... multiple aspects. Design/methodology/approach This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect...

... neural network models mentioned in this paper. Design/methodology/approach This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore...

..., 2021). Several studies have proposed deep neural network models, such as recurrent neural networks (RNN) and long-short-term memory neural networks (LSTM) to improve the accuracy of financial time series prediction (Fischer and Krauss, 2018 ; Kim and Kang, 2019 ; Li et al., 2018 ; Selvin...

...) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes. Findings Among these, the highest accuracy...