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Artificial Intelligence and Security in Computing Systems: by Facchinetti Gisella, Franci Francesco, Mastroleo Giovanni,

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By Facchinetti Gisella, Franci Francesco, Mastroleo Giovanni, Pagliaro Vittorio (auth.), Jerzy Sołdek, Leszek Drobiazgiewicz (eds.)

Artificial Intelligence and safety in Computing Systems is a peer-reviewed convention quantity concentrating on 3 components of perform and study growth in details applied sciences:

-Methods of synthetic Intelligence offers equipment and algorithms that are the root for functions of man-made intelligence environments.
-Multiagent Systems comprise laboratory examine on multiagent clever structures in addition to upon their purposes in transportation and data platforms.
-Computer safeguard and Safety offers strategies and algorithms to be able to be of significant curiosity to practitioners. ordinarily, they concentrate on new cryptographic algorithms (including a symmetric key encryption scheme, hash features, mystery new release and sharing schemes, and safe information storage), a proper language for coverage entry keep an eye on description and its implementation, and chance administration equipment (used for non-stop research either in allotted community and software program improvement projects).

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Additional info for Artificial Intelligence and Security in Computing Systems: 9th International Conference, ACS’ 2002 Międzyzdroje, Poland October 23–25, 2002 Proceedings

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8, 199-251; 301-357 ; 9, 43-80. pl Abstract: The paper presents a problem of vessel traffic optimisation with the use of fuzzy linear programming. The Swinoujscie-Szczecin fairway has been taken as a practical example. The L-R representation of fuzzy numbers was used for the model description. The interpretation of fuzzy numbers has been presented. As deviations from the fixed values of constraint coefficients have been allowed; the solutions obtained are more realistic. The approach enables a flexible and safer formulation of an optimisation task.

Artificial Intelligence and Security in Computing Systems © Springer Science+Business Media New York 2003 54 The neurons of hidden and output layers ofMLP generate their output signals y according to the rule (1): (1) where Xi is the i-th input signal (incoming to the given neuron) , Wi is the weight parameter of i-th input connection, and f is so called activation (transfer) function. g. sigmoid or hyperbolic tangent) , what allows MLP to model non-linear dependencies in the analysed data. The development of neural model is based on the network learning (training) process.

The case of Szczecin-Swinoujscie fairway has been discussed. The L-R representation of fuzzy numbers has been used for the description of constraint coefficients. The form of fuzzy constraints for linear programming problems is necessary in the case when accurate values of constraint coefficients are not known. By transforming fuzzy constraints into non-fuzzy linear constraints we can conventional methods for solving a linear programming problem . The results will depend on the degree of aspiration of fulfiIling the fuzzy inequalities.

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