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Now showing items 141-150 of 151
Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning
(Elsevier, 2022-03)
Fishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control ...
Robust labeling of human motion markers in the presence of occlusions
(Elsevier, 2019-08-11)
Human motion capture by optical sensors produces snapshots of the motion of a cloud of points that need to be labeled in order to carry out ensuing motion analysis for medical or other purposes. We generate the labeling ...
WebLabel: OpenLABEL-compliant multi-sensor labelling
(Springer Nature, 2024)
Annotated datasets have become crucial for training Machine Learning (ML) models
for developing Autonomous Vehicles (AVs) and their functions. Generating these data-
sets usually involves a complex coordination of ...
COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge
(MDPI, 2024-02-28)
COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary ...
Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning
(MDPI, 2024-03-01)
Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class ...
Learning positioning policies for mobile manipulation operations with deep reinforcement learning
(Springer Nature, 2023)
This work focuses on the operation of picking an object on a table with a mobile manipulator. We use deep reinforcement
learning (DRL) to learn a positioning policy for the robot’s base by considering the reachability ...
Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis
(BMC, 2024)
Background
Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with ...
Above-ground biomass estimation from LiDAR data using random forest algorithms
(Elsevier, 2022-02)
Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric ...
Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain
(MDPI, 2019-09-19)
Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available ...
EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management
(MDPI, 2024-04-24)
Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater ...