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Titlebook: Artificial Intelligence Applications and Innovations; 3rd IFIP Conference Ilias Maglogiannis,Kostas Karpouzis,Max Bramer Conference procee

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發(fā)表于 2025-3-21 16:13:17 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Artificial Intelligence Applications and Innovations
期刊簡(jiǎn)稱(chēng)3rd IFIP Conference
影響因子2023Ilias Maglogiannis,Kostas Karpouzis,Max Bramer
視頻videohttp://file.papertrans.cn/163/162102/162102.mp4
發(fā)行地址Most up-to-date research on Artificial Intelligence applications and innovations.Includes supplementary material:
學(xué)科分類(lèi)IFIP Advances in Information and Communication Technology
圖書(shū)封面Titlebook: Artificial Intelligence Applications and Innovations; 3rd IFIP Conference  Ilias Maglogiannis,Kostas Karpouzis,Max Bramer Conference procee
影響因子.Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications..
Pindex Conference proceedings 2006
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Artificial Intelligence Applications and Innovations3rd IFIP Conference
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Conference proceedings 2006ations and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications..
地板
發(fā)表于 2025-3-22 05:44:26 | 只看該作者
Patricia Reagan-Cirincione,John Rohrbaughates certain drawbacks of both techniques and produces a compact, comprehensible and efficient rulebase. Experiments in benchmark classification tasks prove that this method does not only reduce computational cost, but it also maintains performance at high levels, offering fast and accurate processing during realtime operations.
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發(fā)表于 2025-3-22 10:38:41 | 只看該作者
Lessons for Democratic Governancem. Policy mappings are utilized in order to enable cross-organizational role assignment. Further optimization on policy mappings is achieved by casting the problem to a partially ordered multi-criteria shortest path problem.
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Lessons for Democratic Governanceo be addressed: how many HMMs do we need for all possible scenarios and how many states for each HMM. We propose in this paper an approach based on an incremental algorithm addressing these two problems. Under the best conditions we obtained the minimal error rate of 1.96% (2 errors in 102 validation entries).
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Marie-Line Germain,Robin S. Greniermmediate advice to the student in order to help them create a valid logic model is presented. Through experimentation it is demonstrated that the users of LMC can have rich interaction and assessment while exploring decision making logic constructs.
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發(fā)表于 2025-3-23 01:44:31 | 只看該作者
Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamicallyates certain drawbacks of both techniques and produces a compact, comprehensible and efficient rulebase. Experiments in benchmark classification tasks prove that this method does not only reduce computational cost, but it also maintains performance at high levels, offering fast and accurate processing during realtime operations.
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發(fā)表于 2025-3-23 08:16:27 | 只看該作者
Optimized Multi-Domain Secure Interoperation using Soft Constraints,m. Policy mappings are utilized in order to enable cross-organizational role assignment. Further optimization on policy mappings is achieved by casting the problem to a partially ordered multi-criteria shortest path problem.
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