Copula‐Based Data Augmentation and Machine Learning for Predicting Tensile Strength of 3D‐Printed PLA Under Anisotropic Conditions
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Applied Polymer Science"
DOI: 10.1002/app.57533
Abstract: In this study, 48 polylactic acid (PLA) samples were produced via 3D printing, incorporating four infill geometries (gyroid, lattice, honeycomb, and linear), four infill rates (15%–60%), and three printing directions (x, y, z). Tensile testing… read more here.
Keywords: tensile strength; data augmentation; infill; machine learning ... See more keywords
FC‐ACGAN‐based data augmentation for terahertz time‐domain spectral concealed hazardous materials identification
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23013
Abstract: Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time‐domain spectroscopy technology based… read more here.
Keywords: spectroscopy; data augmentation; hazardous materials; time domain ... See more keywords
Domain‐specific data augmentation for segmenting MR images of fatty infiltrated human thighs with neural networks
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Magnetic Resonance Imaging"
DOI: 10.1002/jmri.26544
Abstract: Fat‐fraction has been established as a relevant marker for the assessment and diagnosis of neuromuscular diseases. For computing this metric, segmentation of muscle tissue in MR images is a first crucial step. read more here.
Keywords: domain specific; segmenting images; data augmentation; images fatty ... See more keywords
The effect of data augmentation and network simplification on the image‐based detection of broccoli heads with Mask R‐CNN
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Field Robotics"
DOI: 10.1002/rob.21975
Abstract: In current practice, broccoli heads are selectively harvested by hand. The goal of our work is to develop a robot that can selectively harvest broccoli heads, thereby reducing labor costs. An essential element of such… read more here.
Keywords: broccoli; data augmentation; network simplification; broccoli heads ... See more keywords
Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation
Sign Up to like & getrecommendations! Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep03(2021)273
Abstract: We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and… read more here.
Keywords: inference; observational data; augmentation; data augmentation ... See more keywords
Ensemble classification from deep predictions with test data augmentation
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-019-03976-7
Abstract: Data augmentation has become a standard step to improve the predictive power and robustness of convolutional neural networks by means of the synthetic generation of new samples depicting different deformations. This step has been traditionally… read more here.
Keywords: augmentation; data augmentation; deep predictions; classification deep ... See more keywords
Few-shot imbalanced classification based on data augmentation
Sign Up to like & getrecommendations! Published in 2021 at "Multimedia Systems"
DOI: 10.1007/s00530-021-00827-0
Abstract: Few-shot imbalanced classification tasks are commonly faced in the real-world applications due to the unbalanced data distribution and few samples of rare classes. As known, the traditional machine learning algorithms perform poorly on the imbalanced… read more here.
Keywords: shot; shot imbalanced; imbalanced classification; data augmentation ... See more keywords
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Sign Up to like & getrecommendations! Published in 2024 at "Knowledge and Information Systems"
DOI: 10.1007/s10115-025-02349-x
Abstract: Data augmentation is arguably the most important regularization technique commonly used to improve generalization performance of machine learning models. It primarily involves the application of appropriate data transformation operations to create new data samples with… read more here.
Keywords: machine learning; augmentation; performance; data augmentation ... See more keywords
A comprehensive survey of recent trends in deep learning for digital images augmentation
Sign Up to like & getrecommendations! Published in 2021 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-021-10066-4
Abstract: Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without… read more here.
Keywords: image data; image; survey; deep learning ... See more keywords
Data augmentation for handwritten digit recognition using generative adversarial networks
Sign Up to like & getrecommendations! Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-08883-w
Abstract: Supervised learning techniques require labeled examples that can be time consuming to obtain. In particular, deep learning approaches, where all the feature extraction stages are learned within the artificial neural network, require a large number… read more here.
Keywords: generative adversarial; adversarial networks; recognition; labeled examples ... See more keywords
Learning double weights via data augmentation for robust sparse and collaborative representation-based classification
Sign Up to like & getrecommendations! Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-08918-2
Abstract: Image classification is a hot technique applied in many multimedia systems, where both l 1 and l 2 regularizations have shown potential for robust sparse representation-based image classification. However, previous studies showed that l 1… read more here.
Keywords: robust sparse; classification; double weights; data augmentation ... See more keywords