izvorni znanstveni rad
Chapter will propose novel approach which combine traditional machine learning approach in churn management and customer satisfaction evaluation which unite traditional machine learning approach and expert based approach which leans on event- based management. Core of the proposed framework is hybrid fuzzy expert system which can contain variety data mining predictive models responsible for some specific areas as addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within rule blocks. Chapter will introduce permanent monitoring and self-learning approach-based o mentioned approach with workflow how revealed patterns can be applied for continual portfolio management improvement. Proposed solution unite advanced analytical techniques with decision making process within holistic self-learning framework.
Portfolio management, machine learning, big data, analytical techniques
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…
