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Titlebook: Enterprise Information Systems; 13th International C Runtong Zhang,Juliang Zhang,José Cordeiro Conference proceedings 2012 Springer-Verlag

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樓主: 哥哥大傻瓜
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發(fā)表于 2025-3-23 13:27:09 | 只看該作者
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發(fā)表于 2025-3-23 16:12:19 | 只看該作者
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發(fā)表于 2025-3-23 20:43:11 | 只看該作者
Adaptive Information Integration: Bridging the Semantic Gap between Numerical Simulationscialized tools. Hence, the interconnection of tools is the only way, because otherwise the achievable prediction accuracy would be insufficient. In this chapter, a framework is presented that helps to interconnect heterogeneous simulation tools, considering their incompatible file formats, different semantics of data and missing data consistency.
14#
發(fā)表于 2025-3-24 00:11:26 | 只看該作者
PAR-COM: A New Methodology for Post-processing Association Rulesnts the PAR-COM methodology that, by combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. An experimental study demonstrates the potential of PAR-COM to minimize the user’s effort during the post-processing process.
15#
發(fā)表于 2025-3-24 03:50:12 | 只看該作者
A Study on Noisy Typing Stream Analysis Using Machine Learning Approachhm to correct typing mistakes. Results demonstrate a high performance of the designed model, about 70% of all tests score above Basic Correction Rate, and simulation also shows a very unstable trend of user’s ‘Hitting Adjacent Key Errors’ behavior with specific datasets used by the research. Further work is suggested in the conclusion.
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發(fā)表于 2025-3-24 07:20:35 | 只看該作者
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發(fā)表于 2025-3-24 22:29:37 | 只看該作者
Conclusion: Force and Trust in the Future,e resources, so that mobile devices can continue with clustering at acceptable accuracy even under low memory resources. Experimental results show that not only is RA-HCluster more accurate than RA-VFKM, it is able to maintain a low and stable memory usage.
20#
發(fā)表于 2025-3-25 02:29:49 | 只看該作者
The Use of Human Beings in Researchin purpose is to discover new uncertain frequent patterns from iterative versions of the mUF-tree. Our preliminary performance study shows that the UF-Evolve algorithm is efficient and scalable for mining additional uncertain frequent patterns with different sizes of uncertain databases.
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