Results 31 to 40 of about 30,189 (295)
Machine translation of English speech: Comparison of multiple algorithms
Journal of Intelligent Systems, 2022 In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition.
Wu Yijun, Qin Yonghong
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IEEE Access, 2018 To solve matrix-type linear time-varying equation more efficiently, a novel exponentialtype varying gain recurrent neural network (EVG-RNN) is proposed in this paper. Being distinguished from the traditional fixed-parameter gain recurrent neural network (
Zhijun Zhang +3 more
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TS-RNN: Text Steganalysis Based on Recurrent Neural Networks [PDF]
IEEE Signal Processing Letters, 2019 IEEE Signal Processing ...
Zhongliang Yang +4 more
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Behavioural pattern identification and prediction in intelligent environments [PDF]
, 2013 In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed.
Mahmoud, S, Lotfi, A, Langensiepen, C
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A Dynamic Branch Predictor Based on Parallel Structure of SRNN
IEEE Access, 2020 Branch predictor is a key component of processor, which can improve the efficiency of instruction execution. The branch predictor based on machine learning algorithm can achieve high branch prediction accuracy, but it has the disadvantages of long ...
Lei Zhang +4 more
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Sensors, 2021 In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of ...
Tuan-Khai Nguyen +2 more
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Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network
SN Applied Sciences, 2021 This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural network with rectified linear unit (BGCP-RNN-ReLU model), which can effectively predict the changes in the upstream and ...
Tao Deng +5 more
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Fully Convolutional Sequence Recognition Network for Water Meter Number Reading
IEEE Access, 2019 One of the most widely used frameworks for image-based sequence recognition is the convolutional recurrent neural network, which uses a convolutional neural network (CNN) for feature extraction and a recurrent neural network (RNN) for sequence modeling ...
Fan Yang +4 more
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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of ...
Shuai Li 0005 +4 more
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, 2021 Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data.
E. A., P. Akhir +5 more
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