Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Maja Braovic"'
Publikováno v:
IEEE Access, Vol 12, Pp 9860-9870 (2024)
The confusion matrix is the tool commonly used for the evaluation of the performance of a classification algorithm. While the computation of the confusion matrix for multi-class classification follows a well-developed procedure, the common approach f
Externí odkaz:
https://doaj.org/article/53dbb372f36f4a3696fcb9f099683ed0
Publikováno v:
Journal of Communications Software and Systems, Vol 19, Iss 2, Pp 158-167 (2023)
This paper explores the problem of media content data analysis with the focus on the phenomenon of vaccination, closely related to the COVID-19 pandemic. The presented research is an extension of the previous work, but it differs in two main areas. F
Externí odkaz:
https://doaj.org/article/44aac49c0c934868abee4dac1bb40cde
In this paper were analyzed the applicability of regression models to the problem of real time viewership prediction. In this paper the analysis is based on precise viewership data obtained from the national company responsible for maintaining the br
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a3b67bf4dfe38a6addc104a07f03459
https://doi.org/10.23919/splitech55088.2022.9854230
https://doi.org/10.23919/splitech55088.2022.9854230
Cogent Confabulation is a comprehensive and simple method for data classification, but is unjustly neglected in modern machine learning. Cogent confabulation uses multiple evidence to classify data items, requiring less computation than Bayesian clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f79d984e6dee07a1cf03b3c023c1a86d
https://www.bib.irb.hr/1199363
https://www.bib.irb.hr/1199363
Publikováno v:
Electronics, Vol 10, Iss 3004, p 3004 (2021)
In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the pa
Publikováno v:
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech).
This paper presents an evaluation of the efficacy and efficiency of various transfer learning methods in wood knot classification. We compared the wood knot classification results from four different convolutional neural networks (Xception, Inception
Nowadays, we are increasingly aware that irresponsible human behavior is the main reason for many instances of environmental pollution, including oil spills in the sea. In order to detect such contaminants in a timely manner and take care of them as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63c6aeb1571169c23cd175a3e8f619aa
https://doi.org/10.23919/splitech52315.2021.9566456
https://doi.org/10.23919/splitech52315.2021.9566456
Publikováno v:
Computer Science & Information Technology.
Confusion matrix is a useful and comprehensive presentation of the classifier performance. It is commonly used in the evaluation of multi-class, single-label classification models, where each data instance can belong to just one class at any given po
Publikováno v:
International journal of electrical and computer engineering systems
Volume 11
Issue 2
Volume 11
Issue 2
Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b926de6264377d396f48ac6f8469f935
https://www.bib.irb.hr/1077454
https://www.bib.irb.hr/1077454
Convolutional Neural Networks and Transfer Learning Based Classification of Natural Landscape Images
Publikováno v:
JUCS-Journal of Universal Computer Science 26(2): 244-267
Scopus-Elsevier
Journal of Universal Computer Science, Vol 26, Iss 2, Pp 244-267 (2020)
Scopus-Elsevier
Journal of Universal Computer Science, Vol 26, Iss 2, Pp 244-267 (2020)
Natural landscape image classification is a difficult problem in computer vision. Many classes that can be found in such images are often ambiguous and can easily be confused with each other (e.g. smoke and fog), and not just by a computer algorithm,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11f680e025a6ba2b12dccc0be9405d57
https://www.bib.irb.hr/1054397
https://www.bib.irb.hr/1054397