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Using Deep Learning for Morphological Classification in Pigs with a Focus on Sanitary Monitoring
The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional Neural Networks) algorithms to classify pig body conditions in normal or not normal conditions, with a focus on characteristics that are observed in sanitary monitoring, and were used six different algorithms to do this task.
Pig aggression classification using CNN, Transformers and Recurrent Networks
Thus, the development of applications can assist breeders in making decisions to improve production performance and reduce costs, once the animal behavior is analyzed by humans and this can lead to susceptible errors and time consumption.
Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction
Smart indoor tourist attractions, such as smart museums and aquariums, usually require a significant investment in indoor localization devices.
Aedes aegypti Egg Counting with Neural Networks for Object Detection
Aedes aegypti is still one of the main concerns when it comes to disease vectors.
Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding.
A New Machine Learning Dataset of Bulldog Nostril Images for Stenosis Degree Classification
For this evaluation, the problem was modeled in two different ways: first, as a three-class classification problem (mild or open, moderate, and severe); second, as a binary classification problem, with severe stenosis as target.
