Results 131 to 140 of about 9,795 (189)

Sentiment Analysis of COVID-19 on Weibo text using optimized Bi-LSTM model

, 2023
Yang S.
europepmc   +1 more source

Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM. [PDF]

Sci Rep
Huang L, Lv W, Huang Q, Zhang H, Jin S, Chen T, Shen B.   +6 more
europepmc   +1 more source

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities. [PDF]

Bioengineering (Basel)
Ayub H, Khan MA, Shehryar Ali Naqvi S, Faseeh M, Kim J, Mehmood A, Kim YJ.   +6 more
europepmc   +1 more source

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA-CNN-Bi-LSTM deep learning model. [PDF]

Heliyon
Khan MK, Houran MA, Kauhaniemi K, Zafar MH, Mansoor M, Rashid S.   +5 more
europepmc   +1 more source

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection. [PDF]

Sci Rep
Yin X, Fang W, Liu Z, Liu D.
europepmc   +1 more source

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis. [PDF]

Sci Rep
Ozsahin DU, Ameen ZS, Hassan AS, Mubarak AS.   +3 more
europepmc   +1 more source

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period. [PDF]

BMC Infect Dis
Zhu H, Chen S, Qin W, Aynur J, Chen Y, Wang X, Chen K, Xie Z, Li L, Liu Y, Chen G, Ou J, Zheng K.   +12 more
europepmc   +1 more source

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals. [PDF]

Cogn Neurodyn
Said A, Göker H.
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A Multilingual Framework of CNN and Bi-LSTM for Emotion Classification

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020
Speech emotion recognition is a crucial task for developing human-computer based interfaces. However, many significant studies have focused on extracting low-level features for classification. In this paper, we propose a language-independent, deep learning-based framework for speech emotion classification.
Ashima Yadav, Dinesh Kumar Vishwakarma
openaire   +1 more source