Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection

Zhou, Qian; Zhou, Changjun; Wang, Xiao

doi:10.1371/journal.pone.0262501

Cited by 23 publications

(20 citation statements)

References 37 publications

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“…A more comprehensive and complex set of text features is obtained for each node by concatenating the results of the forward and backward layers. This process enables a context-based comprehensive judgment [45][46][47].…”

Section: B Analysis Of the Bi-gru Modelmentioning

confidence: 99%

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

Li,

Zhao

et al. 2024

IEEE Access

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This work aims to investigate the use of the Recurrent Neural Network (RNN) in automated English grading. In order to achieve this, this work first constructs an automated English grading system based on the Internet of Things (IoT). Next, based on the variant of RNN called Gated Recurrent Unit (GRU), it introduces a self-attention mechanism into bidirectional GRU to form the Bidirectional-GRU_self-attention (Bi-GRU_Att) model. Simultaneously, an attention pooling (AP) mechanism is introduced into bidirectional GRU to form the Bidirectional-GRU_AP (Bi-GRU_AP) model. Comparative experiments are conducted using Chinese and English corpora to compare the performance of these two models. The results indicate that the Bi-GRU_AP model performs well on both Chinese and English datasets. On the Chinese dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 1.3%, 9.9%, and 19%, respectively. On the English dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 2.2%, 9.8%, and 19.2%, respectively. This suggests that introducing the AP module enables the model to better capture sentence information, thereby enhancing model performance. Additionally, after 20 iterations, the Bi-GRU_AP model exhibits good convergence and stability. The findings provide new insights for the development of automated English subjective grading systems based on IoT and deep learning.

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Section: B Analysis Of the Bi-gru Modelmentioning

confidence: 99%

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

Li,

Zhao

et al. 2024

IEEE Access

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“…Finally, the BiGRU model is exploited for the HGR procedure. An RNN is effectively utilized for handling data series from distinct regions [20]. In RNN, assume that input series x ¼ x 1 ; .…”

Section: Module Ii: Gesture Recognitionmentioning

confidence: 99%

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Alrowais¹,

Marzouk²,

Al‐Wesabi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people's communication. The presented BODL-HGRSLC technique integrates the concepts of computer vision (CV) and DL models. In the presented BODL-HGRSLC technique, a deep convolutional neural network-based residual network (ResNet) model is applied for feature extraction. Besides, the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process. At last, a bidirectional gated recurrent unit (BiGRU) model is exploited for the HGR procedure. A wide range of experiments was conducted to demonstrate the enhanced performance of the presented BODL-HGRSLC model. The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

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“…The concept of a GNN first appeared in 2005, and since then, there has been a boom in deep learning, and GNN research has become quite active. In particular, since 2016, major methods have emerged, such as that of a Gated GNN and Graph Convolution Network (GCN), which is an architecture that can learn embedded representations of nodes both with and without supervision based on the features of the nodes [84][85][86][87] (Figure 3). When obtaining an embedding representation for node A on a graph with a two-layer GCN, the GCN computes the embedding representation of the node by repeated aggregation of neighboring nodes.…”

Section: Otherwisementioning

confidence: 99%

“…Since the recursive process in Step 1 is repeated, the amount of calculation is large. A gated-graph sequential neural network (GGS-NN) replaces the recursion process in Step 1 with a Gated Recurrent Unit (GRU), which is the gating mechanism in a recurrent neural network (RNN) and which has better performance on certain smaller datasets and removes the constraints of contraction mapping [86][87][88][89][90][91][92]. The GRU concept can be expressed using the following formula:…”

Section: Current Qsarmentioning

confidence: 99%

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

Matsuzaka

Uesawa

2023

Processes

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In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR applications for in silico toxicity prediction have been studied for a long time. However, in recent years, the remarkable predictive performance of deep learning has attracted attention for practical applications. In this review, we summarize the application of deep learning to QSAR for constructing prediction models, including a discussion of parameter optimization for deep learning.

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Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection

Cited by 23 publications

References 37 publications

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

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