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Dataset of manually classified images obtained from a construction site
(Elsevier, 2022)
Acceso abierto
A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset ...
PhyDaC - Stress Detection from Physiological Data in Cattle : Challenges in IoT
(CEUR-WS, 2022)
Acceso abierto
Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types ...
On the relevance of the metadata used in the semantic segmentation of indoor image spaces
(Elsevier, 2021)
Acceso abierto
The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions ...
Humpback Whale’s Flukes Segmentation Algorithms
(Springer, 2021)
Acceso restringido
Photo-identification consists of the analysis of photographs to identify cetacean individuals based on unique characteristics that each specimen of the same species exhibits. The use of this tool allows us to carry out ...
Classification of Breast Cancer and Breast Neoplasm Scenarios Based on Machine Learning and Sequence Features from lncRNAs–miRNAs-Diseases Associations
(Springer, 2021)
Acceso restringido
The influence of non-coding RNAs, such as lncRNAs (long non-coding RNAs) and miRNAs (microRNAs), is undeniable in several diseases, for example, in the formation of neoplasms and cancer scenarios. However, there are ...
Source to source compiler for the automatic parallelization of JavaScript code
(IEEE, 2021)
Acceso restringido
This work focuses on the development of a source-to-source compiler for the automatic parallelization of JavaScript code. The proposed compiler transforms the input code to an AST (Abstract syntax tree). Afterward, we ...