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Journal articles on the topic 'Convolutional LSTM Network'

Author: Grafiati

Published: 5 September 2021

Last updated: 29 July 2025

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1

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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Wan, Renzhuo, Shuping Mei, Jun Wang, Min Liu, and Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting." Electronics 8, no. 8 (2019): 876. http://dx.doi.org/10.3390/electronics8080876.

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Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-N

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Zhang, Jiaan, Chenyu Liu, and Leijiao Ge. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN." Energies 15, no. 7 (2022): 2633. http://dx.doi.org/10.3390/en15072633.

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The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the f

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Yang, Wuyi, Wenlei Chang, Zhongchang Song, Fuqiang Niu, Xianyan Wang, and Yu Zhang. "Denoising odontocete echolocation clicks using a hybrid model with convolutional neural network and long short-term memory network." Journal of the Acoustical Society of America 154, no. 2 (2023): 938–47. http://dx.doi.org/10.1121/10.0020560.

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Ocean noise negatively influences the recording of odontocete echolocation clicks. In this study, a hybrid model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network—called a hybrid CNN-LSTM model—was proposed to denoise echolocation clicks. To learn the model parameters, the echolocation clicks were partially corrupted by adding ocean noise, and the model was trained to recover the original echolocation clicks. It can be difficult to collect large numbers of echolocation clicks free of ambient sea noise for training networks. Data augmentation and transfer

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Mountzouris, Konstantinos, Isidoros Perikos, and Ioannis Hatzilygeroudis. "Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism." Electronics 12, no. 20 (2023): 4376. http://dx.doi.org/10.3390/electronics12204376.

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Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact

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Swapna.C. "Conv2D-LSTM-AE-GAN: Convolutional 2D LSTM Auto Encoder Generative Adversarial Network." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 792–806. https://doi.org/10.52783/jisem.v10i14s.2396.

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Surveillance video refers to video footage captured by cameras for the purpose of monitoring and recording activities in specific environments. These videos are commonly used for security purposes in places such as airports, shopping malls, streets, industrial facilities, hospitals, and other public or private spaces. The primary objective of surveillance video systems is to maintain safety, detect suspicious activities, and collect evidence for investigation. Anomaly detection in Surveillance video is an important and evolving field with applications across various industries. It involves ana

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Guan, Wenhui, and Binbin Li. "Research on diagnosis method of motor vibration signal based on MSCNN-LSTM." Journal of Physics: Conference Series 2816, no. 1 (2024): 012035. http://dx.doi.org/10.1088/1742-6596/2816/1/012035.

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Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. I

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Zhang, Feizhou, Ke Shang, Lei Yan, Haijing Nan, and Zicong Miao. "Prediction of Parking Space Availability Using Improved MAT-LSTM Network." ISPRS International Journal of Geo-Information 13, no. 5 (2024): 151. http://dx.doi.org/10.3390/ijgi13050151.

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The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, a comparison was made between a temporal convolutional network (TCN) based on CNNs and a long short-term memory (LSTM) network based on RNNs to determine an appropriate baseline for predicting parking space availability. Subsequently, a multi-head attention (MAT) mechani

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Waczyńska, Joanna, Edoardo Martelli, Sofia Vallecorsa, Edward Karavakis, and Tony Cass. "Convolutional LSTM models to estimate network traffic." EPJ Web of Conferences 251 (2021): 02050. http://dx.doi.org/10.1051/epjconf/202125102050.

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Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration—details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration—is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of la

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Wang, Kejun, Xiaoxia Qi, and Hongda Liu. "Photovoltaic power forecasting based LSTM-Convolutional Network." Energy 189 (December 2019): 116225. http://dx.doi.org/10.1016/j.energy.2019.116225.

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Huang, Yanglai, Jing Huang, Xiaoyue Wu, and Yu Jia. "Dynamic Sign Language Recognition Based on CBAM with Autoencoder Time Series Neural Network." Mobile Information Systems 2022 (April 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3247781.

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The CNN-LSTM network has a low generalization ability, and the backward relevance of actions is not strong. In this work, a convolutional self-encoding timing network with a fusion of attention mechanism, namely, convolutional block attention module (CBAM), is proposed. The model first designs a convolutional self-encoding network for pretraining to obtain feature vectors of smaller dimensions. Second, it uses the BN network to speed up the training process and enhance the network generalization ability. Then, we use the encoder part of the pretrained convolutional autoencoder, embed the atten

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BALOGLU, ULAS BARAN, and ÖZAL YILDIRIM. "CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION." Journal of Mechanics in Medicine and Biology 19, no. 01 (2019): 1940005. http://dx.doi.org/10.1142/s0219519419400050.

