Zobrazeno 1 - 10
of 314
pro vyhledávání: '"Loris Nanni"'
Publikováno v:
IEEE Access, Vol 12, Pp 74218-74229 (2024)
The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures.
Externí odkaz:
https://doaj.org/article/cac40f03d5c34895ae9dc2b01a63f8ba
Publikováno v:
Analytics, Vol 2, Iss 3, Pp 676-693 (2023)
For robust classification, selecting a proper classifier is of primary importance. However, selecting the best classifiers depends on the problem, as some classifiers work better at some tasks than on others. Despite the many results collected in the
Externí odkaz:
https://doaj.org/article/a4923a2742dc4997917477b817af9555
Publikováno v:
Signals, Vol 4, Iss 3, Pp 524-538 (2023)
This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically convolutional neural networks (CNNs), to analyze digital images of for
Externí odkaz:
https://doaj.org/article/050b1c52284948259671bfa556a5edbf
Publikováno v:
Applied Computing and Informatics, Vol 19, Iss 3/4, Pp 265-283 (2023)
In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional
Externí odkaz:
https://doaj.org/article/246ca0235e1d4c4b82642d62c49fd786
Publikováno v:
IEEE Access, Vol 11, Pp 124962-124974 (2023)
This paper presents a study on an automated system for image classification, which is based on the fusion of various deep learning methods. The study explores how to create an ensemble of different Convolutional Neural Network (CNN) models and transf
Externí odkaz:
https://doaj.org/article/377458e5525d4efa98a8b00001efe8c3
Autor:
Sheryl Brahnam, Loris Nanni, Shannon McMurtrey, Alessandra Lumini, Rick Brattin, Melinda Slack, Tonya Barrier
Publikováno v:
Applied Computing and Informatics, Vol 19, Iss 1/2, Pp 122-143 (2023)
Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are two
Externí odkaz:
https://doaj.org/article/6b3fd09aabc04f02a9454cdfb82ddcfc
Publikováno v:
IEEE Access, Vol 11, Pp 8810-8823 (2023)
Convolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply in medical fields and similar fields. However, when large quantities of sampl
Externí odkaz:
https://doaj.org/article/799f4d53fb7f4ff4b56857e98431a6a0
Publikováno v:
Signals, Vol 3, Iss 4, Pp 911-931 (2022)
Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the
Externí odkaz:
https://doaj.org/article/135215f098e740ffab2803b4ed951efa
Publikováno v:
Information, Vol 14, Iss 12, p 657 (2023)
To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing
Externí odkaz:
https://doaj.org/article/ac2882ffa1614d029ed4eee59d53161b
Publikováno v:
Signals, Vol 3, Iss 2, Pp 341-358 (2022)
Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification
Externí odkaz:
https://doaj.org/article/81970f2b0235469aa4a253aa445ff74c