithms and natural processing language to adapt for different queries and improve its performance in future and ensuring the perfect accuracy level of the answers. The system is combined with bank database so customers can see their bank details, ensurin
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Improved Evaluator for Subjective Answers Using Natural Language Processinguts like the user response, expected sentences and expected keywords and works with the help of numerical vectors, natural language processing and deep learning approaches and other mathematical calculations to calculate the aggregate score for an answer.
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Self-harm Detection from Texts: A Comparative Study Utilizing BERT, Machine Learning, and Deep Learnhese experiments, we attained exceptional F1 scores of 99% for RoBERTa, 98% for AlBERT, and 96% for BERT base. In contrast, traditional models like Logistic Regression achieved 93%, Random Forest 89%, and deep learning models such as LSTM, BiLSTM and CNN achieved 82%, 93% and 90%, respectively. The
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