Gotowe bibliografie tematyczne / Deep residual bidirectional long short-term memory fusion

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Gotowa bibliografia na temat „Deep residual bidirectional long short-term memory fusion”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Deep residual bidirectional long short-term memory fusion"

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.

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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

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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.

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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 co

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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.

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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 dat

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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.

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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 li

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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.

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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

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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.

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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 v

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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.

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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 ca

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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.

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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

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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.

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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 bidire

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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.

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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

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Części książek na temat "Deep residual bidirectional long short-term memory fusion"

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.

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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 th

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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.

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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 S

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Streszczenia konferencji na temat "Deep residual bidirectional long short-term memory fusion"

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.

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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.

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