MCT-CNN-LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network by Kaiyu Chen, Yue Diao, Yucheng Wang, Xiafeng Zhang, Yannian Zhou, Minming Gu, Bo Zhang, Bin Hu, Meng Li, Wei Li, Shaoxi Wang

“…Initially, a multi-channel convolutional neural network (CNN) combined with a Long Short-Term Memory Network (LSTM) is employed. …”
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SGRD: A Ship Group Relationship Description Method Based on Scene Graph Generation With a Global-Local Context Fusion Network by Qianwen Rui, Yanan You, Jingyi Cao, Kaiwen Zhu, Yuanyuan Qiao

“…To address this, we propose a ship group relationship description (SGRD) method based on remote sensing SGG with a global and local context fusion network, called GLFN. The proposed network integrates global feature fusion through a transformer-based self-attention mechanism and enhances local feature fusion using a graph convolutional network focused on object-specific graph structures. …”
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Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach by Waqar Khan, Muhammad Usama, Muhammad Shahbaz Khan, Oumaima Saidani, Hussam Al Hamadi, Noha Alnazzawi, Mohammed S. Alshehri, Jawad Ahmad

“…The model integrates a Transformer-based encoder, Convolutional Neural Networks (CNNs), and Variational Autoencoder-Long Short-Term Memory (VAE-LSTM) networks to enhance anomaly detection capabilities. …”
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Research on ultra wide band NLoS/LoS recognition method based on the fusion of 1DCNN and LSTM by ZHENG Enrang, MENG Xin, JIANG Suying, XUE Jing, ZHANG Yi, LI Qiang

“…Convolutional neural network (CNN) was first employed to extract spatial features from channel impulse response (CIR) data, and LSTM network was used to capture their temporal characteristics. …”
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CochleaSpecNet: An Attention-Based Dual Branch Hybrid CNN-GRU Network for Speech Emotion Recognition Using Cochleagram and Spectrogram by Atkia Anika Namey, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…This research introduces a novel SER approach that utilizes cochleagram and spectrogram features to capture relevant speech patterns for the classifier network. The network integrates a hybrid model that combines Convolutional Neural Networks (CNN) for feature extraction with Gated Recurrent Units (GRU) to handle temporal dependencies. …”
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Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model by Lizhen Lu, Yunci Xu, Xinyu Huang, Hankui K. Zhang, Yuqi Du

“…In this paper, we demonstrated a large-scale PML mapping using Sentinel-2 data by combining the PML domain knowledge and the deep Convolutional Neural Network (CNN). We developed a dual-branch Index-Feature-Spatial-Attention fused Deep Learning Model (IFSA_DLM) for effectively acquiring and fusing multi-scale discriminative features and thus for accurately detecting PML. …”
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Siamese text classification network (SiamTCN) for multi-class multi-label information extraction of typhoon disasters from social media data by Zhi He, Chengle Zhou, Liwei Zou, Suhong Zhou, Xueqiang Zhao

“…This paper proposes a siamese text classification network (SiamTCN) for multi-class multi-label information extraction from typhoon disasters based on Sina Weibo data. …”
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Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea by Tianhong Ding, Zhiqiang Xu, Yunjie Wang, Qinglian Hou, Xiangyong Liu, Fengshuang Ma

“…A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. …”
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Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion by Chenxi LIU, Dong WANG, Huiling CHEN, Renfa LI

“…By effectively capturing the spatio-temporal characteristics of urban private car travel, a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly, private car trajectory and area-of-interest data were integrated.Secondly, the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs, the multi-graph convolution-attention network (MGC-AN) was proposed to extract the spatio-temporal characteristics of private car travel.Finally, the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets, which were collected in Changsha and Shenzhen.The experimental results show that, compared with the existing prediction model, the root mean square error of the MGC-AN is reduced 11.3%～20.3%, and the average absolute percentage error is reduced 10.8%～36.1%.…”
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Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approach... by Tathagat Banerjee, Prachi Chhabra, Manoj Kumar, Abhay Kumar, Kumar Abhishek, Mohd. Asif Shah

