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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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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.

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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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Tong, Yizhi, Ping Wu, Jiajun He, Xujie Zhang, and Xinlong Zhao. "Bearing fault diagnosis by combining a deep residual shrinkage network and bidirectional LSTM." Measurement Science and Technology 33, no. 3 (2021): 034001. http://dx.doi.org/10.1088/1361-6501/ac37eb.

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Abstract Bearings are indispensable and key components in rotating machinery. To ensure the safe and reliable operation of rotating machinery, bearing fault diagnosis plays a crucial role. To explore the spatial and temporal information in vibration signals, a novel bearing fault diagnosis method is proposed by combining a deep residual shrinkage network (DRSN) and bidirectional long short-term memory (Bi-LSTM) network in this study. Firstly, a DRSN is employed to extract the spatial features from noise-related vibration signals. Then, a Bi-LSTM network is adopted to further address the long-t

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He, Zhiqian, Qingzheng Liu, Anxu Chen, et al. "Metal Temperature Prediction Model based on Attention-Enhanced CNN-BiLSTM for Pulverized Coal Boiler Reheat Wall." Journal of Physics: Conference Series 3001, no. 1 (2025): 012018. https://doi.org/10.1088/1742-6596/3001/1/012018.

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Abstract The overheating of heat transfer wall is a primary contributor to boiler tube failures in coal-fired boilers. A fusion model that integrates sparse self-attention (SSA), convolutional neural networks (CNN), and bidirectional long short-term memory networks (BiLSTM) is proposed to predict the wall temperature of the final reheater, based on their dynamic characteristics. Firstly, the original candidate variables are subjected to filtering and dimensionality reduction through the KPCA algorithm, resulting in the selection of the top 26 principal component variables as the final input fo

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Zhang, Ke, and Yaming Guo. "Attention-Based Residual Dilated Network for Traffic Accident Prediction." Mathematics 11, no. 9 (2023): 2011. http://dx.doi.org/10.3390/math11092011.

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Traffic accidents directly influence public safety and economic development; thus, the prevention of traffic accidents is of great importance in urban transportation. The accurate prediction of traffic accidents can assist traffic departments to better control and prevent accidents. Thus, this paper proposes a deep learning method named attention-based residual dilated network (ARDN), to extract essential information from multi-source datasets and enhance accident prediction accuracy. The method utilizes bidirectional long short-term memory to model sequential information and incorporates an a

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Cruz-Victoria, Juan Crescenciano, Alma Rosa Netzahuatl-Muñoz, and Eliseo Cristiani-Urbina. "Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark." Sustainability 16, no. 7 (2024): 2874. http://dx.doi.org/10.3390/su16072874.

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Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle si

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Cui, Ziti, Wei Wang, Wei Jiang, Jun Guo, and Yang Liu. "High-precision identification and prediction of low-voltage load characteristics in smart grids based on hybrid deep learning framework." International Journal of Low-Carbon Technologies 19 (2024): 2656–66. http://dx.doi.org/10.1093/ijlct/ctae221.

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Abstract This paper proposes a hybrid deep learning framework (HDLF) that combines improved convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformer models. First, feature selection and dimensionality reduction are performed using XGBoost and principal component analysis, respectively. Secondly, CNN is enhanced by multiscale convolution, residual connection, and attention mechanism. Then, the bidirectional LSTM is combined with temporal convolutional network to improve the LSTM. Then, an improved dynamic focusing mechanism of transformer is introduced. The

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Malki, Zohair, Elsayed Atlam, Guesh Dagnew, Ahmad Reda Alzighaibi, Elmarhomy Ghada, and Ibrahim Gad. "Bidirectional Residual LSTM-based Human Activity Recognition." Computer and Information Science 13, no. 3 (2020): 40. http://dx.doi.org/10.5539/cis.v13n3p40.

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The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in th

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Nguyen, Huu Khoa Minh, Quoc-Dung Phan, Yuan-Kang Wu, and Quoc-Thang Phan. "Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)." Energies 16, no. 9 (2023): 3792. http://dx.doi.org/10.3390/en16093792.

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Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the

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Jamatia, Anupam, Amitava Das, and Björn Gambäck. "Deep Learning-Based Language Identification in English-Hindi-Bengali Code-Mixed Social Media Corpora." Journal of Intelligent Systems 28, no. 3 (2019): 399–408. http://dx.doi.org/10.1515/jisys-2017-0440.

