MBLSTM is a contextual interaction refined method for time series prediction. [PDF]
Sci RepQiu W, Zhu F, Hao T, Wang M, Huang R.
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Monitoring and deformation of deep excavation engineering based on DFOS technology and hybrid deep learning. [PDF]
Sci RepPeng Y, Zhao J, Fan Y, Fan C.
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High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks. [PDF]
Sci RepReliga P +6 more
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Channel equalization in ultraviolet communication based on LSTM-DNN hybrid model. [PDF]
Sci RepZhang L.
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Design optimization of university ideological and political education system based on deep learning. [PDF]
Sci RepAi S, Ding H.
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Temporal user interest modeling for online advertising using Bi-LSTM network improved by an updated version of Parrot Optimizer. [PDF]
Sci RepYang Y, Zhang L, Liu J.
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction. [PDF]
Sci RepLee S +7 more
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Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation. [PDF]
Sci RepKrishnasamy L +6 more
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Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region. [PDF]
Sci RepElabd E, Hamouda HM, Ali MAM, Fouad Y.
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Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths. [PDF]
Sci RepFathi MM +5 more
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