Results 31 to 40 of about 51,131 (269)
Remote Sensing, 2019 Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features ...
Kaiqiang Zhu +4 more
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Monaural speech separation with deep learning using phase modelling and capsule networks [PDF]
, 2019 The removal of background noise from speech audio is a problem with high practical relevance. A variety of deep learning approaches have been applied to it in recent years, most of which operate on a magnitude spectrogram representation of a noisy ...
dubey +9 more
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Artificial Intelligence in Colon Capsule Endoscopy—A Systematic Review
Diagnostics, 2022 Background and aims: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability.
Sarah Moen +3 more
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A Unified Framework of Deep Neural Networks by Capsules [PDF]
, 2019 With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue. We first formalize neural networks mathematically with their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs.
Yujian Li, Chuanhui Shan
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Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data
Applied Sciences, 2019 This study focuses on the retrieval of chemical oxygen demand (COD) in the Baiyangdian area in North China, using a modified capsule network. Herein, the capsule model was modified to analyze the regression relationship between 1-D hyperspectral data and
Chubo Deng, Lifu Zhang, Yi Cen
doaj +1 more source
Sensors, 2021 Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities.
Bin Han +3 more
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ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
Applied Sciences, 2023 The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent.
Lantian Yao +8 more
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IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lower-limb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates
Yanggang Feng +4 more
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Polyphonic Sound Event Detection by Using Capsule Neural Networks [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2019 Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs).
Fabio Vesperini +3 more
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Research on Lane Occupancy Rate Forecasting Based on the Capsule Network
IEEE Access, 2020 This paper proposes a hybrid lane occupancy rate prediction model called 2LayersCapsNet, which combines the improved capsule network and convolutional neural networks (CNNs). The model uses CNNs to mine the spatial-temporal correlation characteristics of
Ran Tian +3 more
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