Relevant bibliographies by topics / Deep residual bidirectional long short-term memory fusion

Contents

  1. Journal articles
  2. Book chapters
  3. Conference papers

Academic literature on the topic 'Deep residual bidirectional long short-term memory fusion'

Author: Grafiati
Published: 2 June 2025
Last updated: 22 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Deep residual bidirectional long short-term memory fusion.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Deep residual bidirectional long short-term memory fusion"

1

Munsarif, Muhammad, and Ku Ruhana Ku-Mahamud. "Deep residual bidirectional long short-term memory fusion: achieving superior accuracy in facial emotion recognition." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2143–55. https://doi.org/10.11591/eei.v14i3.9090.

Full text
Abstract:
Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn-Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.
APA, Harvard, Vancouver, ISO, and other styles
2

Fan, Jiaxing, Lin Dong, Gang Sun, and Zhize Zhou. "A Deep Learning Approach for Mental Fatigue State Assessment." Sensors 25, no. 2 (2025): 555. https://doi.org/10.3390/s25020555.

Full text
Abstract:
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts.
APA, Harvard, Vancouver, ISO, and other styles
3

Kang, Junhyuk, Jieun Shin, Jaewon Shin, Daeho Lee, and Ahyoung Choi. "Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network." Sensors 22, no. 1 (2021): 174. http://dx.doi.org/10.3390/s22010174.

Full text
Abstract:
Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.
APA, Harvard, Vancouver, ISO, and other styles
4

Song, Xudong, Qi Zhang, Rui Sun, et al. "A Hybrid Deep Learning Prediction Method of Remaining Useful Life for Rolling Bearings Using Multiscale Stacking Deep Residual Shrinkage Network." International Journal of Intelligent Systems 2023 (November 17, 2023): 1–15. http://dx.doi.org/10.1155/2023/6665534.

Full text
Abstract:
The vibration signal is easily interfered by noise due to the influence of environment and other factors, which can lead to the poor adaptability, low accuracy of remaining useful life (RUL) prediction, and other problems. To solve this problem, this paper proposes a novel RUL prediction method, which is based on multiscale stacking deep residual shrinkage network (MSDRSN). MSDRSN combines the ability of stacking in improving prediction accuracy and the advantages of deep residual shrinkage network (DRSN) in denoising. First, cumulative sum (CUSUM) from statistics is used to divide the full life cycle of the rolling bearings and discover the points of failure. Second, stacking is used for feature learning on the raw data, multiple convolutional kernels of different scales are selected as base-learners, and fully connected neural networks are selected as meta-learners for feature fusion and learning. Then, DRSN is used to do prediction, and the acquired results are fitted with Savitzky–Golay (SG) smoothing. Finally, the effectiveness of the proposed method is proved by the IEEE PHM 2012 data challenge dataset. Compared with the multiscale convolutional neural network with fully connected layer (MSCNN-FC) and the bidirectional long short-term memory (BiLSTM) for RUL prediction under the noise. Using the proposed method, the mean absolute error (MSE) of the best result is 0.002 and the mean square error (MSE) is 0.014; meanwhile, the coefficient of determination (R2) of the best prediction result can reach 97.6%. It is also compared with other machine learning methods, and all the results prove the accuracy and effectiveness of the proposed method for RUL prediction applications.
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Lei, Dongrun Chang, and Zongshuai Li. "MSCNN-BLSTM based Prediction of the Remaining Useful Life of Aeroengine." Journal of Physics: Conference Series 2361, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2361/1/012019.

