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Titlebook: Decision Support System for the Location of Healthcare Facilities; SitHealth Evaluation Marta Dell‘Ovo,Alessandra Oppio,Stefano Capolongo B

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樓主: 遮陽傘
21#
發(fā)表于 2025-3-25 05:05:36 | 只看該作者
Studies in Philosophy and Religionnd sometimes conflictual criteria, the presence of different stakolders with their own viewpoints and the necessity to find a trade-off among dimensions involved. Given these premises the contribution is aimed at proposing a detailed analysis of the State of the Art (SoA) of the location problem und
22#
發(fā)表于 2025-3-25 11:00:10 | 只看該作者
23#
發(fā)表于 2025-3-25 14:38:10 | 只看該作者
24#
發(fā)表于 2025-3-25 16:20:40 | 只看該作者
Introduction — Why Death Matters to Policyo the public domain, as for the location of healthcare facilities, the use of DSSs could be fundamental to justify choices made and to easily communicate the results to citizens. Given the nature of the decision problem, the support of the Multicriteria-Spatial Decision Support Systems (MC-SDSS) has
25#
發(fā)表于 2025-3-25 22:43:47 | 只看該作者
Introduction to the Interview Processtion. In fact, by including different stakeholders in the decision process different knowledge domains can be integrated. To facilitate this processes, Decision Support Systems (DSSs) have been framed to support stakeholders in decision making for specific purposes. The contribution aims at reflecti
26#
發(fā)表于 2025-3-26 03:36:27 | 只看該作者
SpringerBriefs in Applied Sciences and Technologyhttp://image.papertrans.cn/d/image/264275.jpg
27#
發(fā)表于 2025-3-26 06:52:57 | 只看該作者
Decision Support System for the Location of Healthcare Facilities978-3-030-50173-0Series ISSN 2191-530X Series E-ISSN 2191-5318
28#
發(fā)表于 2025-3-26 11:16:30 | 只看該作者
29#
發(fā)表于 2025-3-26 14:20:59 | 只看該作者
978-3-030-50172-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
30#
發(fā)表于 2025-3-26 20:29:12 | 只看該作者
,Improved Faster R-CNN Detection Algorithm for?Small Unmanned Aerial Vehicle Targets,ight important features. The experimental results demonstrate that, compared with the Faster R-CNN model, the SDPnet achieved a precision rate increase of 57.34% and a total error rate decrease of 40.51% on the self-collected dataset. Furthermore, when compared to YOLOv5, SDPnet also exhibits relative superiority.
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