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Abstract This article addresses language identification at the word level in Indian social media corpora taken from Facebook, Twitter and WhatsApp posts that exhibit code-mixing between English-Hindi, English-Bengali, as well as a blend of both language pairs. Code-mixing is a fusion of multiple languages previously mainly associated with spoken language, but which social media users also deploy when communicating in ways that tend to be rather casual. The coarse nature of code-mixed social media text makes language identification challenging. Here, the performance of deep learning on this tas

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Yen, Chih-Ta, Sheng-Nan Chang, and Cheng-Hong Liao. "Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions." Measurement and Control 54, no. 3-4 (2021): 439–45. http://dx.doi.org/10.1177/00202940211001904.

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This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset c

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Wu, Yiqi, Mei Liu, Zhaoyuan Peng, Meiqi Liu, Miao Wang, and Yingqi Peng. "Recognising Cattle Behaviour with Deep Residual Bidirectional LSTM Model Using a Wearable Movement Monitoring Collar." Agriculture 12, no. 8 (2022): 1237. http://dx.doi.org/10.3390/agriculture12081237.

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Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and

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Cai, Linqin, Yaxin Hu, Jiangong Dong, and Sitong Zhou. "Audio-Textual Emotion Recognition Based on Improved Neural Networks." Mathematical Problems in Engineering 2019 (December 31, 2019): 1–9. http://dx.doi.org/10.1155/2019/2593036.

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With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was

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Yang, Chengjin, Yanzhong Zhai, and Zehua Liu. "Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting." PLOS One 20, no. 6 (2025): e0323714. https://doi.org/10.1371/journal.pone.0323714.

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The fluctuations in corn prices not only increase uncertainty in the market but also affect farmers’ planting decisions and income stability, while also impeding crucial investments in sustainable agricultural practices. Collectively, these factors jeopardize the long-term sustainability of the corn sector. In order to address the challenges posed by maize price volatility to the sustainability of the industry, this study proposes a multi-module wavelet transform-based fusion forecasting model: the TLDCF-TSD-BiTCEN-BiLSTM-FECAM (TLDCF-TSD-BBF) model, which is capable of accurately predicting s

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Charles, Pranob Kumar, Habibulla Khan, and K. S. Rao. "Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks." Intelligent Communication and Computing for Next Generation Wireless Communication Networks 11, NGWCN (2023): 20–24. http://dx.doi.org/10.37391/ijeer.11ngwcn04.

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Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution.

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Choi, Kanghae, Hokyoung Ryu, and Jieun Kim. "Deep Residual Networks for User Authentication via Hand-Object Manipulations." Sensors 21, no. 9 (2021): 2981. http://dx.doi.org/10.3390/s21092981.

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With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple d

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Guo, Longfei, Yunwei Pu, and Wenxiang Zhao. "CNN-BiLSTM Daily Precipitation Prediction Based on Attention Mechanism." Atmosphere 16, no. 3 (2025): 333. https://doi.org/10.3390/atmos16030333.

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Accurate daily precipitation forecasting is crucial for the rational utilization of water resources and the prediction of flood disasters. To address the low reliability and low prediction accuracy of existing daily precipitation prediction models based on deep learning which arise from the nonlinear and non-stationary characteristics of surface precipitation data, this paper first employs the principal component analysis (PCA) method to extract the principal components of the original data. Given that the convolutional neural network (CNN) is adept at capturing spatial dependencies, bidirecti

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Khan, Gulista, Sanjeev Kumar Mandal, and Sunil Sharma. "Deployment of the deep learning fusion method to emotional semantic evaluation of natural language." Multidisciplinary Science Journal 5 (August 10, 2023): 2023ss0114. http://dx.doi.org/10.31893/multiscience.2023ss0114.

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The emotional semantic evaluation of natural language plays a crucial role in sentiment analysis. Deep learning methods have shown great potential in capturing the complex relationships between words and emotions. This paper proposes a deep learning fusion method for deploying emotional semantic evaluation. The technique combines multiple deep learning architectures to capture local and global contextual information, including Bidirectional Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Self-Attention mechanisms. Pretrained GloVe word embedding’s utilized to enhance w

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Geetha Paranjothi, Arunachalam A.S. "Lung Cancer Detection: Advancing CT Image Analysis Through Hybrid Bidirectional Long Short-Term Memory and Recurrent Neural Network." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 29–46. https://doi.org/10.52783/jisem.v10i18s.2880.

