Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier
Sign Up to like & getrecommendations! Published in 2021 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-021-01991-6
Abstract: Corona Virus Disease-2019 (COVID-19) is a global pandemic which is spreading briskly across the globe. The gold standard for the diagnosis of COVID-19 is viral nucleic acid detection with real-time polymerase chain reaction (RT-PCR). However,… read more here.
Keywords: detection covid; cnns; diagnosis; covid ... See more keywords
Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics
Sign Up to like & getrecommendations! Published in 2017 at "Acta Materialia"
DOI: 10.1016/j.actamat.2017.09.004
Abstract: Abstract Convolutional neural networks (CNNs) have recently exhibited state-of-the-art performance with respect to image recognition tasks. In the present study, we adopt CNNs to link experimental microstructures with corresponding ionic conductivities. The results reveal that… read more here.
Keywords: cnns; neural networks; convolutional neural; microstructure recognition ... See more keywords
A kernel-based weight decorrelation for regularizing CNNs
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.11.065
Abstract: Abstract Recent years has witnessed the success of convolutional neural networks (CNNs) in many machine learning and pattern recognition applications, especially in image recognition. However, due to the increasing model complexity, the parameter redundancy problem… read more here.
Keywords: cnns; kernel based; decorrelation regularizing; based weight ... See more keywords
On the Dynamics and Feasibility of Transferred Inference for Diagnosis of Invasive Ductal Carcinoma: A Perspective
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3159700
Abstract: It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we… read more here.
Keywords: vanilla; performance; cnns; diagnosis ... See more keywords
MemUnison: A Racetrack-ReRAM-Combined Pipeline Architecture for Energy-Efficient in-Memory CNNs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2022.3148858
Abstract: Though ReRAM has been greatly successful in reducing energy consumption of various neural networks, it still suffers write amplification in energy, which impedes ReRAM to provide efficient storage for the ubiquitous streaming data in CNNs,… read more here.
Keywords: reram; energy; cnns; racetrack reram ... See more keywords
Photonic Reconfigurable Accelerators for Efficient Inference of CNNs With Mixed-Sized Tensors
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3197538
Abstract: Photonic microring resonator (MRR)-based hardware accelerators have been shown to provide disruptive speedup and energy-efficiency improvements for processing deep convolutional neural networks (CNNs). However, previous MRR-based CNN accelerators fail to provide efficient adaptability for CNNs… read more here.
Keywords: cnns mixed; hardware; mrr based; cnns ... See more keywords
Data Augmentation Using Random Image Cropping and Patching for Deep CNNs
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2019.2935128
Abstract: Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN… read more here.
Keywords: cnns; random image; image; data augmentation ... See more keywords
A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2019.2955447
Abstract: Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques… read more here.
Keywords: based recognition; eeg based; cnns; coincidence filtering ... See more keywords
SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2018.2886758
Abstract: The availability of large-scale annotated data and the uneven separability of different data categories have become two major impediments of deep learning for image classification. In this paper, we present a semi-supervised hierarchical convolutional neural… read more here.
Keywords: hcnn; cnns; semi supervised; image classification ... See more keywords
Design of Fully Spectral CNNs for Efficient FPGA-Based Acceleration.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3224779
Abstract: Computing convolutional layers in the frequency domain using fast Fourier transformation (FFT) has been demonstrated to be effective in reducing the computational complexity of convolutional neural networks (CNNs). Nevertheless, the main challenge of this approach… read more here.
Keywords: efficient; hardware; cnns; fully spectral ... See more keywords
Fabrication of FeTCPP@CNNS for Efficient Photocatalytic Performance of p-Nitrophenol under Visible Light
Sign Up to like & getrecommendations! Published in 2023 at "Catalysts"
DOI: 10.3390/catal13040732
Abstract: A photocatalyst of iron–porphyrin tetra-carboxylate (FeTCPP)-sensitized g-C3N4 nanosheet composites (FeTCPP@CNNS) based on g-C3N4 nanosheet (CNNS) and FeTCPP have been fabricated by in situ hydrothermal self-assembly. FeTCPP is uniformly introduced to the surface of CNNS. Only… read more here.
Keywords: fetcpp cnns; photocatalytic performance; fabrication fetcpp; cnns ... See more keywords