Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. …”
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CNN-SENet: a GNSS-R ocean wind speed retrieval model integrating CNN and SENet attention mechanism by Yimin Xia, Dongliang Guan, Zhiling Zhou

“…The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network (CNN) models to retrieve sea surface wind speed from GNSS-R observables. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. …”
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End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

“…Initially, the networks were built using different convolutional layers and weights for feature extraction, and then the fully connected layers extracted the temperature profiles of the PCM. …”
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Research on Bearing Fault Diagnosis Method Based on MESO-TCN by Ruibin Gao, Jing Zhu, Yifan Wu, Kaiwen Xiao, Yang Shen

“…To address the issues of information redundancy, limited feature representation, and empirically set parameters in rolling bearing fault diagnosis, this paper proposes a Multi-Entropy Screening and Optimization Temporal Convolutional Network (MESO-TCN). The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. …”
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Radar HRRP Feature Fusion Recognition Method Based on ConvLSTM Network with Multi-Input Gate Recurrent Unit by Wei Yang, Tianqi Chen, Shiwen Lei, Zhiqin Zhao, Haoquan Hu, Jun Hu

“…To fully exploit the multi-domain information present in HRRP sequences, this paper proposes a novel target feature fusion recognition approach. By combining a convolutional long short-term memory (ConvLSTM) network with a cascaded gated recurrent unit (GRU) structure, the proposed method leverages multi-domain and temporal information to enhance recognition performance. …”
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Crude Oil and Hot-Rolled Coil Futures Price Prediction Based on Multi-Dimensional Fusion Feature Enhancement by Yongli Tang, Zhenlun Gao, Ya Li, Zhongqi Cai, Jinxia Yu, Panke Qin

“…In addition, a deep fusion model is constructed, which combines the temporal feature extraction ability of the convolution neural network with the nonlinear mapping advantage of an extreme gradient boosting tree. …”
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Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning by Wang Juan, Liu Wei, Zhang Yong, Liu Zhi, Zheng Xiaolei, Wang Yuxin, Hao Jianshu, Dai Xuanding

“…To overcome this challenge, this study proposes an error estimation method that integrates highway convolutional neural networks with bidirectional long short-term memory (LSTM) networks, which enables real-time prediction of measurement performance at charging piles. …”
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Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration by YAO Gang, WANG Yun, WANG Yuanliang, SONG Zihao

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A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images by Reda Yaagoubi, Charaf-Eddine Lakber, Yehia Miky

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Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution by Vivaldi Rinaldi, Masoud Ghandehari

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Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme by Anyuan Zhang, Qi Li, Zhenlan Li, Jiming Li

“…Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. …”
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A Deep Learning Model for NOx Emissions Prediction of a 660 MW Coal-Fired Boiler Considering Multiscale Dynamic Characteristics by Jianrong Huang, Yanlong Ji, Haiquan Yu

“…This study applies a Multiscale Graph Convolutional Network (MSGNet) designed to capture multiscale dynamic relationships among operational parameters of a 660 MW coal-fired boiler. …”
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Intelligent Operation and Maintenance of Wind Turbines Gearboxes via Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun, Binkai Wang

“…Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. …”
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Vegetation classification in a subtropical region with Sentinel-2 time series data and deep learning by Ming Zhang, Dengqiu Li, Guiying Li, Dengsheng Lu

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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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Detection of the origin of wolfberry based on electronic nose and electronic tongue combined with LSTM-AM-M1DCNN by MA Zeliang, LIU Yaqian, CHENG Qifeng, WANG Pingping, YANG Tianxing, DU Gang

“…The accuracy, precision, recall, and F1-Score of the test set reached 97.4%, 97.6%, 97.4%, and 0.974, respectively.ConclusionThe use of LSTM-AM-M1DCNN overcomes the limitations of traditional convolutional neural networks that are not fully capable of extracting temporal and spatiotemporal features. …”
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STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data by Wei Zhang, Weiming Zeng, Hongyu Chen, Jie Liu, Hongjie Yan, Kaile Zhang, Ran Tao, Wai Ting Siok, Nizhuan Wang

“…<b>New Method</b>: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. …”
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