Full text
Abstract:
Accurate prediction of the aeroengine remaining useful life (RUL) is essential to improve engine availability and reliability. Aiming at the reliable prediction of residual life of aeroengine system, an engine residual life prediction model based on the fusion of multiscale fusion two-dimensional convolutional neural network and bidirectional long and short term memory (MSCNN-BLSTM) is proposed. Based on the fusion of two-dimensional convolutional neural network and bidirectional long and short time memory (BLSTM) network, the engine medium and advanced features extracted by the convolutional neural network are integrated to make residual life prediction. Finally, C-MAPSS dataset provided by NASA was used for validation. It is shown that the proposed multiscale hybrid model, compared with other model predictions, reduces the performance index score and root mean square error by 32.2% and 14.7% respectively. It can be seen that the data-driven model can effectively extract the information from the degradation data, which improves the prediction performance of aeroengine remaining life.
APA, Harvard, Vancouver, ISO, and other styles
6

Zhao, Yu, Rennong Yang, Guillaume Chevalier, Ximeng Xu, and Zhenxing Zhang. "Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors." Mathematical Problems in Engineering 2018 (December 30, 2018): 1–13. http://dx.doi.org/10.1155/2018/7316954.

Full text
Abstract:
Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.
APA, Harvard, Vancouver, ISO, and other styles
7

Asadullaev, R. G., and M. A. Sitnikova. "INTELLIGENT MODEL FOR CLASSIFYING HEMODYNAMIC PATTERNS OF BRAIN ACTIVATION TO IDENTIFY NEUROCOGNITIVE MECHANISMS OF SPATIAL-NUMERICAL ASSOCIATIONS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 235 (January 2024): 38–45. http://dx.doi.org/10.14489/vkit.2024.01.pp.038-045.

Full text
Abstract:
The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.
APA, Harvard, Vancouver, ISO, and other styles
8

Li, Xin, Hong Huang, Guotao Yuan, Zhaolian Wang, and Rui Du. "An Intrusion Detection Method based on Fusion Neural Network." Frontiers in Computing and Intelligent Systems 4, no. 2 (2023): 124–30. http://dx.doi.org/10.54097/fcis.v4i2.10369.

Full text
Abstract:
Aiming at the problems of class imbalance, insufficient feature learning, weak generalization ability, and representation capability in existing intrusion detection models, we propose a multi-scale feature fusion Intrusion Detection Model (MSFF). This model combines multi-scale one-dimensional convolution and bidirectional long short-term memory (LSTM) networks, and incorporates residual connections with identity mappings to address the problem of network degradation. The multi-scale convolution captures feature representations at different levels, thereby improving the expressive power of the model. The WGAN-GP algorithm is employed to augment the minority samples and balance the dataset. By performing convolution operations and extracting local window features and global features using bidirectional LSTM units, the model effectively captures temporal information and long-term dependencies. Experimental results demonstrate significant performance improvement compared to a single model. The MSFF model achieves an accuracy of 99.50% and 94.73% in binary classification experiments on the NSL-KDD and UNSW-NB15 datasets, respectively, and an accuracy of 99.50% and 83.78% in multi-class classification experiments.
APA, Harvard, Vancouver, ISO, and other styles
9

Xie, Jialing, Weifeng Shi, and Yuqi Shi. "Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM." Machines 10, no. 9 (2022): 736. http://dx.doi.org/10.3390/machines10090736.

Full text
Abstract:
To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep learning algorithm is used to establish a fault diagnosis model to distinguish the types of electric drive faults. First, the powerful fault feature extraction ability of the residual network is used to deeply mine the fault features in the signals. Then, perform time-series learning through a bidirectional long short-term memory network, and further excavate the transient time-series features in the fault features so as to achieve the accurate classification of drive inverter faults. The effectiveness of the method is verified using noise-free fault data, and the robustness of the method is verified using data with varying degrees of noise. The results show that compared with conventional deep learning algorithms, Res-BiLSTM has the fastest and most stable training process, the diagnostic performance is improved, and the accuracy can be maintained over 95% under 25–19 dB. It has certain robustness and can be applied to marine electric propulsion systems drive inverter fault diagnosis, and its results can provide data support for the implementation of self-healing control strategies.
APA, Harvard, Vancouver, ISO, and other styles
10

Ma, Yahong, Zhentao Huang, Yuyao Yang, et al. "MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion." Biomimetics 10, no. 3 (2025): 178. https://doi.org/10.3390/biomimetics10030178.