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Globally, lung cancer (LC) is the leading cause of death from cancer. Medical image analysis based on deep learning (DL) has strong potential for detecting and diagnosing lung cancer by identifying early symptoms with image aid from positron emission tomography (PET) and computed tomography (CT). The majority of DL models created for LC detection are very resource-intensive, requiring a great deal of computational capacity; hence, they pose a challenge to deployment on a standard clinical system and are therefore significantly less accessible in resource-constrained settings. This additional c

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Abid, Fazeel, Muhammad Alam, Faten S. Alamri, and Imran Siddique. "Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization." AIMS Mathematics 8, no. 9 (2023): 19993–20017. http://dx.doi.org/10.3934/math.20231019.

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<abstract> <p>Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consump

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Fang, Yifan, Shanshan Jiang, Shengxuan Fang, Zhenxi Gong, Min Xia, and Xiaodong Zhang. "Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network." Buildings 12, no. 7 (2022): 1048. http://dx.doi.org/10.3390/buildings12071048.

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Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the compu

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Setiadi, De Rosal Ignatius Moses, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Syahroni Wahyu Iriananda, and Arnold Adimabua Ojugo. "Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 23–38. http://dx.doi.org/10.62411/faith.2024-11.

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This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing

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Ge, Ming, and Yongbo Yuan. "Evaluation model design of project construction safety level based on bidirectional recurrent neural network (BiRNN) and bidirectional long short-term memory (BiLSTM)." PeerJ Computer Science 10 (October 18, 2024): e2351. http://dx.doi.org/10.7717/peerj-cs.2351.

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Integrating deep learning methods for multi-element regression analysis poses a challenge in constructing safety evaluations for building construction. To address this challenge, this paper evaluates the integration of construction safety by quantitatively analyzing practitioners’ information and on-site construction conditions. The analytic hierarchy process (AHP) method quantifies construction safety capabilities, considering four key aspects: operators’ primary conditions, organizational personnel’s working conditions, on-site management conditions, and analysis of unsafe behaviors. A compr

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Wu, Wei. "Predicting danceability and song ratings using deep learning and auditory features." PeerJ Computer Science 11 (July 23, 2025): e3009. https://doi.org/10.7717/peerj-cs.3009.

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Predicting a song’s danceability and overall rating poses a significant challenge due to the complex interplay between musical characteristics and listener preferences. In this study, we propose a deep learning framework that jointly addresses the tasks of danceability estimation and popularity prediction. Our model integrates a Bidirectional Long Short-Term Memory (BiLSTM) network to capture sequential and contextual patterns from categorical inputs, alongside a Residual Network (ResNet) that extracts hierarchical representations from numerical auditory features. These complementary feature s

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Adil, Mohd, Jei-Zheng Wu, Ripon K. Chakrabortty, Ahmad Alahmadi, Mohd Faizan Ansari, and Michael J. Ryan. "Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival." Processes 9, no. 10 (2021): 1759. http://dx.doi.org/10.3390/pr9101759.

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Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist arrivals. However, search intensities indices (SII) indicators have recently been included as a forecasting model, which significantly e

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Zhang, Hao, Qiang Zhang, Siyu Shao, Tianlin Niu, Xinyu Yang, and Haibin Ding. "Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning." Shock and Vibration 2020 (September 14, 2020): 1–16. http://dx.doi.org/10.1155/2020/8888627.

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Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Fi

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Xiao, Xiaqiu, Buyun Sheng, Gaocai Fu, and Yingkang Lu. "Construction of Knowledge Graph for Air Compressor Fault Diagnosis Based on a Feature-Fusion RoBERTa-BiLSTM-CRF Model." Actuators 13, no. 9 (2024): 339. http://dx.doi.org/10.3390/act13090339.

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Diagnosing complex air compressor systems with traditional data-driven deep learning models often results in isolated fault diagnosis, ignoring correlations between concurrent faults. This paper introduces a knowledge graph construction approach for the air compressor fault diagnosis field, using after-sales business data as the source. We propose a model based on Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), specifically tailored for constructing a knowledge graph for air compressor fault diagnosis. By integrating Whole Word Masking (WWM) technology, Bi

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Dhaka, Priyanka, and Ruchi Sehrawat. "Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT." Fusion: Practice and Applications 14, no. 1 (2024): 40–55. http://dx.doi.org/10.54216/fpa.140104.

