Results 61 to 70 of about 1,980,897 (317)

Simplicial 2-Complex Convolutional Neural Nets

, 2020
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground,
Bunch, Eric, You, Qian, Fung, Glenn, Singh, Vikas   +3 more
openaire   +2 more sources

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

IET Image Processing
Anemia (hemoglobin (Hb) 
Linquan Xu, Yuwen Chen, Songmei Lu, Kunhua Zhong, Yujie Li, Bin Yi   +5 more
doaj   +1 more source

Convolutional Neural Networks–Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps

Cells, 2020
Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections.
Aneta Manda-Handzlik, Krzysztof Fiok, Adrianna Cieloch, Edyta Heropolitanska-Pliszka, Urszula Demkow   +4 more
doaj   +1 more source

BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks [PDF]

IEEE Access, 2019
Binarization can greatly compress and accelerate deep convolutional neural networks (CNNs) for real-time industrial applications. However, existing binarized CNNs (BCNNs) rely on scaling factor (SF) and batch normalization (BatchNorm) that still involve resource-consuming floating-point multiplication operations.
Lijun Wu 0002, Peiqing Jiang, Zhicong Chen, Xu Lin, Yunfeng Lai, Peijie Lin, Shuying Cheng   +6 more
openaire   +2 more sources

Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks [PDF]

Communications in Computational Physics, 2020
Deep networks, especially convolutional neural networks (CNNs), have been successfully applied in various areas of machine learning as well as to challenging problems in other scientific and engineering fields. This paper introduces Butterfly-net, a low-complexity CNN with structured and sparse cross-channel connections, together with a Butterfly ...
Li, Y, Cheng, X, Lu, J
openaire   +2 more sources

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

IET Computer Vision
The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network ...
Nafiseh Zarei, Payman Moallem, Mohammadreza Shams   +2 more
doaj   +1 more source

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

IET Generation, Transmission & Distribution, 2023
The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid.
Prabaakaran Kandasamy, Chandrasekaran Kumar, Muthuramalingam Lakshmanan, Selvaraj Jaisiva, Albert Alexander Stonier, Geno Peter, Vivekananda Ganji   +6 more
doaj   +1 more source

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source