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樓主
發(fā)表于 2025-3-21 16:40:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Green Sustainability: Towards Innovative Digital Transformation
編輯Dalia Magdi,Ahmed Abou El-Fetouh,Amit Joshi
視頻videohttp://file.papertrans.cn/389/388516/388516.mp4
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: ;
出版日期Conference proceedings 2023
版次1
doihttps://doi.org/10.1007/978-981-99-4764-5
isbn_softcover978-981-99-4763-8
isbn_ebook978-981-99-4764-5Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:58:12 | 只看該作者
Interpolation und Approximation scenarios on ensuring financial security. It corresponds with current situations, taking into account forecast states for implementation of corrective actions in order to limit internal and external environment threats.
板凳
發(fā)表于 2025-3-22 03:28:01 | 只看該作者
https://doi.org/10.1007/978-3-662-64110-1d services and analyzes the risk if the controls are not applied. In addition, the model will provide the user with various client-side controls as well as certain service level agreement (SLA) guiding parameters.
地板
發(fā)表于 2025-3-22 05:52:18 | 只看該作者
5#
發(fā)表于 2025-3-22 10:07:48 | 只看該作者
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發(fā)表于 2025-3-22 14:41:34 | 只看該作者
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發(fā)表于 2025-3-22 19:14:41 | 只看該作者
Strategic Measures and Alternatives to Ensure the Financial Security of Machine-Building Enterprises scenarios on ensuring financial security. It corresponds with current situations, taking into account forecast states for implementation of corrective actions in order to limit internal and external environment threats.
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發(fā)表于 2025-3-22 22:38:27 | 只看該作者
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發(fā)表于 2025-3-23 02:36:39 | 只看該作者
Machine Learning for Blood Donors Classification Model Using Ensemble Learningncludes data preprocessing and donor classification. Several ensemble models have been developed. The results show that the random forest model led to the best test set accuracy (96.3%), which beat other methods.
10#
發(fā)表于 2025-3-23 08:17:46 | 只看該作者
A Review for Software Defect Prediction Using Machine Learning Algorithms and also prediction of software defects. In this paper, most research approaches concerning defect prediction problems are discussed. According to this study, it is observed that the genetic algorithm with the deep neural network, random forest, and artificial neural networks‘ techniques improve the accuracy of software defect prediction.
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