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Internet-of-Things (IoT)-based heart disease prediction is a complex task and processing the real collected data directly for remote patient monitoring suffers from the limitations due to the irrelevant data features, affecting the prediction accuracy and raising the security concerns. Hence, the efficient Adaptive ensembled deep Convolution neural network –Bidirectional Long Short Term Memory (Adaptive ensembled deep CNN-BiLSTM ) classifier model is proposed via the fusion of interactive hunt-based CNN and Whale on Marine optimization (WoM)-based deep BiLSTM. The Adaptive optimization develop

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Parivendan, Sibi Chakravathy, Kashfia Sailunaz, and Suresh Neethirajan. "Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review." Animals 15, no. 13 (2025): 1835. https://doi.org/10.3390/ani15131835.

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This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and at

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Liu, Yong, Jiaqi Liu, Han Wang, Mingshun Yang, Xinqin Gao, and Shujuan Li. "A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory." Machines 12, no. 5 (2024): 342. http://dx.doi.org/10.3390/machines12050342.

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In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network base

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Zhao, Jianjun, Wenying Yan, and Yang Yang. "DeepTP: A Deep Learning Model for Thermophilic Protein Prediction." International Journal of Molecular Sciences 24, no. 3 (2023): 2217. http://dx.doi.org/10.3390/ijms24032217.

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Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence information. To solve this problem, a deep learning model based on self-attention and multiple-channel feature fusion was proposed to predict thermophilic proteins, called DeepTP. First, a large new dataset consisting of 20,842 proteins was constructed. Second, a convolutional neural network and bidirectional long short-term memory network were us

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Saddozai, Furqan Khan, Sahar K. Badri, Daniyal Alghazzawi, Asad Khattak, and Muhammad Zubair Asghar. "Multimodal hate speech detection: a novel deep learning framework for multilingual text and images." PeerJ Computer Science 11 (April 16, 2025): e2801. https://doi.org/10.7717/peerj-cs.2801.

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The rapid proliferation of social media platforms has facilitated the expression of opinions but also enabled the spread of hate speech. Detecting multimodal hate speech in low-resource multilingual contexts poses significant challenges. This study presents a deep learning framework that integrates bidirectional long short-term memory (BiLSTM) and EfficientNetB1 to classify hate speech in Urdu-English tweets, leveraging both text and image modalities. We introduce multimodal multilingual hate speech (MMHS11K), a manually annotated dataset comprising 11,000 multimodal tweets. Using an early fus

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Huang, Songtao, Jun Shen, Qingquan Lv, Qingguo Zhou, and Binbin Yong. "A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting." Future Internet 15, no. 1 (2022): 22. http://dx.doi.org/10.3390/fi15010022.

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Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a

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Zheng, Tianwei, Mei Wang, Yuan Guo, and Zheng Wang. "The Bidirectional Information Fusion Using an Improved LSTM Model." Mobile Information Systems 2021 (April 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/5595898.

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The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep f

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Chen, Kangen, Xiuhong Lin, Tao Xia, and Rushan Bai. "Research on Park Perception and Understanding Methods Based on Multimodal Text–Image Data and Bidirectional Attention Mechanism." Buildings 15, no. 9 (2025): 1552. https://doi.org/10.3390/buildings15091552.

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Parks are an important component of urban ecosystems, yet traditional research often relies on single-modal data, such as text or images alone, making it difficult to comprehensively and accurately capture the complex emotional experiences of visitors and their relationships with the environment. This study proposes a park perception and understanding model based on multimodal text–image data and a bidirectional attention mechanism. By integrating text and image data, the model incorporates a bidirectional encoder representations from transformers (BERT)-based text feature extraction module, a

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Zhang, Chen, Qingxu Li, and Xue Cheng. "Text Sentiment Classification Based on Feature Fusion." Revue d'Intelligence Artificielle 34, no. 4 (2020): 515–20. http://dx.doi.org/10.18280/ria.340418.

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The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was em

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Zhao, Yonghong, Xiumei Fan, and Jisong Liu. "Robust DOA Estimation via a Deep Learning Framework with Joint Spatial–Temporal Information Fusion." Sensors 25, no. 10 (2025): 3142. https://doi.org/10.3390/s25103142.

