Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Sheryl Brahnam"'
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
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:
Applied Sciences, Vol 13, Iss 14, p 8029 (2023)
Ecoacoustics is arguably the best method for monitoring marine environments, but analyzing and interpreting acoustic data has traditionally demanded substantial human supervision and resources. These bottlenecks can be addressed by harnessing contemp
Externí odkaz:
https://doaj.org/article/20788c88312d454e9b5bf91e07f72e8f
Publikováno v:
Applied Sciences, Vol 13, Iss 5, p 2987 (2023)
Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance a
Externí odkaz:
https://doaj.org/article/876b94f118804e4d9aeb65bcd69a70f7
Publikováno v:
Applied Computing and Informatics, Vol 17, Iss 1, Pp 19-35 (2021)
This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effec
Externí odkaz:
https://doaj.org/article/3b957b9bea78478dba1f2d608a90db2d
Autor:
Loris Nanni, Yandre M. G. Costa, Rafael L. Aguiar, Rafael B. Mangolin, Sheryl Brahnam, Carlos N. Silla
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2020, Iss 1, Pp 1-14 (2020)
Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification acc
Externí odkaz:
https://doaj.org/article/a24db37327d840a28501f69b6e1a78fb