Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

“…The model named Spatio-Temporal Anomaly Detection Network (STADNet) captures the spatio-temporal features of video images through multi-scale Three-Dimensional (3D) convolution module and spatio-temporal attention mechanism. …”
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Fault Diagnosis for Bearing Based on 1DCNN and LSTM by Haibin Sun, Shichao Zhao

“…In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. …”
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MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN by Vishal Perekadan, Chaity Banerjee, Tathagata Mukherjee, Eduardo Pasiliao, Hovannes Kulhandjian, Michel Kulhandjian

“…In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data.  …”
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An urban road traffic flow prediction method based on multi-information fusion by Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang

“…Then, a superimposed one-dimensional inflated convolutional layer is used to extract long-term trends, a dynamic graph convolutional layer to extract periodic features, and a short-term trend extractor to learn short-term temporal features. …”
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Urban Traffic Flow Forecasting Based on Graph Structure Learning by Guangyu Huo, Yong Zhang, Yimei Lv, Hao Ren, Baocai Yin

“…At the same time, the temporal convolution network captures the temporal correlation between a single time series. …”
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Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. …”
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Analysis of baseball behavior recognition model based on Dual-GCN improved by motion weights by Ji Li

“…A motion weight improvement model based on dual-graph convolutional network is proposed. The new model takes a dual-graph convolutional network for behavior recognition and key region segmentation of baseball video images, and enhances the correlation and contribution between characters through motion weights. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…Considering Chaohu Lake as a case study, we proposed a random forest algorithm based on the convolutional neural network (CNN-RF) to investigate the spatial and temporal patterns of PP concentration in the lake using HJ-2 hyperspectral satellite images. …”
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Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain by Damián Gulich, Myrian Tebaldi, Daniel Sierra-Sosa

“…These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.…”
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Mapping Crop Types for Beekeepers Using Sentinel-2 Satellite Image Time Series: Five Essential Crops in the Pollination Services by Navid Mahdizadeh Gharakhanlou, Liliana Perez, Nico Coallier

“…Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). …”
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An Accelerated FPGA-Based Parallel CNN-LSTM Computing Device by Xin Zhou, Wei Xie, Han Zhou, Yongjing Cheng, Ximing Wang, Yun Ren, Shandong Yuan, Liuwen Li

“…Recently, the combination of convolutional neural network (CNN) and long short-term memory (LSTM) exhibits better performance than single network architecture. …”
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A novel lightweight 3D CNN for accurate deformation time series retrieval in MT-InSAR by Mahmoud Abdallah, Xiaoli Ding, Samaa Younis, Songbo Wu

“…To address this limitation, we propose UNet-3D, a novel three-dimensional encoder-decoder architecture that captures the spatiotemporal features of phase components through an enhanced 3D convolutional neural network (CNN) ensemble, enabling accurate separation of deformation time series. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
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Classification and Analysis of Employee Feedback with Deep Learning Algorithms by Beyza Eken, Serap Çakar Kaman, Gökhan Yiğidefe

“…To overcome the challenges of manual feedback analysis, the study employs Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) algorithms. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…The proposed model leverages Convolutional Neural Networks (CNN) to extract spatial features, Long Short-Term Memory networks (LSTM) to model temporal dependencies, and a Spatial Attention Mechanism (SAM) to enhance feature weighting dynamically. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. …”
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Pattern transition recognition based on transfer learning for exoskeleton across different terrains by Yifan Gao, Jianbin Zheng, Yang Gao, Ziyao Chen, Jing Tang, Liping Huang

“…In the study, a novel transfer learning method based on temporal convolutional network spatial attention (TCN-SA) is applied for pattern transition recognition under triple physical loads on different terrains. …”
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GNN for LoRa Device Fingerprint Identification by Bojun Zhang

“…The system employs GGC(Gated Graph Convolution) networks to extract feature representations from the graphs, which are then encoded by a time encoder, specifically an LSTM(Long Short-Term Memory) network, to obtain a coarse-grained temporal representation. …”
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Short-Term Traffic Flow Forecasting Model Based on GA-TCN by Rongji Zhang, Feng Sun, Ziwen Song, Xiaolin Wang, Yingcui Du, Shulong Dong

“…The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. …”
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A Comparative Study of Deep Learning’s Performance Methods for News Article using Word Representations by Iman Saladin B. Azhar, Winda Kurnia Sari, Naretha Kawadha Pasemah Gumay

“…This research evaluates the effectiveness of three deep learning techniques Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) for online news classification using 300-dimensional GloVe word representations. …”
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