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In this paper, we propose a robust deep learning (DL)-based method for Direction-of-Arrival (DOA) estimation. Specifically, we develop a novel CRDCNN-LSTM network architecture, which integrates a Cross-Residual Depthwise Convolutional Neural Network (CRDCNN) with a Long Short-Term Memory (LSTM) module for effective capture of both spatial and temporal features. The CRDCNN employs multi-level cross-residual connections and depthwise separable convolutions to enhance feature diversity while mitigating issues such as gradient vanishing and overfitting. Furthermore, a customized FD loss function,

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Yang, Chao, Xingli Gan, Antao Peng, and Xiaoyu Yuan. "ResNet Based on Multi-Feature Attention Mechanism for Sound Classification in Noisy Environments." Sustainability 15, no. 14 (2023): 10762. http://dx.doi.org/10.3390/su151410762.

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Environmental noise affects people’s lives and poses challenges for urban sound classification. Traditional algorithms such as Mel frequency cepstral coefficients (MFCCs) struggle due to audio signal complexity. This study applied an attention mechanism to a deep residual network (ResNet) deep learning network to overcome the structural impact of urban noise on audio signals and improve classification accuracy. We propose a three-feature fusion ResNet + attention method (Net50_SE) to maximize information representation in environmental sound signals. This method uses residual structured convol

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Yang, Chuanli, Huawang Qin, and Siyuan Hu. "A 3D-ResNet Combined with BRNN: Application in the Auxiliary Diagnosis of ADHD." Advances in Computer and Engineering Technology Research 1, no. 3 (2024): 452. http://dx.doi.org/10.61935/acetr.3.1.2024.p452.

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Attention Deficit/Hyperactivity Disorder (ADHD) is a common mental disorder that exhibits a high incidence rate in children and adolescents, and it is also observed in adults. Currently, there is a lack of objective diagnostic methods for ADHD. Therefore, a three-dimensional residual network (3D-ResNet) deep learning method based on feature extraction from rs-fMRI images for assisting in the diagnosis of ADHD based on resting-state functional magnetic resonance imaging (rs-fMRI) and deep learning models was proposed in this paper. Taking into consideration the temporal characteristics of rs-fM

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Lou, Yongjun, Meng Gao, Shuo Zhang, et al. "Chinese Named Entity Recognition for Dairy Cow Diseases by Fusion of Multi-Semantic Features Using Self-Attention-Based Deep Learning." Animals 15, no. 6 (2025): 822. https://doi.org/10.3390/ani15060822.

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Named entity recognition (NER) is the basic task of constructing a high-quality knowledge graph, which can provide reliable knowledge in the auxiliary diagnosis of dairy cow disease, thus alleviating problems of missed diagnosis and misdiagnosis due to the lack of professional veterinarians in China. Targeting the characteristics of the Chinese dairy cow diseases corpus, we propose an ensemble Chinese NER model incorporating character-level, pinyin-level, glyph-level, and lexical-level features of Chinese characters. These multi-level features were concatenated and fed into the bidirectional l

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Li, Zhongyu, Xu Liu, Yu Lin, Xiaohua Xu, and Xiangfa Wang. "Energy Efficiency Prediction of Energy Storage Virtual Synchronous Machine Based on Long Short-Term Memory Network." Journal of Physics: Conference Series 2665, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2665/1/012014.

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Abstract The integration of renewable energy sources in the power grid has led to an increased demand for energy storage systems to manage the intermittency and variability of these sources. Among various energy storage technologies, Energy Storage Virtual Synchronous Machines (ESVSMs) have emerged as a promising solution for enhancing grid stability and energy efficiency. However, optimizing the performance of ESVSMs requires accurate energy efficiency predictions. This paper proposes a energy efficiency prediction network (EEPNet) to achieve energy efficiency prediction of ESVSMs. Firstly, c

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Zou, Yingyong, Tao Liu, and Xingkui Zhang. "A Three-Channel Feature Fusion Approach Using Symmetric ResNet-BiLSTM Model for Bearing Fault Diagnosis." Symmetry 17, no. 3 (2025): 427. https://doi.org/10.3390/sym17030427.

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For mechanical equipment to operate normally, rolling bearings—which are crucial parts of rotating machinery—need to have their faults diagnosed. This work introduces a bearing defect diagnosis technique that incorporates three-channel feature fusion and is based on enhanced Residual Networks and Bidirectional long- and short-term memory networks (ResNet-BiLSTM) model. The technique can effectively establish spatial-temporal relationships and better capture complex features in data by combining the powerful spatial feature extraction capability of ResNet and the bidirectional temporal modeling

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