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Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep net

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Sheng, Wanxing, Keyan Liu, Dongli Jia, Shuo Chen, and Rongheng Lin. "Short-Term Load Forecasting Algorithm Based on LST-TCN in Power Distribution Network." Energies 15, no. 15 (2022): 5584. http://dx.doi.org/10.3390/en15155584.

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In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are

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Li, Yaxiang, and Yuanqing Wang. "Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network." Mobile Information Systems 2022 (June 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/2174208.

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In order to explore a mobile virtual reality railway traffic congestion prediction algorithm based on convolutional neural network, an expanded causal convolution neural network (DCFCN) was proposed, which introduced the expanded convolution to increase the size of the receptive field and obtain the long-term memory of the sequence. At the same time, causal convolution is introduced to solve the problem of information leakage. DCFCN is made up of 6 convolutional layers, each layer achieves causal convolution through padding, and the expansion coefficient increases exponentially layer by layer.

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Syed, Zafi Sherhan, Muhammad Zaigham Abbas Shah Syed, Muhammad Shehram Shah Syed, and Aunsa Shah. "Sequential Modeling for the Recognition of Activities in Logistics." Sukkur IBA Journal of Emerging Technologies 4, no. 1 (2021): 12–21. http://dx.doi.org/10.30537/sjet.v4i1.848.

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Activity recognition is an important task in cyber physical system research and has been the focus of researchers worldwide. This paper presents a method for activity recognition in logistic operations using data from accelerometer and gyroscope sensors. A Long Short Term Memory (LSTM) recurrent neural network, bidirectional LSTM and a Convolutional LSTM (ConvLSTM) are used to classify between six activities being performed in the logistics operations being carried out. Comparing the performance of the LSTMs to the Conv-LSTM network, the designed Bi-LSTM RNN outperforms the other networks cons

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Guo, Shuai, Yao Liu, and Yang Su. "Network Traffic Anomaly Detection Method Based on CAE and LSTM." Journal of Physics: Conference Series 2025, no. 1 (2021): 012025. http://dx.doi.org/10.1088/1742-6596/2025/1/012025.

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Abstract This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the fea

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Ahmed, Nasser, and Al-Khazraji Huthaifa. "A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 721–30. https://doi.org/10.11591/ijece.v12i1.pp721-730.

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Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and

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K, Manimekalai, and A. Kavitha Dr. "Deep Learning Methods in Classification of Myocardial Infarction by employing ECG Signals." Indian Journal of Science and Technology 13, no. 28 (2020): 2823–32. https://doi.org/10.17485/IJST/v13i28.445.

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Abstract <strong>Background/Objectives:</strong> To automatically classify and detect the Myocardial Infarction using ECG signals.<strong> Methods/Statistical analysis:</strong> Deep Learning algorithms Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) and Enhanced Deep Neural Network(EDN) were implemented. The proposed model EDN, comprises the techniques CNN and LSTM. Vector operations like matrix multiplication and gradient decent were applied to large matrices of data that are executed in parallel with GPU support. Because of parallelism EDN faster the execution

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Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

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Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namel

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Huang, Yongjie, Xiaofeng Huang, and Qiakai Cai. "Music Generation Based on Convolution-LSTM." Computer and Information Science 11, no. 3 (2018): 50. http://dx.doi.org/10.5539/cis.v11n3p50.

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In this paper, we propose a model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for music generation. We first convert MIDI-format music file into a musical score matrix, and then establish convolution layers to extract feature of the musical score matrix. Finally, the output of the convolution layers is split in the direction of the time axis and input into the LSTM, so as to achieve the purpose of music generation. The result of the model was verified by comparison of accuracy, time-domain analysis, frequency-domain analysis and human-auditory evaluation.

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Nasser, Ahmed, and Huthaifa AL-Khazraji. "A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 721. http://dx.doi.org/10.11591/ijece.v12i1.pp721-730.

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<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accu

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22

Liu, Cheng, Bingchuan Wang, and Yiqun Zou. "Combining Multi-Scale Convolutional Neural Network with Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-ion Batteries." Journal of Physics: Conference Series 2456, no. 1 (2023): 012017. http://dx.doi.org/10.1088/1742-6596/2456/1/012017.

