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Titlebook: Artificial Intelligence, Finance, and Sustainability; Economic, Ecological Thomas Walker,Dieter Gramlich,Akram Sadati Book 2024 The Editor(

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樓主: Buchanan
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發(fā)表于 2025-3-23 10:00:07 | 只看該作者
Assessing Physical Climate Risks: An AI-Powered Tool for Businessesacity to leverage outputs. To meet the needs of countless small and large companies facing physical climate risks, we have developed an innovative artificial intelligence (AI) powered methodology. Our methodology leverages widely recognized, open-access climate data and models. Refined through empir
12#
發(fā)表于 2025-3-23 15:11:31 | 只看該作者
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發(fā)表于 2025-3-23 19:56:06 | 只看該作者
The Law of Fintech: How Artificial Intelligence and Innovative Technologies Contribute to a Sustainaproach of regulating Fintech to ensure its sustainable growth. The chapter first discusses a variety of technology developments in financial services, especially artificial intelligence (AI), that have formed the foundation of modern Fintech. Then, the chapter evaluates the main business models of F
14#
發(fā)表于 2025-3-23 23:36:20 | 只看該作者
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發(fā)表于 2025-3-24 05:02:28 | 只看該作者
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發(fā)表于 2025-3-24 08:31:05 | 只看該作者
Arteries, veins and lymphatic vesselsternal climate change initiatives, among other things. The . viewpoint addresses how to optimize human resource deployment, candidate experience, and customized employee pension plans. The . standpoint considers how AI might help improve governance and transparency by automating daily processes, det
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發(fā)表于 2025-3-24 11:46:39 | 只看該作者
18#
發(fā)表于 2025-3-24 15:09:31 | 只看該作者
19#
發(fā)表于 2025-3-24 19:58:48 | 只看該作者
Handbook of Biomedical Image Analysisenerating profits while adhering to ethical and environmental considerations. This chapter provides an overview of pairs trading, encompassing both established practices and recent developments, with a focus on emerging trends such as artificial intelligence (AI) and machine learning (ML). While the
20#
發(fā)表于 2025-3-25 00:17:51 | 只看該作者
Recent Advances in the Level Set Method,port Vector Regression (SVR) model demonstrates the highest level of precision in predicting carbon emissions, with a 21.55% increase in Mean Absolute Error (MAE). Notably, natural gas, diesel oil, and raw coal are identified as the most influential factors in the model. Additionally, the influence
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