Results 31 to 40 of about 3,882,996 (203)

Feature Extraction From Images Using Integrated Photonic Convolutional Kernel

IEEE Photonics Journal, 2022
Optical neural networks are expected to solve the problems of computational efficiency and energy consumption in neural networks. Herein, we experimentally implemented a 2 × 2 photonic convolutional kernel (PCK) using four on-chip micro-ring ...
Yulong Huang, Beiju Huang, Chuantong Cheng, Huan Zhang, Hengjie Zhang, Run Chen, Hongda Chen   +6 more
doaj   +1 more source

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

NeuroImage: Clinical, 2019
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals.
Ali Emami, Naoto Kunii, Takeshi Matsuo, Takashi Shinozaki, Kensuke Kawai, Hirokazu Takahashi   +5 more
doaj   +1 more source

Color Filter Array Demosaicking Using Densely Connected Residual Network

IEEE Access, 2019
Deep convolutional neural networks have been used extensively in recent image processing research, exhibiting drastically improved performance. In this study, we apply convolutional neural networks to color filter array demosaicking, which plays an ...
Bumjun Park, Jechang Jeong
doaj   +1 more source

FocusedDropout for Convolutional Neural Network

CoRR, 2021
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units.
Tianshu Xie, Minghui Liu, Jiali Deng, Xuan Cheng, Xiaomin Wang, Ming Liu 0002   +5 more
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Interpretable Convolutional Neural Networks [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part.
Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu   +2 more
openaire   +3 more sources

Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

IEEE Access, 2020
To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.
Chenkun Zhang, Cheng Wang
doaj   +1 more source

Data-Driven Bearing Fault Diagnosis for Induction Motor

Journal of Electrical and Computer Engineering, 2023
Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs.
Aqib Raqeeb, Fahim Shah, Zaheer Alam, Subhashree Choudhury, Bilal Khan, R. Palanisamy   +5 more
doaj   +1 more source

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

IEEE Access, 2021
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao, Yaodong Zhao, Jie Gu, Yaping Zhang, Yi Zhong   +4 more
doaj   +1 more source

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Journal of Education, Health and Sport, 2021
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka, Tomasz Włodarczyk, Ryszard Szczerba, Anna Chomiak, Oskar Komisarek   +4 more
doaj   +1 more source

Convolutional Neural Networks In Convolution

CoRR, 2018
Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
openaire   +3 more sources