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Titlebook: Knowledge-Based and Intelligent Information and Engineering Systems; 14th International C Rossitza Setchi,Ivan Jordanov,Lakhmi C. Jain Conf

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樓主: Wilder
41#
發(fā)表于 2025-3-28 17:16:54 | 只看該作者
0302-9743 as held during September 8–10, 2010 in Cardiff, UK. The conference was organized by the School of Engineering at Cardiff University, UK and KES International. KES2010 provided an international scientific forum for the presentation of the - sults of high-quality research on a broad range of intellige
42#
發(fā)表于 2025-3-28 22:03:17 | 只看該作者
43#
發(fā)表于 2025-3-28 23:08:02 | 只看該作者
44#
發(fā)表于 2025-3-29 03:28:27 | 只看該作者
45#
發(fā)表于 2025-3-29 08:33:28 | 只看該作者
46#
發(fā)表于 2025-3-29 12:55:40 | 只看該作者
Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cardslevel in the case of churn prediction and IRIS datasets. Further, in the present study, we noticed that the rule base size of proposed hybrid is less in churn prediction and IRIS datasets when compared to that of the DT and equal in the case of remaining datasets.
47#
發(fā)表于 2025-3-29 17:01:38 | 只看該作者
Sensitivity Analysis and Automatic Calibration of a Rainfall-Runoff Model Using Multi-objectivesmulti-objective viewpoint. The Nondominated Sorting Differential Evolution (NSDE) was used to calibrate the rainfall-runoff model. The method has been applied for calibration of a test catchment and compared on validation data. The simulations show that the NSDE method possesses the ability to finding the optimal Pareto front.
48#
發(fā)表于 2025-3-29 23:29:19 | 只看該作者
49#
發(fā)表于 2025-3-30 02:43:52 | 只看該作者
A Novel Approach of Process Mining with Event Graphrious process related information. Analysis is conducted to show the advantages of event graph based models compared to Petri nets. A case study is also reported to illustrate the entire mining process. Finally, a preliminary evaluation is conducted to show the merits of our method in terms of precision, generalization and robustness.
50#
發(fā)表于 2025-3-30 06:03:41 | 只看該作者
Embracing Uncertainty: The New Machine Intelligencemachine intelligence which has emerged over the last five years, and which allows prior knowledge from domain experts to be integrated with machine learning techniques to enable a new generation of large-scale applications. The talk will be illustrated with tutorial examples as well as real-world case studies.
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