Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning by Guangning Qin

“…Combining the channel attention mechanism with deep convolution, a dynamic channel attention convolution (DCAConv) is proposed, which can dynamically adjust the channel weights and capture key features more sensitively. …”
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Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Special emphasis is placed on deep learning methods, categorized by network architecture into sequential models, convolutional neural networks, graph convolutional networks,  generative adversarial networks, etc. …”
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A capsule network framework for flood mapping integrating remote sensing fusion techniques by Pouya Ahmadi, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, Nazila Kardan Halvaie, Ebrahim Ghaderpour

“…The rising frequency of flood events and concerns about the likelihood of future occurrences underscore the urgent need for thorough investigations into flood dynamics. Creating a flood hazard area map is the first critical step in evaluating flood-related damage. …”
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State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM by Yu Zhao, Yonghong Xu, Yidi Wei, Liang Tong, Yiyang Li, Minghui Gong, Hongguang Zhang, Baoying Peng, Yinlian Yan

“…To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. …”
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Design of an Iterative Method for Time Series Forecasting Using Temporal Attention and Hybrid Deep Learning Architectures by Yuvaraja Boddu, A. Manimaran

“…This limitation becomes increasingly problematic in dynamic environments where temporal relevance and variable interdependencies fluctuate significantly. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…Satellite cloud images exhibit complex multidimensional characteristics, including spectral, textural, and spatiotemporal dynamics. The temporal evolution of cloud systems plays a crucial role in accurate classification, particularly under the coexistence of multiple weather systems. …”
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Predicting the Evolution of the Supercontinuum Generation With CNN-LSTM Model by Yi Feng, Ruiyuan Liu, Xinyue Chang, Xiangzhen Huang, Yuan He, Ning Li, Tiantian Zhou, Chujun Zhao

“…We propose a hybrid deep learning model, namely convolutional neural network–long short-term memory (CNN-LSTM) approach to investigate the evolution of the supercontinuum (SC) generation numerically. …”
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Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images by Long Gao, Liyuan Li, Jianing Yu, Xiaoxuan Zhou, Lu Zou, Nan Fang, Xiaofeng Su, Fansheng Chen

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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…This study aims to develop a robust, dynamic, and accurate approach to traffic density analysis, vehicle classification, and dynamic signal control in order to achieve high accuracy in traffic flow analysis. …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…Classifying galaxies is an essential step for studying their structures and dynamics. Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in nongalaxy and galaxy classes, respectively. …”
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Low-Memory-Footprint CNN-Based Biomedical Signal Processing for Wearable Devices by Zahra Kokhazad, Dimitrios Gkountelos, Milad Kokhazadeh, Charalampos Bournas, Georgios Keramidas, Vasilios Kelefouras

“…This work studies the impact of CNN architecture parameters, focusing on the convolutional and dense layers, to identify configurations that balance accuracy and efficiency. …”
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Automatic detection of foreign object intrusion along railway tracks based on MACENet. by Xichun Chen, Yu Tian, Ming Li, Bin Lv, Shuo Zhang, Zixian Qu, Jianqing Wu, Shiya Cheng

“…The network architecture integrates the GOLD-YOLO module, an advanced object detection approach, alongside the updated deformable convolutional networks (DCNv3). The incorporation of DCNv3 allows the model to dynamically adapt its sampling positions according to actual object shapes, significantly enhancing feature extraction accuracy, especially for irregularly shaped intrusions. …”
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Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN by Chun Wang, Weihua Zhang, Cong Wu, Heng Hu, Wenjia Zhu

“…The TCN architecture integrates the advantages of recurrent neural network (RNN) and convolutional neural network (CNN), which could fully use the previous DHW information. …”
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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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Optimal features assisted multi-attention fusion for robust fire recognition in adverse conditions by Inam Ullah, Nada Alzaben, Yousef Ibrahim Daradkeh, Mi Young Lee

“…Our approach introduces three key innovations: Firstly, Convolutional Self-Attention (CSA), integrating global self-attention with convolution through dynamic kernels and trainable filters for enhanced low-level fire feature processing. …”
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Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net. by Bin Wu, Mei Xue, Ying Jia, Ning Zhang, GuoJin Zhao, XiuPing Wang, Chunlei Zhang

“…To address these challenges, we propose ASTM‑Net, an Activity‑aware SpatioTemporal Multi‑branch graph convolutional network comprising two novel modules. First, the Activity‑aware Spatial Graph convolution Module (ASGM) dynamically models Activity‑Aware Adjacency Graphs (3A‑Graphs) by fusing a manually initialized physical graph, a learnable graph optimized end‑to‑end, and a dynamically inferred, activity‑related graph-thereby capturing evolving spatial affinities. …”
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TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions by Huizhi Xu, Wenting Tan, Yamei Li, Yue Tian

“…The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. …”
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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

“…We propose a hybrid prediction model—PSO–CNN–LSTM–BOA–XGBoost (PCLBX)—that integrates a particle swarm optimization (PSO)-enhanced convolutional LSTM (CNN–LSTM) with a Bayesian optimization algorithm-tuned XGBoost (BOA–XGBoost). …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…To address these limitations, this study proposes a novel hybrid deep learning neural network (DLNN) model that integrates bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU) architectures to effectively learn complex temporal dependencies in power system dynamics. The model is benchmarked against baseline architectures, including Bi-LSTM, Bi-GRU, and convolutional neural networks (CNNs). …”
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A Temporal–Spatial–Spectral Fusion Framework for Coastal Wetland Mapping on Time-Series Remote Sensing Imagery by Xiang Li, Shenfu Zhang, Qiang Liu, Liang Chen, Gang Yang, Rui Zhao, Weiwei Sun, Feng Shao, Xiangchao Meng

“…A hybrid transformer-convolution module enhances fine-grained feature extraction. …”
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