izvorni znanstveni rad
This chapter will propose solution how to recognise important factors within portfolio, how to derive new information from existing data and evaluate its importance factor. The chapter will also propose methodology for sensitivity evaluation between factors recognised as important. This information has valuable factors for Bayesian network construction. Such created Bayesian network can be used as simulation tool, as well as tool for portfolio optimization. As a simulation tool, such Bayesian network can be for output analysis regarding potential decisions via decision graphs. Also, as an optimization tool, Bayesian network can be used in way of finding optimal value of decision factors upon expected outputs from portfolio. For achieving this aim, evolutionary algorithms will be used as optimization tool.
Bayesian Networks, Data science
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…