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Titlebook: Enabling Process Management for Loosely Framed Knowledge-intensive Processes; Steven Mertens Book 2020 Springer Nature Switzerland AG 2020

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11#
發(fā)表于 2025-3-23 12:45:07 | 只看該作者
Practical Machine Learning with PythonThis chapter is the result of the second iteration of RC1 and EC2, and the first iteration of RC2. It describes the development of a process modeling language for loosely framed KiPs..Reprinted from Mertens, S., Gailly, F., Poels, G.: .-.-.. Expert Syst. Appl. 87, 316–334 (2017), with permission from Elsevier...
12#
發(fā)表于 2025-3-23 15:56:41 | 只看該作者
13#
發(fā)表于 2025-3-23 18:42:45 | 只看該作者
14#
發(fā)表于 2025-3-23 22:30:33 | 只看該作者
Anterior Segment Dysgenesis SyndromesThis chapter concludes the dissertation, discusses the generalizability of the results to other domains, lists the research contributions and implications for researchers and practitioners, and outlines possible future research opportunities.
15#
發(fā)表于 2025-3-24 02:40:17 | 只看該作者
Introduction,This chapter introduces the domain of process management, the targeted processes, the problems to be addressed, the research objectives and the chosen approach to achieve these objectives.
16#
發(fā)表于 2025-3-24 07:59:05 | 只看該作者
Towards a Decision-Aware Declarative Process Modeling Language for Knowledge-Intensive Processes,This chapter is the result of the second iteration of RC1 and EC2, and the first iteration of RC2. It describes the development of a process modeling language for loosely framed KiPs..Reprinted from Mertens, S., Gailly, F., Poels, G.: .-.-.. Expert Syst. Appl. 87, 316–334 (2017), with permission from Elsevier...
17#
發(fā)表于 2025-3-24 12:49:23 | 只看該作者
Discovering Loosely Framed Knowledge-Intensive Processes Using DeciClareMiner,This chapter is the result of EC4. It describes the development of an automated process and decision discovery technique for loosely framed KiPs.
18#
發(fā)表于 2025-3-24 17:44:52 | 只看該作者
19#
發(fā)表于 2025-3-24 20:51:26 | 只看該作者
20#
發(fā)表于 2025-3-25 01:55:34 | 只看該作者
How to deal with secondary hypertension,ined to calculate probability estimates for each possible next activity. The recommendations are subsequently generated as a list of possible next activities, sorted according to the highest calculated probability. The strategies were implemented and applied to three different healthcare data sets a
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