“…To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. …”
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Enhancing prediction of fluid-saturated fracture characteristics using deep learning super resolution by Manju Pharkavi Murugesu, Vignesh Krishnan, Anthony R. Kovscek

“…To this end, we acquired multiscale low- and high-resolution CT rock images in unsaturated and saturated states. Among GAN and convolutional neural networks, GAN’s produce realistic high-resolution reconstructions of saturated geological media when trained using high-resolution, unsaturated images and lower resolution images in various saturation states. …”
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Deep learning in time series forecasting with transformer models and RNNs by Rogerio Pereira dos Santos, João P. Matos-Carvalho, Valderi R. Q. Leithardt

“…In contrast, RNN models such as auto-temporal convolutional networks (TCN) and bidirectional TCN (BiTCN) were better suited to short-term forecasting, despite being more prone to significant errors. …”
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Residual Life Prediction of Pneumatic Control Valves Based on Trans-TCN-GRU Modeling by Guoning Li, Jinjin Chen, Yongting Tao, Anxu Li, Xinwang Zhang

“…The approach involved using an improved Transformer model for feature extraction from the data, applying a Temporal Convolutional Network (TCN) to mine temporal relationships among the data, and finally using a GRU module to extract features from non-time-series operating condition data. …”
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MSTCNet: Toward Generalization Improving for Multiframe Infrared Small Target Detection by Ruining Cui, Na Li, Junfu Liu, Huijie Zhao

“…To solve the problems mentioned above, combining the concept of domain generalization (DG) in transfer learning, we propose a multiscale spatio-temporal feature combined network (MSTCNet). First, we utilize the advantages of convolutional neural networks and recurrent neural networks, integrating them to build a high-performance structure. …”
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Daily soil moisture prediction during winter wheat growth season using an SCSSA-CNN-BiLSTM model by CUI Song, WU Jin, ZHANG Naifeng, LIU Meng, HU Yongsheng, HE Yanan, GU Yue, LONG Xinya, WANG Zhenlong

“…It was then combined with Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal sequence learning. …”
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A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM by Qiang Liu, Zhengwei Dai, Hongxi Lai, Minghao Chen, Huiyuan Huang, Jiahui Fu, Mingxin Hou, Xiaoming Xu, Guangbin Wang, Jin Yan

“…Compared to conventional deep learning-based bearing life prediction methods, the proposed approach leverages a temporal convolutional network (TCN) to extract long-term temporal dependencies and higher-level spatial features from historical data, while employing a long short-term memory (LSTM) network to capture short-term temporal dependencies and sequence relationships. …”
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Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism by Fanglan Ma, Changsheng Zhu, Peng Lei

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Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…In response to the tilting and damage disasters of deep vertical shafts in thick water-bearing loose layers, the tilting and deformation monitoring of shafts was carried out by taking the deep vertical shaft (800 m) of a mine in Lunan as the research object, studying the spatial and temporal change characteristics of shaft tilting, and analyzing the main influencing factors of shaft tilting; based on this, based on the deep learning theory, four types of deep learning method, namely, recurrent neural network (RNN), long and short-term memory network (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1DCNN), were used. unit (GRU), and one-dimensional convolutional neural network (1DCNN) to construct a wellbore tilt deformation prediction model, and compare the prediction results with the measured values to analyze the accuracy of the wellbore deformation prediction model, validate the reliability of the model, studied overall wellbore and critical area prediction effects, and carry out engineering applications. …”
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Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…Firstly, four disaster indicators (population disaster index, housing disaster index, agricultural disaster index and economic disaster index) were introduced to reflect different losses, which could form a comprehensive disaster index to quantify the overall loss degree; Second, with raster data and VGGNet, a lightweight regression convolutional neural network model VGG-Light was proposed to solve these problem; Third, focused on impact of precipitation on disaster situations, the ConvLSTM module was used to capture the spatiotemporal characteristics of precipitation data, and then the TSVGG-Light model was presented for feature fusion. …”
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). …”
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