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Titlebook: Enterprise Information Systems; 24th International C Joaquim Filipe,Micha? ?mia?ek,Slimane Hammoudi Conference proceedings 2023 The Editor(

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發(fā)表于 2025-3-21 19:38:07 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Enterprise Information Systems
副標(biāo)題24th International C
編輯Joaquim Filipe,Micha? ?mia?ek,Slimane Hammoudi
視頻videohttp://file.papertrans.cn/312/311480/311480.mp4
叢書(shū)名稱Lecture Notes in Business Information Processing
圖書(shū)封面Titlebook: Enterprise Information Systems; 24th International C Joaquim Filipe,Micha? ?mia?ek,Slimane Hammoudi Conference proceedings 2023 The Editor(
描述This book constitutes the refereed post-conference proceedings of the 24th?International Conference on Enterprise Information Systems, ICEIS 2022, This?conference was held virtually due to the COVID-19 crisis, during April 25-27, 2022..The 20 full papers included in this book were carefully reviewed and selected from 197 submissions. They were organized in topical sections as follows: Databases and Information Systems Integration;?Artificial Intelligence and Decision Support Systems;?Information Systems Analysis and Specification;?Human-Computer Interaction; and Enterprise Architecture..
出版日期Conference proceedings 2023
關(guān)鍵詞Information systems; Artificial Intelligence; Decision Support Systems; HCI – human computer interactio
版次1
doihttps://doi.org/10.1007/978-3-031-39386-0
isbn_softcover978-3-031-39385-3
isbn_ebook978-3-031-39386-0Series ISSN 1865-1348 Series E-ISSN 1865-1356
issn_series 1865-1348
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書(shū)目名稱Enterprise Information Systems影響因子(影響力)




書(shū)目名稱Enterprise Information Systems影響因子(影響力)學(xué)科排名




書(shū)目名稱Enterprise Information Systems網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Enterprise Information Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Enterprise Information Systems被引頻次




書(shū)目名稱Enterprise Information Systems被引頻次學(xué)科排名




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A Storage Location Assignment Problem with?Incompatibility and?Isolation Constraints: An?Iterated Lo distance from hospitals and pharmacies, allowing both a reduction in warehouse costs and prompt replenishment in case of shortages. However, for this practice to lead to effective optimization, warehouses need to be equipped with efficient order fulfillment and picking strategies, as well as a stor
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Implementation Solutions for FAIR Clinical Research Data ManagementHowever, collecting and processing these data and publishing them according to rules defined by the responsible institutions is challenging. This paper addresses a modular, scalable, distributed, and flexible platform to promote such processing and publication in a FAIR manner. In addition, we descr
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Resource Planning in?Workflow Nets Based on?a?Symbolic Time Constraint Propagation Mechanismver the activities of Workflow Management Systems. Using Colored Petri nets, it is possible to separate the process model from the resource model and formally define the communication mechanisms between the two models. Each token from the Colored Petri net will then be able to carry the time informa
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Implementation of?the?Maintenance Cost Optimization Function in?Manufacturing Execution Systems the optimal replacement time for a?spare part has been extensively studied in the context of a?single part. The available analytical models allow for a?very accurate estimation of further service life and savings related to the replacement. Nevertheless, the simultaneous optimization problem of the
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: A Decision Guidance Framework and?System for?Design, Analysis and?Optimization of?Cloud-Manufacturpredictability and agility in the traditional idea-to-market paradigm, stiff hindrances have to be overcome, which is perceived as the manufacturing-entrepreneurship disconnect. Toward bridging this gap, we propose and develop . - a decision guidance system to recommend Pareto-optimal instantiation
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Efficient Deep Neural Network Training Techniques for?Overfitting Avoidanceo make predictions. Overfitting is the term used to describe how successfully the prediction model created by the machine learning algorithm adapts to the training data. When a network is improperly adapted to a limited set of input data, overfitting occurs. In this scenario, the predictive model wi
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