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Titlebook: Advances in Artificial Intelligence; 4th Helenic Conferen Grigoris Antoniou,George Potamias,Dimitris Plexous Conference proceedings 2006 Sp

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41#
發(fā)表于 2025-3-28 16:16:59 | 只看該作者
https://doi.org/10.1007/978-3-642-80724-4igent Virtual Agents have become more realistic, intelligent and sociable, with apparent and substantial benefits to domains such as training, tutoring, simulation and entertainment. However, even though many end-users can enjoy these benefits today, the development of such applications is restricte
42#
發(fā)表于 2025-3-28 21:11:04 | 只看該作者
43#
發(fā)表于 2025-3-29 02:15:39 | 只看該作者
44#
發(fā)表于 2025-3-29 05:46:26 | 只看該作者
https://doi.org/10.1007/0-387-28810-4. Admitting that the quality of a decision depends on the quality of the knowledge used to make it, it is argued that the enhancement of the decision making efficiency and effectiveness is strongly related to the appropriate exploitation of all possible organizational knowledge resources. Taking the
45#
發(fā)表于 2025-3-29 07:36:04 | 只看該作者
46#
發(fā)表于 2025-3-29 12:45:19 | 只看該作者
Variable Selection for Cluster Analysisnature of SARSL degrades its performance as the complexity of the search space is increasing. To solve this problem, this paper proposes a population structure of SARSL (PSARSL) which enables multi-point search. Moreover, adaptive partitioning method (APM) is used to reduce the optimization time. Th
47#
發(fā)表于 2025-3-29 16:17:38 | 只看該作者
48#
發(fā)表于 2025-3-29 22:33:05 | 只看該作者
Seriation—Unidimensional Scalinge. This results in frequent patterns occurring in the video signal. We propose an algorithm for the automatic detection of such patterns that exploits the video’s self-similarity induced by the patterns. The approach is applied to the problem of anchor shot detection, but can also be used for other
49#
發(fā)表于 2025-3-30 01:27:45 | 只看該作者
50#
發(fā)表于 2025-3-30 04:14:08 | 只看該作者
Seriation—Maximization of a Dominance Indexrable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing neuro-fuzzy wind power forecasting model taking into
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