Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…We also assessed the predictability of global mixing ellipses using machine learning algorithms, including Spatial Transformer Networks (STN), Convolutional Neural Network (CNN) and Random Forest (RF), with mean-flow and eddy- properties as features. …”
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TF-CEP: carbon emission prediction with data augmentation and temporal-frequency fusion contrasting by Zhiqiang Ma, Qi Yang, Fei Liang, Yuliang Shi, Jieying Kang, Peng Liu

“…In order to improve the generalization ability of the model, GAN is used for data augmentation of the power data, and 1-Dimensional Convolutional Neural Network and frequency enhanced channel attention Mechanism methods are used to learn the temporal domain feature information and the frequency domain feature information of the power data, respectively. …”
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Temporal Analysis Based Driver Drowsiness Detection System Using Deep Learning Approaches by Furkan Kumral, Ayhan Küçükmanisa

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Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. by Jinhao Yang, Junwen Cao, Mingyu Fang

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Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data by Suraj A. Yadav, Xin Zhang, Nuwan K. Wijewardane, Max Feldman, Ruijun Qin, Yanbo Huang, Sathishkumar Samiappan, Wyatt Young, Francisco G. Tapia

“…The proposed feature engineering and prediction model followed a two-fold approach: first, adoption of partial least squares regression (PLSR) algorithm to extract features relevant to yield, and second, a novel context-aware attention and residual connection convolution-bidirectional gated recurrent unit bidirectional long short-term memory-network (CAR Conv1D-BiGRU-BiLSTM-Net) to exploit time-series multifeatures information to predict final yield. …”
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Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models by Abdul Rehman, Nadeem Akhtar, Omar H. Alhazmi

“…Our results show that the Convolutional Neural Network (CNN) model achieved an overall accuracy of 98.7% with class-specific accuracy ranging from with F1-scores across all classes (e.g., 0.90 for Powdery Mildew and 0.87 for Army Worm).…”
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Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory by Xiaolu Wei, Binbin Lei, Hongbing Ouyang, Qiufeng Wu

“…The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).…”
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CGLCS-Net: Addressing Multi-Temporal and Multi-Angle Challenges in Remote Sensing Change Detection by Ke Liu, Hang Xue, Caiyi Huang, Jiaqi Huo, Guoxuan Chen

“…Currently, deep learning networks based on architectures such as CNN and Transformer have achieved significant advances in remote sensing image change detection, effectively addressing the issue of false changes due to spectral and radiometric discrepancies. …”
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Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer by Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad, Ali M. Al‐Shaery, Saad Althobaiti, Muhammad Faheem

“…The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). The TFT has a mean square error (MSE) of 0.0003, root mean square error (RMSE) of 0.0173, mean absolute error (MAE) of 0.0112 and mean absolute percentage error (MAPE) of 1.76%. …”
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Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen, Anderson S. Oliveira

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Toward Spatio‐Temporally Consistent Multi‐Site Fire Danger Downscaling With Explainable Deep Learning by Óscar Mirones, Jorge Baño‐Medina, Swen Brands, Joaquín Bedia

“…Abstract This study introduces a novel Convolutional Long Short‐Term Memory neural networks (ConvLSTM)‐based multi‐site downscaling approach for fire danger prediction, that leverages the properties of Long‐Short Term Memory (LSTM) Recursive Neural Networks and Convolutional Neural Networks (CNNs) by learning daily Multivariate‐Gaussian distributions conditioned on large‐scale atmospheric predictors. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…The method incorporates a multi-scale temporal attention module and a multi-scale temporal convolution module to extract multi-scale information. …”
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EEG-Based Seizure Onset Detection of Frontal and Temporal Lobe Epilepsies Using 1DCNN by Xiaoshuang Wang, Guanyu Wang, Tingting Wu, Ying Wang, Tommi Karkkainen, Fengyu Cong

“…Given this, this work concentrates on seizure detection using scalp EEG signals collected from people with frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). Method: 20 FLE patients and 20 TLE patients are utilized in our work, and a parallel one-dimensional convolutional neural network (1DCNN) model is built for classification. …”
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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning by Mohsen Mozafari, Andrew J. Law, Rafik A. Goubran, James R. Green

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Temporal Dynamics in Short Text Classification: Enhancing Semantic Understanding Through Time-Aware Model by Khaled Abdalgader, Atheer A. Matroud, Ghaleb Al-Doboni

“…The model employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enriched with attention mechanisms to capture both local and global dependencies. …”
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ST-MSRN: An enhanced spatio-temporal super-resolution model for complex meteorological data reconstruction by Ping Mei, Zhi Yang, Changzheng Liu, Lei Wang, Zixin Yin

“…To address these limitations, this study proposes a Spatio-Temporal Multi-Scale Residual Network (ST-MSRN), which integrates a Multi-Scale Residual Feature Block (MSRFB) with a Channel Stacking Mechanism. …”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. …”
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Deep-Learning Integration of CNN–Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection by Abbas Mohammed Noori, Abdul Razzak T. Ziboon, Amjed N. AL-Hameedawi

“…It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. …”
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Vit-Traj: A Spatial–Temporal Coupling Vehicle Trajectory Prediction Model Based on Vision Transformer by Rongjun Cheng, Xudong An, Yuanzi Xu

“…In recent years, data-driven vehicle trajectory prediction models have become a significant research focus, and various spatial–temporal neural network models, based on spatial–temporal data, have been proposed. …”
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AMST2: aggregated multi-level spatial and temporal context-based transformer for robust aerial tracking by Hasil Park, Injae Lee, Dasol Jeong, Joonki Paik

“…Abstract Recently, many existing visual trackers have made significant progress by incorporating either spatial information from multi-level convolution layers or temporal information for tracking. …”
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