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Abstract To develop safe and intelligent battery management systems for electric vehicles, it is necessary to accurately estimate the state of charge (SOC) of lithium-ion batteries. At present, deep learning methods have been broadly applied in the field of SOC estimation of lithium-ion batteries. However, existing deep SOC estimators are difficult to capture global trends due to being too sensitive to the changes of continuous time data points. In addition, a single non-linear neural network tends to ignore the linear features of the data, which makes the robustness of the estimation not good

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Dakdareh, Sara Ghasemi, and Karim Abbasian. "Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using Convolutional Neural Networks." Journal of Alzheimer's Disease Reports 8, no. 1 (2024): 317–28. http://dx.doi.org/10.3233/adr-230118.

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Background: Alzheimer’s disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020. Early diagnosis is crucial for managing these diseases, but their complexity poses a challenge. Convolutional neural networks have shown promise in accurate diagnosis. Objective: The main objective of this research is to diagnose Alzheimer’s disease and mild cognitive impairment in healthy individuals using convolutional neural networks. Methods: This study utilized three different convolutional neural network models, two of which were pre-t

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Aldabagh, Hind, Xianrong Zheng, Mohammad Najand, and Ravi Mukkamala. "Forecasting Crude Oil Price Using Multiple Factors." Journal of Risk and Financial Management 17, no. 9 (2024): 415. http://dx.doi.org/10.3390/jrfm17090415.

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In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs),

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Nguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi, and Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features." Sensors 18, no. 11 (2018): 4057. http://dx.doi.org/10.3390/s18114057.

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Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and

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Ye, Linwei, Zhi Liu, and Yang Wang. "Dual Convolutional LSTM Network for Referring Image Segmentation." IEEE Transactions on Multimedia 22, no. 12 (2020): 3224–35. http://dx.doi.org/10.1109/tmm.2020.2971171.

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Wang, Huidong, Yurun Ma, Aihua Zhang, Dongmei Lin, Yusheng Qi, and Jiaqi Li. "Deep Convolutional Generative Adversarial Network with LSTM for ECG Denoising." Computational and Mathematical Methods in Medicine 2023 (February 10, 2023): 1–17. http://dx.doi.org/10.1155/2023/6737102.

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The electrocardiogram (ECG), as an essential basis for the diagnosis of cardiovascular diseases, is usually disturbed by various noise. To obtain accurate human physiological information from ECG, the denoising and reconstruction of ECG are critical. In this paper, we proposed an ECG denoising method referred to as LSTM-DCGAN which is based on an improved generative adversarial network (GAN). The overall network structure is composed of multiple layers of convolutional networks. Furthermore, the convolutional features can be connected to their time series order dependence by adding LSTM layers

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Mukesh Yadav, Dhirendra S Mishra. "Evaluating Deep Learning Algorithms for Log-Based Anomaly Detection: Insights from Public and Private Datasets." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 954–72. https://doi.org/10.52783/jisem.v10i34s.5885.

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Anomaly detection in network logs is crucial for maintaining the security and efficiency of modern IT systems. This paper evaluates several deep learning algorithms, including Autoencoders, Variational Autoencoders (VAE), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN), for log-based anomaly detection using public datasets such as UNSW, KDD99, and Kyoto, as well as a private dataset consisting of 300,000 log entries. Each model is benchmarked using key performance metrics such as accuracy, p

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Ng, Jia Hui, Ying Han Pang, Sarmela Raja Sekaran, Shih Yin Ooi, and Lillian Yee Kiaw Wang. "Temporal Convolutional Recurrent Neural Network for Elderly Activity Recognition." Journal of Engineering Technology and Applied Physics 6, no. 2 (2024): 84–91. http://dx.doi.org/10.33093/jetap.2024.6.2.12.

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Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. T

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Abed Maeedi, Ahmed, Dalal Abdulmohsin Hammood, and Shatha Mezher Hasan. "Breast Cancer Detection Using Deep Learning." Iraqi Journal for Computers and Informatics 50, no. 2 (2024): 122–31. https://doi.org/10.25195/ijci.v50i2.500.

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This research aims to develop an image classification model by integrating long short-term memory (LSTM) with a convolutional neural network (CNN). LSTM, which is a type of neural network, can retain and retrieve long-term dependencies and improves the feature extraction capabilities of CNN when used in a multi-layer setting. The proposed approach outperforms typical CNN classifiers in image classification. The model’s high accuracy is due to the data passing through two stages and multiple layers: first the LSTM layer, followed by the CNN layer for accurate classification. Convolutional and r

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Han, Kaixu, Wenhao Wang, and Jun Guo. "Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model." Machines 12, no. 12 (2024): 927. https://doi.org/10.3390/machines12120927.

