Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction by Zhu Chen, Yuedan Liu, Zhibin Qin, Haojie Li, Siyuan Xie, Litian Fan, Qilin Liu, Jin Huang

“…Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. …”
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A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics by Arash Shahbazpoor Shahbazi, Farzin Irandoost, Reza Mahdavian, Seyedehsamaneh Shojaeilangari, Abdollah Allahvardi, Hossein Naderi-Manesh

Subjects: “…Convolutional neural network (CNN)…”
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PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

Subjects: “…High dynamic range (HDR) imaging…”
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Fully convolutional video prediction network for complex scenarios by Rui Han, Shuaiwei Liang, Fan Yang, Yong Yang, Chen Li

“…It replaced high-latency recurrent models with fully convolutional ones, improving inference speed. Furthermore, it addressed the dynamic nature of environments with multilevel frequency domain encoders and decoders, facilitating spatial and temporal learning. …”
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Convolutional kernel-based classification of industrial alarm floods by Gianluca Manca, Alexander Fay

“…In the transformation stage, alarm floods are subjected to an ensemble of convolutional kernel-based transformations (MultiRocket) to extract their characteristic dynamic properties, which are then fed into the classification stage, where a linear ridge regression classifier ensemble is used to identify recurring alarm floods. …”
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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…The network introduces a frequency-domain-based convolutional attention mechanism to extract spatial information effectively. …”
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Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model by Gustavo de Souza Ferreti, Thuanne Paixão, Ana Beatriz Alvarez

“…In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. …”
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Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions by Yuxuan Wang, Zhouyuan Zhang, Shu Pi, Haishan Zhang, Jiatian Pi

“…By integrating the strengths of Transformer and Graph Convolutional Recurrent Unit (GCRU) technologies within its Dual-Gated Graph Convolutional Recurrent Unit architecture, DG3L provides a mechanism for fusing Transformer features with contextual features from recurrent units. …”
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A Low-Complexity Transformer-CNN Hybrid Model Combining Dynamic Attention for Remote Sensing Image Compression by L. L. Zhang，X. J. Wang，J. H. Liu, Q. Z. Fang

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Dynamic Q value wavelet transform for IF estimation and seizure detection via QT plane-CNN framework by Amaya Rose Abraham, Anurag Nishad, Mihir Jaipuria, Abhay Upadhyay, Sudeep Baudha

Subjects: “…Dynamic Q value wavelet transform…”
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An Interpretable Data-Driven Dynamic Operating Envelope Calculation Method Based on an Improved Deep Learning Model by Yun Li, Tunan Chen, Jianzhao Liu, Zhaohua Hu, Yuchen Qi, Ye Guo

Subjects: “…convolutional neural networks…”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

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Compression of Marine Environmental Data Using Convolutional Attention Autoencoder by Xuehai Sun, Peiyu Wang, Yanxia Zhou, Kedi Wu, Limin Huang, Xuewen Ma

“…Ocean temperature data is fundamental to the study of ocean dynamics and climate change, and its efficient compression and storage are critical for large-scale data analysis and transmission. …”
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Emotion recognition based on convolutional gated recurrent units with attention by Zhu Ye, Yuan Jing, Qinghua Wang, Pengrui Li, Zhihong Liu, Mingjing Yan, Yongqing Zhang, Dongrui Gao

“…Most existing models extract a single temporal feature from the EEG signal while ignoring the crucial temporal dynamic information, which, to a certain extent, constrains the classification capability of the model. …”
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SDN Anomalous Traffic Detection Based on Temporal Convolutional Network by Ziyi Wang, Zhenyu Guan, Xu Liu, Caixia Li, Xuan Sun, Jun Li

“…The wide application of software-defined network (SDN) architecture, combined with its centralized control characteristics, have exacerbated the potential risk of network attacks, and the traditional anomaly traffic detection methods are facing the challenges of high false alarm rate and insufficient generalization ability due to the reliance on manual rule design and the difficulty in capturing dynamic temporal features. In response to these challenges, we propose a Temporal Convolutional Network (TCN)-based anomalous traffic detection method for SDN. …”
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Frame points attention convolution for deep learning on point cloud by Luyang Li, Ligang He, Jinjin Gao, Xie Han

“…FPAC then combines the quantified correlations with the weights of the frame points to generate spatially continuous filters. The convolution weights for different local areas in the filters are calculated dynamically, without relying on generative models or probabilistic assumptions. …”
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Hypergraph Convolution Network Classification for Hyperspectral and LiDAR Data by Lei Wang, Shiwen Deng

“…Although deep learning methods based on convolutional neural networks (CNNs), transformers, and graph convolutional networks (GCNs) have demonstrated promising results in fusing complementary multi-source data, existing methodologies demonstrate limited efficacy in capturing the intricate higher-order spatial–spectral dependencies among pixels. …”
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Knowledge graph convolutional networks with user preferences for course recommendation by Zhong Hua, Jianbai Yang, Weidong Ji

“…First, the user preference propagation module refines user preferences by exploring relational chains in the knowledge graph and dynamically adjusting attention to improve user representation. …”
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Multiscale Convolutional Fusion Network for Efficient Monaural Speech Separation by Rui Yang, Shanliang Pan

“…The MSCF-Net follows the encoder-mask estimation-decoder pipeline, where the mask estimation process consists of parameter-shared multiscale convolutional fusion (MSCF) modules. MSCF first employs dynamic convolution-based downsampling to enhance the multiscale feature representation. …”
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SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification by Shandong Yuan, Zili Zou, Han Zhou, Yun Ren, Jianping Wu, Kai Yan

“…Specifically, SECNN aggregates multi-scale convolutional features as distinct semantic channels and employs Squeeze-and-Excitation (SE) blocks to learn channel-wise attention weights, thereby enabling dynamic feature recalibration based on inter-channel dependencies. …”
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