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Titlebook: Big Data Technologies and Applications; 11th and 12th EAI In Rui Hou,Huan Huang,Hossam M. Zawbaa Conference proceedings 2023 ICST Institute

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發(fā)表于 2025-3-28 15:50:07 | 只看該作者
42#
發(fā)表于 2025-3-28 19:09:24 | 只看該作者
Outlook: Beyond the Conformal Group, system is, and the more likely it is for covert corruption to proliferate. Only by enhancing audit mode, ensuring data quality, increasing audit efficiency, and reducing audit risk using blockchain technology can the corruption of executives of state-owned companies be effectively stopped.
43#
發(fā)表于 2025-3-29 02:10:16 | 只看該作者
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發(fā)表于 2025-3-29 06:11:23 | 只看該作者
Introduction to Constraint Databasesprocedural score feedback and learning outcome. (3) time allocation for non-evaluative tasks does not mediate the relationship between procedural score feedback and learning outcome. The study suggests some potentially effective measures for MOOC teachers and developers to provide learners with proc
45#
發(fā)表于 2025-3-29 07:30:08 | 只看該作者
Aggregation and Negation Queries,a on the number of the user’s followers and retweet potential in order to generate the user’s impact factor. Experiments are performed using data collected from Twitter and the results show the effectiveness of the proposed approach in identifying fake news spreaders.
46#
發(fā)表于 2025-3-29 11:34:55 | 只看該作者
47#
發(fā)表于 2025-3-29 19:22:33 | 只看該作者
48#
發(fā)表于 2025-3-29 20:59:08 | 只看該作者
Using Requirements Clustering to Discover Dependent Requirements for Hidden Impact Analysisroactively strengthen “Measuring Change Ripple Effect”, Third, new ideas need to be discussed and future research explored. We have used Natural Language Processing (NLP) and Similarity Models to support the model.
49#
發(fā)表于 2025-3-30 00:41:46 | 只看該作者
50#
發(fā)表于 2025-3-30 05:12:34 | 只看該作者
A Comparative Study for Anonymizing Datasets with Multiple Sensitive Attributes and Multiple Recordsility and privacy, for this data while reducing information loss and misuse. The objective of this paper is to use different methods and different anonymization algorithms, like the 1:m-generalization algorithm and Mondrian, and compare them to show which of them maintains data privacy and high util
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