| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Search the digital library catalog Help

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 10 / 15
First pagePrevious page12Next pageLast page
1.
Landslide assessment of the Strača basin (Croatia) using machine learning algorithms
Miloš Marjanović, Miloš Kovačević, Branislav Bajat, Snježana Mihalić Arbanas, Biljana Abolmasov, 2011, original scientific article

Abstract: In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starča Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms.
Keywords: landslides, support vector machines, decision trees classifier, Starča Basin
Published in DKUM: 13.06.2018; Views: 651; Downloads: 50
.pdf Full text (382,76 KB)
This document has many files! More...

2.
A statistical model for shutdowns due to air quality control for a copper production decision support system
Khalid Aboura, 2015, original scientific article

Abstract: Background: In the mid-1990s, a decision support system for copper production was developed for one of the largest mining companies in Australia. The research was conducted by scientists from the largest Australian research center and involved the use of simulation to analyze options to increase production of a copper production facility. Objectives: We describe a statistical model for shutdowns due to air quality control and some of the data analysis conducted during the simulation project. We point to the fact that the simulation was a sophisticated exercise that consisted of many modules and the statistical model for shutdowns was essential for valid simulation runs. Method: The statistical model made use of a full year of data on daily downtimes and used a combination of techniques to generate replications of the data. Results: The study was conducted with a high level of cooperation between the scientists and the mining company. This contributed to the development of accurate estimates for input into a support system with an EXCEL based interface. Conclusion: The environmental conditions affected greatly the operations of the production facility. A good statistical model was essential for the successful simulation and the high budget expansion decision that ensued.
Keywords: decision support system, simulation, statistical modelling
Published in DKUM: 28.11.2017; Views: 682; Downloads: 300
.pdf Full text (339,98 KB)
This document has many files! More...

3.
Organizational learning supported by machine learning models coupled with general explanation methods : a case of B2B sales forecasting
Marko Bohanec, Marko Robnik Šikonja, Mirjana Kljajić Borštnar, 2017, original scientific article

Abstract: Background and Purpose: The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML) models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting. Design/Methodology/Approach: Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilizes R software environment. Results: ML model was developed in several design cycles involving users. It was evaluated in the company for several months. Results suggest that based on the explanations of the ML model predictions the users’ forecasts improved. Furthermore, when the users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model. Conclusions: The proposed model promotes understanding, foster debate and validation of existing beliefs, and thus contributes to single and double-loop learning. Active participation of the users in the process of development, validation, and implementation has shown to be beneficial in creating trust and promotes acceptance in practice.
Keywords: decision support, organizational learning, machine learning, explanations
Published in DKUM: 01.09.2017; Views: 1158; Downloads: 196
.pdf Full text (1,31 MB)
This document has many files! More...

4.
Decision making under conditions of uncertainty in agriculture : a case study of oil crops
Karmen Pažek, Črtomir Rozman, 2009, review article

Abstract: In decision under uncertainty individual decision makers (farmers) have to choose one of a set number of alternatives with complete information about their outcomes but in the absence of any information or data about the probabilities of the various state of nature. This paper examines a decision making under uncertainty in agriculture. The classical approaches of Wald’s, Hurwicz’s, Maximax, Savage’s and Laplace’s are discussed and compared in case study of oil pumpkin production and selling of pumpkin oil. The computational complexity and usefulness of the criterion are further presented. The article is concluded with aggregate the results of all observed criteria and business alternatives in the conditions of uncertainty, where the business alternative 1 is suggested.
Keywords: uncertainty, Wald’s, Hurwicz’s, Maximax, Savage’s and Laplace’s criterion, decision support system, agriculture, oil crops
Published in DKUM: 20.07.2017; Views: 726; Downloads: 94
.pdf Full text (174,59 KB)
This document has many files! More...

5.
Application of analytical hierarchy process in agriculture
Karmen Pažek, Črtomir Rozman, 2005, original scientific article

Abstract: Hierarchical decision models are a general decision support methodology aimed at the classification or evaluation of options that accur in desion-making processes. Decision models are typically developed through the decomposition of complex decision problems into smaller and less comple subproblems. This paper presents an approach to the development and implementation of multicriteria decision model based on Analytical Hierarchy Process - AHP (Expert Choice, EC). Likewise, the AHP is used as a potential multicriteria decision making method for application in agriculture. In order to show the implementation of explained MCDA methods in real situation in agriculture, theapplication of AHP on a sample model farm is presented in the second part of the article.
Keywords: multicriteria decision analysis, MCDA, analytical hierarchy process, AHP, decision support system, DSS, agriculture
Published in DKUM: 20.07.2017; Views: 983; Downloads: 175
.pdf Full text (177,82 KB)
This document has many files! More...

6.
7.
Comparative analysis of collaborative and simulation based learning in the management environment
Mirjana Kljajić Borštnar, 2012, original scientific article

Abstract: Purpose of the study is to compare two different approaches to the collaborative problem solving one in a highly controlled laboratory experiment: Optimisation of business politics using business simulator at different experimental condition which reflect different feedback information structure and one in a collaborative environment of the social media, characterised by non-structured, rule-free and even chaotic feedback information. Comparative analyses of participant’s opinion who participate in experiments have been considered in order to find common characteristics relevant for group/collaborative problem solving. Based on these findings a general explanatory causal loop model of collaborative learning during problem solving was built.
Keywords: group decision support, information structure, collaborative learning, simulation model
Published in DKUM: 10.07.2015; Views: 1083; Downloads: 333
.pdf Full text (325,88 KB)
This document has many files! More...

8.
Computer aided decision support in product design engineering
Marina Novak, 2012, original scientific article

Abstract: Product design engineering is a complex discipline, which is undergoing a transformation from informal and largely experience-based domain to scientific oriented domain. Computational intelligence can contribute greatly to product design process, as it is becoming more and more evident that adding intelligence to existing computer aids, such as computer aided design systems, can lead to significant improvements in terms of effectiveness and reliability of various tasks within product design engineering. Providing computer aided decision support is one of the computational intelligence methods that proved to be effective in enabling more intelligent and less experience-dependent design performance. In this paper, some of the most crucial areas of product design engineering process that require additional computational intelligence in terms of computer aided decision support are presented together with some examples of intelligent knowledge-based modules applied to this areas.
Keywords: product development, design engineering, design for X, computational intelligence, decision support, knowledge-based modules
Published in DKUM: 10.07.2015; Views: 1371; Downloads: 76
.pdf Full text (1,73 MB)
This document has many files! More...

9.
10.
Search done in 0.54 sec.
Back to top
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica