MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction by Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu

“…To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). …”
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SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing by Qingjun Niu, Kun Wu, Jialu Zhang, Zhenqi Han, Lizhuang Liu

“…However, existing dehazing methods using vanilla convolution only extract features in the temporal domain and lack the ability to capture multi-directional information. …”
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Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks. by Hongxia Wang, Yang Liu, Zhenkai Nie

“…The Kolmogorov-Arnold Networks (KANs) combined with Temporal Convolutional Networks (TCNs) greatly improve the ability to extract temporal features. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…With the development of computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), Transformers, and diffusion models, have recently been introduced into the field of spatiotemporal fusion, resulting in efficient and accurate algorithms. …”
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Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs by Shiwei Chu, Wenxia Bao

“…To address these challenges, this study proposes a novel Dynamic Agricultural Pest Classification System that integrates an enhanced Self-Activation Optimization Convolutional Neural Network (SAO-CNN) with bio-inspired swarm intelligence for UAVs. …”
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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). …”
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Hybrid Neural Network-Based Maritime Carbon Dioxide Emission Prediction: Incorporating Dynamics for Enhanced Accuracy by Seunghun Lim, Jungmo Oh

“…We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). …”
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Prediction of foreign currency exchange rates using an attention-based long short-term memory network by Shahram Ghahremani, Uyen Trang Nguyen

“…We conducted comprehensive experiments to evaluate and compare the performance of ALFA against several models used in previous work and against state-of-the-art deep learning models such as temporal convolutional networks (TCN) and Transformer. …”
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Hybrid deep learning framework for robust time-series classification: Integrating inception modules with residual networks by Duong Thi Kim Chi, Nguyen Thi Mai Trang, Tran Ba Minh Son, Nguyen Ngoc Thao, Thanh Q. Nguyen

“…To overcome these issues, convolutional neural networks (CNNs), particularly the Inception architecture, have emerged as powerful alternatives due to their ability to capture multiscale local patterns efficiently. …”
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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

“…We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. …”
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Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland by Y. Koo, Y. Koo, Y. Koo, G. Cheng, M. Morlighem, M. Rahnemoonfar, M. Rahnemoonfar

“…In this study, we adopt three standard graph neural network (GNN) architectures, including graph convolutional network, graph attention network, and equivariant graph convolutional network (EGCN), to develop surrogate models for finite-element simulations from the Ice-sheet and Sea-level System Model. …”
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Learning Developmental Age From 3D Infant Kinetics Using Adaptive Graph Neural Networks by Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Tuiskula, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

“…These data are modeled using adaptive graph convolutional networks (AAGCNs), able to capture the spatio-temporal dependencies in infant movements. …”
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Digital Twin-Supported Battery State Estimation Based on TCN-LSTM Neural Networks and Transfer Learning by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…In this study, we propose a DT-supported battery state estimation method, in collaboration with the temporal convolutional network (TCN) and the long short-term memory (LSTM), to address the challenge of feature extraction. …”
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MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos by Wenqin Yan, Jialiang Zhuang, Yuheng Chen, Yun Zhang, Xiujuan Zheng

“…In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). …”
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Improving speaker-independent visual language identification using deep neural networks with training batch augmentation by Jacob L. Newman

“…We use the YOLO object detection algorithm to track the mouth through time, and we employ an ensemble of 3D Convolutional and Recurrent Neural Networks for this classification task. …”
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SLA-MLP: Enhancing Sleep Stage Analysis from EEG Signals Using Multilayer Perceptron Networks by Farah Mohammad, Khulood Mohammed Al Mansoor

“…The key steps of this approach include data collection, where diverse and high-quality EEG data are gathered; preprocessing, which involves signal cropping, spectrogram conversion, and normalization to prepare the data for analysis; data balancing, where class weights are adjusted to address any imbalances in the dataset; feature extraction, utilizing Temporal Convolutional Networks (TCNs) to extract meaningful features from the EEG signals; and final classification, applying a Multilayer Perceptron (MLP) to accurately predict sleep stages. …”
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Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays by Haya Al Kassir, Nikolaos V. Kantartzis, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos, Christos G. Christodoulou, Zaharias D. Zaharis

“…To this end, the superiority of the ResNet architecture in DOA estimations is substantiated through a comparison analysis with two other highly recognized NNs, namely, the feed-forward NN and the convolutional NN. Numerical results indicate that the ResNet model exhibits notable precision in estimating the AOAs, across various classes within a broad spectrum, along with a rapid temporal response. …”
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A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning by Shaokang Cai, Dezhi Han, Xinming Yin, Dun Li, Chin-Chen Chang

“…Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. …”
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TADNet: A Time and Attention-Based Point Cloud Denoising Network for Autonomous Driving in Adverse Weather by Y. Zhang, H. Huang, X. Yan, Y. Liang, Y. Li, J. Yang

“…The method is based on the 3D-OutDet network with the addition of Convolutional Block Attention Module (CBAM), which highlights important features and suppresses minor ones. …”
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Attention-Based Deep Neural Network for High-Dimensional Microwave Modeling of Non-Reciprocal Bandpass Filters by Girdhari Chaudhary, Yao Meng, Dong Sun Park, Yongchae Jeong

“…The proposed method integrates convolutional neural networks (CNN), joint channel-spatial attention, and residual connections to enhance prediction accuracy and model generalization. …”
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