inflate
发表于 2025-3-28 18:10:37
https://doi.org/10.1007/978-3-030-53337-3IoT; artificial intelligence; big data; business process management; chatbots; data analytics; data scienc
achlorhydria
发表于 2025-3-28 21:14:46
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LARK
发表于 2025-3-29 02:02:41
Business Information Systems978-3-030-53337-3Series ISSN 1865-1348 Series E-ISSN 1865-1356
潜移默化
发表于 2025-3-29 04:37:14
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陈旧
发表于 2025-3-29 09:56:38
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homocysteine
发表于 2025-3-29 12:26:34
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羊齿
发表于 2025-3-29 18:45:00
https://doi.org/10.1007/978-3-658-37350-4suitable early Parkinson Patterns, the investigation of this phenomenon is highly relevant. The analysis of sleep is currently done by manual analysis of polysomnography (PSG), which leads to divergent scoring results by different experts. Automated sleep stage detection can help deliver accurate, r
落叶剂
发表于 2025-3-29 23:25:14
https://doi.org/10.1007/978-3-642-46247-4 One of the most crucial steps in the planning of a system includes the modeling of the underlying architecture. However, as of now, no standardized approach exists that facilitates the modeling of big data system architectures (BDSA). In this research, a systematic approach is presented that delive
Albumin
发表于 2025-3-30 01:12:19
https://doi.org/10.1007/978-3-642-46247-4cal analysis. However, especially when working with massive amounts of data, spreadsheet applications have their limitations. To cope with this issue, we introduce a human-in-the-loop approach for scalable data preprocessing using sampling. In contrast to state-of-the-art approaches, we also conside
不安
发表于 2025-3-30 05:08:11
https://doi.org/10.1007/978-3-663-05365-1ance between event data and normative models, and enhancing all aspects of processes. Recently, new techniques have been developed to analyze event data containing uncertainty; these techniques strongly rely on representing uncertain event data through graph-based models capturing uncertainty. In th