Titlebook: 17th WCEAM Proceedings; Sustainable Manageme Georges Abdul-Nour,Minh Ngoc Dinh,Joseph Mathew Conference proceedings 2024 International Soci

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Yiyuan Zheng,Yanyu Lu,Yuwen Jie,Shan Fucription of a DT designed for intelligent maintenance and, in addition, lists a series of characteristics that must be fulfilled when developing these tools, according to the relevant scientific literature. This paper is an excerpt from different contributions of the authors on this topic.

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David Kritchevsky PhD,Jon A. Story PhDapproach is to train deep learning algorithms to detect stagnant water using the YOLOv5 Instance Segmentation model. For the moment, about two-thirds of the water areas in the validation dataset are correctly predicted, implying that a well-trained deep learning model can effectively yield a more satisfactory result.

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Kaveh Sheikhrezaei,Craig Harveyte the effectiveness of our approach, we conducted experiments using practical data and compared it against alternative graph construction methods. Our findings not only confirm the feasibility and efficacy of our approach but also open up avenues for future research in this domain.

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crescendo

2195-4356 rs a range of topics related to sustainable management of en.17th WCEAM Proceedings provides a record of some of the intellectual discussions (including keynote addresses, research paper presentations, panel debates and practical workshops) that took place among the attendees and participants of the

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