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In view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) models. In this study, a convolutional neural network (CNN) model is used to process two-dimensional image data in both time and frequency domains, and a convolutional core attention mechanism is introduced to extract spatial features, such as peaks, cliffs, and w

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Lu, Wenxing, Haidong Rui, Changyong Liang, Li Jiang, Shuping Zhao, and Keqing Li. "A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots." Entropy 22, no. 3 (2020): 261. http://dx.doi.org/10.3390/e22030261.

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Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological d

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Ma, Junwei, Ming Zhao, Wendong Shen, Zepeng Yang, Xiaokun Yu, and Shunfa Lu. "Photovoltaic Power Prediction Based on NRS-PCC Feature Selection and Multi-Scale CNN-LSTM Network." International Journal of Web Services Research 21, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijwsr.353899.

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To improve the quality of photovoltaic (PV) data and power prediction accuracy, a PV power prediction method based on neighborhood rough set and Pearson correlation coefficient (NRS-PCC) feature selection and multi-scale convolutional neural networks and long short-term memory (CNN-LSTM) network is proposed. We first calculate the correlation between different PV features based on PCC and select strongly correlated features to cross-multiply to get the fusion features to enrich the data source. Then, dimensionality reduction of the fusion features by NRS. Finally, correlation analysis based on

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Yolchuyeva, Sevinj, Géza Németh, and Bálint Gyires-Tóth. "Grapheme-to-Phoneme Conversion with Convolutional Neural Networks." Applied Sciences 9, no. 6 (2019): 1143. http://dx.doi.org/10.3390/app9061143.

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Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without r

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Zhang, Shujing. "Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM." Computational Intelligence and Neuroscience 2021 (July 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/2578422.

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Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers’ attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn th

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Liu, Xia. "E-Commerce Precision Marketing Model Based on Convolutional Neural Network." Scientific Programming 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/4000171.

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With the rapid development of network and informatization of the consumer market in my country, the application and maturity of technologies such as the Internet, terminal equipment, logistics, and payment and the continuous improvement of people’s consumption concepts, online shopping has gradually become the mainstream purchase method for Chinese consumers, and e-commerce has gradually become one of the important driving forces to promote the sustained and vigorous development of China's economy. Under the traditional marketing model, companies do not fully understand the needs of users. The

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Chen, Suting, Song Zhang, Huantong Geng, Yaodeng Chen, Chuang Zhang, and Jinzhong Min. "Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM." Atmosphere 11, no. 6 (2020): 569. http://dx.doi.org/10.3390/atmos11060569.

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In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input data and gets 3D tensor data with spatiotemporal features, extracts local short-term spatiotemporal features of radar echoes through 3D convolution networks, then utilizes constructed bi-directional convolutional LSTM to learn global long-term spatiotemporal feature depe

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Nilakanta, Kshetrimayum, Robindro Singh Khumukcham, and Hoque Nazrul. "A Multi-step Short-term Load Forecasting using Hybrid DNN and GAF." Indian Journal of Science and Technology 17, no. 11 (2024): 1016–27. https://doi.org/10.17485/IJST/v17i11.3246.

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Abstract <strong>Background:</strong> Short-term Load Forecasting (STLF) is vital for grid stability, ensuring a steady power supply and resource efficiency. However, the literature review underscores imperfections in current methods, emphasizing the necessity for additional research in this domain. Objectives: This study introduces an effective framework for multi-step STLF, enhancing predictive accuracy by integrating state-of-the-art DNN models like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) in a hybrid architecture, leveraging their complementary strengths.&n

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Khan, Sufiyan Ali. "IMAGE CAPTION GENERATOR USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31987.

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Image Captioning is a task where each image must be understood properly and are able generate suitable caption with proper grammatical structure.Here it is a hybrid system which uses multilayer CNN (Convolutional Neural Network) for generating keywords which narrates given input images and Long Short Term Memory(LSTM) for precisely constructing the significant captions utilizing the obtained words .Convolution Neural Network (CNN) proven to be so effective that there is a way to get to any kind of estimating problem that includes image data as input. LSTM was developed to avoid the poor predic

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Jinyan, Jiang. "Deep learning based on the application of voice emotion." Applied and Computational Engineering 13, no. 1 (2023): 76–80. http://dx.doi.org/10.54254/2755-2721/13/20230712.

