MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. …”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…This module enhance low-level feature representation by fusing global and local context, which is particularly effective for detecting occluded pests in complex environments; Shape-IoU, which improves bounding box regression by accounting for target morphology, and Slide Loss, which addresses class imbalance by dynamically adjusting sample weighting during training were employed. …”
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VMKLA-UNet: vision Mamba with KAN linear attention U-Net by Chenhong Su, Xuegang Luo, Shiqing Li, Li Chen, Juan Wang

“…Abstract In the domain of medical image segmentation, while convolutional neural networks (CNNs) and Transformer-based architectures have attained notable success, they continue to face substantial challenges. …”
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A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system by Jieyun Bai, Xue Kang, Weishan Wang, Ziduo Yang, Weiguang Ou, Yuxin Huang, Yaosheng Lu

“…This could be highly beneficial for dynamically fusing data from different sources throughout the maternal and fetal health lifecycle, from pregnancy to delivery.…”
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SDA-Mask R-CNN: An Advanced Seabed Feature Extraction Network for UUV by Yao Xiao, Dongchen Dai, Hongjian Wang, Chengfeng Li, Shaozheng Song

“…First, we introduce a Structural Synergistic Group-Attention Residual Network (SSGAR-Net) that integrates group convolution with an enhanced convolutional block attention mechanism, complemented by a layer-skipping architecture for optimized information flow and redundancy verification for computational efficiency. …”
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Priority-aware task offloading for LEO satellite edge computing network: a multi-agent deep reinforcement learning-based approach by Juan Chen, Jie Zhong, Zongling Wu, Di Tian, Yujie Chen

“…These two agents interact in real-time, dynamically adjusting their strategies to adapt to environmental changes, effectively enhancing scheduling efficiency. …”
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Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach by Piyush Kumar Pareek, Prasath Alais Surendhar S, Ram Prasad, Govindaraj Ramkumar, Ekta Dixit, R. Subbiah, Saleh H. Salmen, Hesham S. Almoallim, S. S. Priya, S. Arockia Jayadhas

“…A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. …”
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RT-DETR-Smoke: A Real-Time Transformer for Forest Smoke Detection by Zhong Wang, Lanfang Lei, Tong Li, Xian Zu, Peibei Shi

“…First, we designed a high-efficiency hybrid encoder that combines convolutional and Transformer features, thus reducing computational cost while preserving crucial smoke details. …”
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GaitRGA: Gait Recognition Based on Relation-Aware Global Attention by Jinhang Liu, Yunfan Ke, Ting Zhou, Yan Qiu, Chunzhi Wang

“…Unlike traditional gait recognition methods that rely solely on local convolutions, we stack pairwise associations between each feature position in the gait silhouette and all other feature positions, along with the features themselves, using a shallow convolutional model to learn attention. …”
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Oriented R-CNN With Disentangled Representations for Product Packaging Detection by Jiangyi Pan, Jianjun Yang, Yinhao Liu, Yijie Lv

“…Furthermore, targets in varying backgrounds necessitate different receptive fields, which can be dynamically adjusted using different convolutional kernels. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

“…To balance these diverse objectives, we employ a dynamic weight averaging (DWA) mechanism, which adaptively adjusts the relative importance of each loss term based on its convergence behavior. …”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…Accurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. …”
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Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika by Yaosheng Han, Yunpeng Jin, Chunmei Li, Xiangjie Huang

“…The specific improvements are as follows: firstly, the Contextual Transformer module is introduced in YOLO v8 to improve the global modeling capability; secondly, the DRConv dynamic region-aware convolution is introduced in YOLO v8 to improve the convolutional representation capability; thirdly, the attention mechanism is incorporated in the backbone of YOLO v8 to enhance the feature extraction capability of the network capability. …”
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High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods by Baoqin Li, Pengfei Fan, Qixin Chen, Rong Li, Kaijun Lin

“…However, the power grid is dynamic, and some topologies or operation conditions change substantially. …”
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Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification by Bingbing Yu, Mingliang Zuo, Li Sui

“…Channel 1 integrates a bidirectional long short-term memory (BiLSTM) network with a Squeeze-and-Excitation (SE) ResNet attention module to dynamically emphasize critical feature channels. Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. …”
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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…To address these challenges, this paper proposes an innovative deep learning-based framework that integrates an ensemble of two pre-trained convolutional neural networks (CNNs), SqueezeNet and InceptionResNet-V2, combined with an improved Whale Optimization Algorithm (WOA) for feature selection. …”
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Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network by Yuxin Cong, Roxana Dev Omar Dev, Shamsulariffin Bin Samsudin, Kaihao Yu

“…The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). A prediction model is established based on the characteristics of sports behavior and psychological indices of well-being, such as psychological resilience, self-efficacy, and subjective well-being. …”
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Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing by Yajie He, Ningyi Zhang, Xinjin Ge, Siqi Li, Linfeng Yang, Minghao Kong, Yiping Guo, Chunli Lv

“…This study proposes a lightweight deep learning model incorporating a sparse parallel attention mechanism (SPAM) for passion fruit disease detection. Built upon a Convolutional Neural Network (CNN) backbone, the model integrates a dynamically selective attention mechanism to enhance detection performance in cases with complex backgrounds and multi-scale objects. …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…Second, the F-EIoU loss function introduces dynamic size-dependent penalty terms and difficulty-adaptive weighting factors, effectively balancing learning priorities between small targets and normal instances. …”
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Video Instance Segmentation Through Hierarchical Offset Compensation and Temporal Memory Update for UAV Aerial Images by Ying Huang, Yinhui Zhang, Zifen He, Yunnan Deng

“…Next, the temporal memory update (TMU) module is developed by employing convolutional long-short-term memory (ConvLSTM) between the current and adjacent frames to establish the temporal dynamic context correlation and update the current frame feature effectively. …”
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