Results 31 to 40 of about 80,238 (283)

Audio Captioning using Gated Recurrent Units

, 2020
Audio captioning is a recently proposed task for automatically generating a textual description of a given audio clip. In this study, a novel deep network architecture with audio embeddings is presented to predict audio captions. Within the aim of extracting audio features in addition to log Mel energies, VGGish audio embedding model is used to explore
Eren, Ayşegül Özkaya, Sert, Mustafa
openaire   +3 more sources

Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022
Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate ...
Yanli Zhang, Shuxin Yao, Rendi Yang, Xiaojia Liu, Wenlong Qiu, Luben Han, Weidong Zhou, Wei Shang   +7 more
doaj   +1 more source

Anomaly Detection in Cloud Components

, 2020
Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage.
Islam, Mohammad Saiful, Miranskyy, Andriy   +1 more
core   +1 more source

Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging [PDF]

, 2017
Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been successfully adopted for predicting the audio tags in the domestic audio scene.
Huang, Qiang, Kong, Qiuqiang, Plumbley, Mark D., Wang, Wenwu, Xu, Yong   +4 more
core   +2 more sources

Learning long-range spatial dependencies with horizontal gated-recurrent units

, 2018
Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a ...
Kim, Junkyung, Linsley, Drew, Serre, Thomas, Veerabadran, Vijay   +3 more
core   +1 more source

Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model [PDF]

, 2018
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative ...
Holland, Erlend Skullestad, Lindholm, Geir, Ratnaweera, Harsha, Zhang, Duo   +3 more
core   +2 more sources

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks

, 2017
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete.
Bianchi, Filippo Maria, Jenssen, Robert, Kampffmeyer, Michael, Mikalsen, Karl Øyvind, Soguero-Ruiz, Cristina, Strauman, Andreas Storvik   +5 more
core   +1 more source

An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction

Journal of Advanced Transportation, 2022
Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of
Shanchun Zhao, Xu Li
doaj   +1 more source

Landslide Displacement Prediction With Gated Recurrent Unit and Spatial-Temporal Correlation

Frontiers in Earth Science, 2022
Landslides are geohazards of major concern that can cause casualties and property damage. Short-term landslide displacement prediction is one of the most critical and challenging tasks in landslide deformation analysis, and is beneficial for future ...
Wenli Ma, Wenli Ma, Wenli Ma, Jianhui Dong, Jianhui Dong, Zhanxi Wei, Zhanxi Wei, Zhanxi Wei, Liang Peng, Qihong Wu, Qihong Wu, Chunxia Chen, Yuanzao Wu, Yuanzao Wu, Yuanzao Wu, Feihong Xie, Feihong Xie   +16 more
doaj   +1 more source

Learning Simpler Language Models with the Differential State Framework

, 2017
Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train.
Mikolov, Tomas, Ororbia II, Alexander G., Reitter, David   +2 more
core   +1 more source