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Titlebook: Knowledge Management and Acquisition for Smart Systems and Services; 13th Pacific Rim Kno Yang Sok Kim,Byeong Ho Kang,Deborah Richards Conf

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樓主: Clique
51#
發(fā)表于 2025-3-30 08:47:02 | 只看該作者
52#
發(fā)表于 2025-3-30 12:41:36 | 只看該作者
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發(fā)表于 2025-3-30 18:54:14 | 只看該作者
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發(fā)表于 2025-3-30 22:54:02 | 只看該作者
Towards Management of the Data and Knowledge Needed for Port Integration: An Initial Ontologye a vocabulary for the consensual discussion of the port integration decision and to capture the relevant factors and measurement variables that can be used to manage and uncover new knowledge via data mining.
55#
發(fā)表于 2025-3-31 03:30:02 | 只看該作者
Conference proceedings 2014 from 69 initial submissions. They deal with knowledge acquisition, expert systems, intelligent agents, ontology engineering, foundations of artificial intelligence, machine learning, data mining, Web mining, information systems, Web and other applications.
56#
發(fā)表于 2025-3-31 07:43:50 | 只看該作者
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發(fā)表于 2025-3-31 10:03:03 | 只看該作者
Academic Performance in a 3D Virtual Learning Environment: Different Learning Types vs. Different Cl Virtual Learning Environment (VLE) as well as on their academic performance. The results showed that, unlike class type, there is a significant difference between learners’ in their usage of the VLE. Moreover, the results showed that the levels of using a VLE significantly correlated with learners’ academic performance.
58#
發(fā)表于 2025-3-31 16:34:29 | 只看該作者
59#
發(fā)表于 2025-3-31 20:51:48 | 只看該作者
Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection in the Cloud Softwaron the results, we investigated important variables for classifying defecting customers using a random forest and built a prediction model using a decision tree. The final results indicate that defecting customers are mainly characterized by their loyalty and their number of total payments.
60#
發(fā)表于 2025-4-1 00:03:09 | 只看該作者
The Performance of Objective Functions for Clustering Categorical Dataheir global optima, which we argue is a better measurement than average clustering results. The conclusion is that within-cluster dispersion is generally a better objective for discovering cluster structures. Moreover, we evaluate the performance of various distance measures on within-cluster dispersion, and give some useful observations.
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