Full text
Abstract:
Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic feature extraction and classification. Nonetheless, many deep learning approaches still necessitate manual preprocessing, which hampers accuracy and convenience. This paper introduces a novel deep learning technique that integrates multi-scale convolution and bidirectional long short-term memory networks with an attention mechanism for automatic EEG feature extraction and classification. By using raw EEG data, the method applies multi-scale convolutional neural networks and bidirectional long short-term memory networks to extract and merge features, selects key features via an attention mechanism, and classifies emotional EEG signals through a fully connected layer. The proposed model was evaluated on the SEED dataset for emotion classification. Experimental results demonstrate that this method effectively classifies EEG-based emotions, achieving classification accuracies of 99.44% for the three-class task and 99.85% for the four-class task in single validation, with average 10-fold-cross-validation accuracies of 99.49% and 99.70%, respectively. These findings suggest that the MSBiLSTM-Attention model is a powerful approach for emotion recognition.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Deep residual bidirectional long short-term memory fusion"

1

Lyu Xiangru, Lyu Xueqiang, Sun Fanshu, and Dong Zhian. "Patent Domain Terminology Extraction Based on Multi-Feature Fusion and BiLSTM-CRF Model." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-927-0-495.

Full text
Abstract:
In order to improve the accuracy and recall rate of term extraction results in the Chinese patent domain, approaching from the perspective of deep learning, with part-of-speech and dependency relationships as features, a patent domain term extration model (Bi-LSTM-CRF) was proposed by combining Conditional Random Fields (CRF) and bi-directional long short-term memory (Bi-LSTM) based on a multi-feature fusion. Based on the two explicit characteristics of part of speech and dependency, the double-layer bidirectional LSTM neural network was used to mine the temporal and semantic information in the data, which overcame the disadvantages of the traditional methods, such as weak generality and inability to capture the implicit information in the context as well as addressing the dependency relationship among the output tags through the CRF layer. Experimental results show that this deep learning method is effective in terms of domain term extraction.
APA, Harvard, Vancouver, ISO, and other styles
2

Sun, Jiadong, Mengnan Wang, Defen Ren, and Deji Chen. "Research and Application of Text-Based Sentiment Analytics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241391.

Full text
Abstract:
Text-based sentiment analytics is an important research direction in the field of natural language processing, and it is widely applied in identifying emotional tendencies. The proliferation of e-commerce platforms has underscored the criticality of emotional tendencies and user experiences delineated in product reviews for gauging product quality and user satisfaction. This study presents a developed text-based sentiment analysis system tailored to product reviews on e-commerce platforms. Three distinct text-based sentiment classification models based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) were constructed and implemented. Subsequently, sentiment classification and comparative experiments were conducted utilizing real-world product review data. The experimental outcomes underscore that the BERT model outperforms others in terms of the Area Under the Curve (AUC) metric, yielding superior results in the domain of text sentiment analysis. This paper extensively deliberates on the fusion of deep learning methodologies into sentiment analytics, encapsulating the entire spectrum of data collection, preprocessing, and model analysis, with an overarching goal of providing a reference point for research endeavors in analogous realms.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Deep residual bidirectional long short-term memory fusion"

1

Dudi, Bhanuprakash, V. Rajesh, and G. Prasanna Kumar. "Plant Leaf Classification through Deep Feature Fusion with Bidirectional Long Short-Term Memory." In 2022 International Conference on Innovations in Science and Technology for Sustainable Development (ICISTSD). IEEE, 2022. http://dx.doi.org/10.1109/icistsd55159.2022.10010620.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kourkounakis, Tedd, Amirhossein Hajavi, and Ali Etemad. "Detecting Multiple Speech Disfluencies Using a Deep Residual Network with Bidirectional Long Short-Term Memory." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053893.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Deep residual bidirectional long short-term memory fusion' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography