1.

Distributed embedded control systems : improving dependability with coherent design, (Advances in industrial control)

This book discusses embedded systems with regard to their reliability. It presents the main results of the project IST-2001-32122; IFATIS - Intelligent Fault Tolerant Control in Integrated Systems, WP4: Fault Tolerance in Computer Systems (under the supervision of our group); European 5th Framework Programme.

COBISS.SI-ID: 11970070

2.

Bpel cookbook : best practices for SOA-based integration and composite applications development

This book of best practices for SOA-based integration and composite applications development was awarded with the Best SOA/Web Services Book Award in the SOA World Reader Awards.

COBISS.SI-ID: 11580438

3.

Knowledge discovery with classification ruls in a cardiovascular dataset

In this paper, we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rule induction called AREX, using the evolutionary induction of decision trees and automatic programming, is also introduced. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The final result is the discovery of possibly new medical knowledge in the field of pediatric cardiology.

COBISS.SI-ID: 10103062

4.

Automated software size estimation based on function points using UML models

The presented results of the research work enable a systematic approach to software size estimation that is important for accurate project planning. A unified mapping of UML models into function points is proposed. The formal approach enables automatic size estimation, which leads to better usability, the reduction of subjectiveness during estimation and an increase of precision during estimation.

COBISS.SI-ID: 10010134

5.

Autonomous evolutionary algorithm in medical data analysis

An autonomous evolutionary algorithm for constructing decision trees is presented. The algorithm requires no, or minimal, human interaction and shows some interesting properties when used on different medical datasets. The algorithm uses a non-standard implicit fitness evaluation in the selection phase of a co-evolving environment. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some dataset is just difficult or impossible to analyze.

COBISS.SI-ID: 10102550