By Mieczysław A. Kłopotek (auth.), Khalid Saeed, Rituparna Chaki, Agostino Cortesi, Sławomir Wierzchoń (eds.)
This e-book constitutes the court cases of the twelfth IFIP TC eight overseas convention, CISIM 2013, held in Cracow, Poland, in September 2013. The forty four papers awarded during this quantity have been conscientiously reviewed and chosen from over 60 submissions. they're equipped in topical sections on biometric and biomedical purposes; development acceptance and photo processing; a number of elements of laptop safeguard, networking, algorithms, and business purposes. The e-book additionally comprises complete papers of a keynote speech and the invited talk.
Read Online or Download Computer Information Systems and Industrial Management: 12th IFIP TC8 International Conference, CISIM 2013, Krakow, Poland, September 25-27, 2013. Proceedings PDF
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Additional info for Computer Information Systems and Industrial Management: 12th IFIP TC8 International Conference, CISIM 2013, Krakow, Poland, September 25-27, 2013. Proceedings
The traditional techniques, based on magnetic or chip cards or on password systems no longer meet the contemporary security requirements. The biometric methods of identiﬁcation provide the high security level and are very convenient. They can be applied separately or together with the traditional access control solutions in order to prevent the room and computer networks from the nefarious use by the unauthorized people. Currently, the most popular biometric techniques are based on: 1. 2. 3. 4.
The sought discriminant function should produce for each data vector a score (called also decision score) allowing to assign that vector to one of the considered classes. We will consider only some basic discriminant functions which - to our opinion - are advised to start with, when attempting to build a discriminant function providing a decision boundary between two groups of observed data. In next subsections we consider in turn: (i) Linear discriminant function formulated and solved as ordinary multivariate regression function with predicted values y = +1 for one of the groups and y = −1 for the other group.
Mechanical Systems and Signal Processing 23(5), 1528–1534 (2009) 2. : Visualizing Some Multi-Class Erosion Data Using Kernel Methods. , Vichi, M. ) Proceedings in Computational Statistics, 17th Symposium Held in Rome, pp. 805–812. Physica-Verlag (2006) 3. : Visualizing of some multi-class erosion data using GDA and supervised SOM. , et al. ) Biometrics, Computer Security Systems and Artiﬁcial Intelligence Applications, pp. 13–22. Springer (2006) 4. : Visualization of Five Erosion Risk Classes using Kernel Discriminants.