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A language is a valuable tool for human development and progress, and it is also an important medium for human beings to transmit information and express emotions. Language signals are ubiquitous, and it is an indispensable part of human life. This article will take the analysis of language as the starting point, combined with the relevant content of computer deep learning, and summarize various methods of language emotion recognition based on a convolutional neural network. In recent years, with the gradual intelligentization of computers, more in-depth discoveries and research have been made

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Alamri, Nawaf Mohammad H., Michael Packianather, and Samuel Bigot. "Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm." Applied Sciences 13, no. 4 (2023): 2536. http://dx.doi.org/10.3390/app13042536.

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Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in bo

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Wan, Yingliang, Hong Tao, and Li Ma. "Forecasting Zhejiang Province's GDP Using a CNN-LSTM Model." Frontiers in Business, Economics and Management 13, no. 3 (2024): 233–35. http://dx.doi.org/10.54097/bmq2dy63.

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Zhejiang province has experienced notable economic growth in recent years. Despite this, achieving sustainable high-quality economic development presents complex challenges and uncertainties. This study employs advanced neural network methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and an integrated CNN-LSTM model, to predict Zhejiang's economic trajectory. Our empirical analysis demonstrates the proficiency of neural networks in delivering reasonably precise economic forecasts, despite inherent prediction residuals. A comparative assessmen

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Yu, Xinlian, Ailun Lan, and Haijun Mao. "Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTM." Systems 11, no. 10 (2023): 485. http://dx.doi.org/10.3390/systems11100485.

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Demand prediction for on-demand food delivery (ODFD) is of great importance to the operation and transportation resource utilization of ODFD platforms. This paper addresses short-term ODFD demand prediction using an end-to-end deep learning architecture. The problem is formulated as a spatial–temporal prediction. The proposed model is composed of convolutional long short-term memory (ConvLSTM), and convolutional neural network (CNN) units with encoder–decoder structure. Specifically, long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning order depende

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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long

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Zhao, Yao, Zhidan Zhong, Haobo Zhang, ZhiHui Zhang, and AoYu Yang. "Fault diagnosis of Rolling Bearing Based on One-dimensional Residual Convolution Recurrent Neural Network." Journal of Physics: Conference Series 2400, no. 1 (2022): 012058. http://dx.doi.org/10.1088/1742-6596/2400/1/012058.

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Abstract To resolve the issue that conventional rolling bearing fault diagnosis technology are incapable of extracting features adaptively, a one-dimensional residual convolutional recurrent neural network (1DRCRNN-LSTM) is proposed to obtain signal characteristics directly from the original signal. Firstly, a train-valid-test paradigm dataset with sample overlap is created by data augmentation and one-hot coding. Secondly, a convolutional neural network (CNN) and a long short-term memory neural network (LSTM) are fused and a residual learning mechanism is introduced to build a network model f

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Prasad, Bathaloori Reddy. "Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1097–108. http://dx.doi.org/10.47059/revistageintec.v11i2.1739.

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Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies s

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K A, Shirien, Neethu George, and Surekha Mariam Varghese. "Descriptive Answer Script Grading System using CNN-BiLSTM Network." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 139–44. http://dx.doi.org/10.35940/ijrte.e5212.019521.

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Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to lea

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Chen, Zhixin, Xu Zhang, Zhiyuan Li, and Anchu Li. "Construction of the Open Oral Evaluation Model Based on the Neural Network." Scientific Programming 2021 (September 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/3928246.

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According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model

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Zhang, Chun-Xiang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu, and Bo Yu. "Attention Neural Network for Biomedical Word Sense Disambiguation." Discrete Dynamics in Nature and Society 2022 (January 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/6182058.

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In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from 4 adjacent lexical units are extracted as disambiguation features. The attention layer is used to generate a feature matrix. Average asymmetric convolutional neural networks (Av-ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. The softmax function is applied to determine the semantic category

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Dang, Zhaoyang, Bei Sun, Can Li, Shudong Yuan, Xiaoyue Huang, and Zhen Zuo. "CA-LSTM: An Improved LSTM Trajectory Prediction Method Based on Infrared UAV Target Detection." Electronics 12, no. 19 (2023): 4081. http://dx.doi.org/10.3390/electronics12194081.

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In order to improve the UAV prevention and control capability in key areas and improve the rapid identification and trajectory prediction accuracy of the ground detection system in anti-UAV early warnings, an improved LSTM trajectory prediction network CA-LSTM (CNN-Attention-LSTM) based on attention enhancement and convolution fusion structure is proposed. Firstly, the native Yolov5 network is improved to enhance its detection ability for small targets of infrared UAVs, and the trajectory of UAVs in image space is constructed. Secondly, the LSTM network and convolutional neural network are int

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