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### Evaluating Expert Estimators Based on Elicited Competences

Karna, Hrvoje, Gotovac, Sven
Journal of information and organizational sciences, 2015, Vol.39(1), pp.49-63 [Peer Reviewed Journal]
Central and Eastern European Online Library (C.E.E.O.L.)
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### Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services

Kraljević, Goran, Gotovac, Sven
Automatika, 01 January 2010, Vol.51(3), pp.275-283 [Peer Reviewed Journal]
Taylor & Francis (Taylor & Francis Group)
Available
Title: Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services
Author: Kraljević, Goran; Gotovac, Sven
Subject: Data Mining Applications ; Prepaid Churn Model ; Neural Networks ; Decision Trees ; Logistic Regression ; Aplikacije Dubinske Analize Podataka ; Prepaid Churn Model ; Neuronske Mreže ; Stabla Odlučivanja ; Logisticčka Regresija ; Engineering
Description: This paper defines an advanced methodology for modeling applications based on Data Mining methods that represents a logical framework for development of Data Mining applications. Methodology suggested here for Data Mining modeling process has been applied and tested through Data Mining applications for predicting Prepaid users churn in the telecom industry. The main emphasis of this paper is defining of a successful model for prediction of potential Prepaid churners, in which the most important part is to identify the very set of input variables that are high enough to make the prediction model precise and reliable. Several models have been created and compared on the basis of different Data Mining methods and algorithms (neural networks, decision trees, logistic regression). For the modeling examples we used WEKA analysis tool.
Is part of: Automatika, 01 January 2010, Vol.51(3), pp.275-283
Identifier: 0005-1144 (ISSN); 1848-3380 (E-ISSN); 10.1080/00051144.2010.11828381 (DOI)
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### Data Mining Approach to Effort Modeling on Agile Software Projects

Karna, Hrvoje, Gotovac, Sven, Vicković, Linda
Informatica, Jun 2020, Vol.44(2), pp.231-239 [Peer Reviewed Journal]
© ProQuest LLC All rights reserved, Computing Database (Alumni edition), ProQuest Computing, Advanced Technologies & Aerospace Database, ProQuest Computer Science Journals, East Europe, Central Europe Database, Publicly Available Content Database, Advanced Technologies Database with Aerospace, Technology Research Database, ProQuest Central, ProQuest Advanced Technologies & Aerospace Collection, ProQuest Technology Collection, ProQuest Computer Science Collection, ProQuest SciTech Collection, Computer and Information Systems Abstracts, ProQuest Central (new), ProQuest Central K-12, ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest Central China, ProQuest One Academic, Advanced Technologies & Aerospace Index (ProQuest)
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Title: Data Mining Approach to Effort Modeling on Agile Software Projects
Author: Karna, Hrvoje; Gotovac, Sven; Vicković, Linda
Subject: Research ; Machine Learning ; Product Development ; Project Management ; Data Mining ; Product Development ; Data Mining ; Prediction Models ; Iterative Methods ; Clustering ; Algorithms ; Methods ; Software Engineering ; Algorithms ; Clustering ; Agile Manufacturing ; Software Engineering
Description: Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.
Is part of: Informatica, Jun 2020, Vol.44(2), pp.231-239
Identifier: 03505596 (ISSN); 18543871 (E-ISSN); 10.31449/inf.v44i2.2759 (DOI)
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### Improving Sentence Retrieval Using Sequence Similarity

Boban, Ivan, Doko, Alen, Gotovac, Sven
Applied Sciences, 2020, Vol.10(12), p.4316 [Peer Reviewed Journal]

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### Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions

Božić-Štulić, Dunja, Marušić, Željko, Gotovac, Sven
International Journal of Computer Vision, 2019, Vol.127(9), pp.1256-1278 [Peer Reviewed Journal]
Springer Science & Business Media B.V.
Available
Title: Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions
Author: Božić-Štulić, Dunja; Marušić, Željko; Gotovac, Sven
Subject: Convolutional neural networks ; RCNN ; Salient object detection ; Unmanned aerial vehicles (UAV) ; Search and rescue ; SAR image database
Description: In this paper, we propose a novel approach to person detection in UAV aerial images for search and rescue tasks in Mediterranean and Sub-Mediterranean landscapes. Person detection in very high spatial resolution images involves target objects that are relatively small and often camouflaged within the environment; thus, such detection is a challenging and demanding task. The proposed method starts by reducing the search space through a visual attention algorithm that detects the salient or most prominent segments in the image. To reduce the number of non-relevant salient regions, we selected those regions most likely to contain a person using pre-trained and fine-tuned convolutional neural networks (CNNs) for detection. We established a special database called HERIDAL to train and test our model. This database was compiled for training purposes, and it contains over 68,750 image patches of wilderness acquired from an aerial perspective as well as approximately 500 labelled full-size real-world...
Is part of: International Journal of Computer Vision, 2019, Vol.127(9), pp.1256-1278
Identifier: 0920-5691 (ISSN); 1573-1405 (E-ISSN); 10.1007/s11263-019-01177-1 (DOI)
• Several versions