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This e-book constitutes the refereed complaints of the eighth overseas convention on man made Intelligence and gentle Computing, ICAISC 2006, held in Zakopane, Poland, in June 2006.
The 128 revised contributed papers provided have been conscientiously reviewed and chosen from four hundred submissions. The papers are equipped in topical sections on neural networks and their purposes, fuzzy structures and their purposes, evolutionary algorithms and their functions, tough units, class and clustering, photograph research and robotics, bioinformatics and clinical functions, quite a few difficulties of man-made intelligence.
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Additional info for Artificial Intelligence and Soft Computing – ICAISC 2006: 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings
They are generally unable to protect against malicious mobile code, insider attacks and unsecured modems. Programming errors cannot be avoided as the complexity of the system and application software is changing rapidly leaving behind some exploitable weaknesses. Intrusion detection is therefore required as an additional wall for protecting systems. Intrusion detection is useful not only in detecting successful intrusions, but also provides important information for timely countermeasures. Intrusion detection is classiﬁed into two types: misuse intrusion detection and anomaly intrusion detection.
Gorgo´ n and M. Wrzesi´ nski The proposed MLP implementation method is based - generally speaking - on the application of specialized subsystems for realization of particular, elementary tasks of the system. The main goal of the design was to enable independent, parallel and thus ﬂuent operation of individual subsystems, without any forced halts. Therefore a pipelined data ﬂow organization and proper buﬀering of intermediate results has been introduced. The internal BlockRAM memories available in Virtex/Spartan series devices - have been used as data buﬀers.
8. 6 shows that the results of the given learning methods and the using regularization parameters are rarely related; therefore, these results also prove that learning regularization parameter is ineﬀectively adapted with learning weight vectors without DM.