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Titlebook: Emerging Challenges in Intelligent Management Information Systems; Proceedings of 26th Marcin Hernes,Jaros?aw W?tróbski Conference proceed

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樓主: MEDAL
21#
發(fā)表于 2025-3-25 06:03:56 | 只看該作者
22#
發(fā)表于 2025-3-25 10:13:32 | 只看該作者
23#
發(fā)表于 2025-3-25 14:21:51 | 只看該作者
https://doi.org/10.1007/978-1-4615-7416-3 learning methods. Such an approach allows combining several different strategies to detect fake news detections into a unified, cohesive methodology. The performed experiments that used the widely known “Liar, Liar Pants on Fire” benchmark dataset yielded promising results which surpassed outcomes described in the literature.
24#
發(fā)表于 2025-3-25 18:18:13 | 只看該作者
25#
發(fā)表于 2025-3-25 23:43:57 | 只看該作者
26#
發(fā)表于 2025-3-26 00:36:29 | 只看該作者
Supporting Algorithmic Trading with?Machine Learning: Progress in?Backend Technologymunity, with most recent machine learning libraries being Python-based. In this paper, Python optimization is tested on the machine learning models frequently used in algorithmic trading. The results are compared between four different Python versions: Python 3.7, Python 3.8, Python 3.9, and Python 3.11.
27#
發(fā)表于 2025-3-26 04:41:15 | 只看該作者
https://doi.org/10.1007/978-1-4615-2530-1elected companies. Based on the collected data, the association rules were extracted using the Apriori algorithm. While the obtained results are very promising, however, one should also estimate the rate of return for the interrelated transactions to determine the true and ultimate value of discovered relationships.
28#
發(fā)表于 2025-3-26 12:15:44 | 只看該作者
29#
發(fā)表于 2025-3-26 15:35:22 | 只看該作者
30#
發(fā)表于 2025-3-26 20:20:07 | 只看該作者
Forecasting e-learning Course Purchases Using Deep Learning Based on Customer Retentiongraphic data. From the business point of view, it is a crucial benefit for new customers who are not registered in the customer relationship management system, but use anonymously this system via the website.
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