EDT-MCFEF: a multi-channel feature fusion model for emergency department triage of medical texts by Tao Lin, Shiming Yi

“…Moreover, the model employs a convolutional neural network (CNN) and a multi-headed attention (MHA) mechanism to extract text features from multiple channels, effectively capturing both local and global features. …”
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Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature by Bosen Peng, Jiancheng Mu, Feng Xu, Wanyue Guo, Chuhuan Sun, Wei Fan

“…“ConclusionOur bibliometric analysis outlines the dynamic evolution and structural relationships within the AI and ophthalmology field. …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…The DySample adaptive upsampling structure is employed to dynamically adjust sampling point locations, thereby enhancing the detection capability for objects at different scales. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…A novel application of RL for dynamic feature refinement and adjustment based on reward functions was employed to optimize the detection capabilities of the model. …”
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Plug-and-Play Self-Supervised Denoising for Pulmonary Perfusion MRI by Changyu Sun, Yu Wang, Cody Thornburgh, Ai-Ling Lin, Kun Qing, John P. Mugler, Talissa A. Altes

“…Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. …”
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An Improved Backbone Fusion Neural Network for Orchard Extraction by Baiyu Dong, Ziqi Wang, Chongzhi Chen, Ke Wang, Jing Zhang

“…Semantic segmentation deep learning models utilizing convolutional neural networks (CNNs) or vision transformers have become the cornerstone for such tasks. …”
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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity by Abdulrahman Alzahrani, Patty Kostkova, Hamoud Alshammari, Safa Habibullah, Ahmed Alzahrani

“…The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. …”
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Azimuth-Guided Feature Embedding Network With Dual Inference Mechanism for Few-Shot SAR Target Recognition by Yan Peng, Xuelian Yu, Haohao Ren, Lei Miao, Lin Zou, Yun Zhou

“…The proposed DFEN is capable of dynamically extracting the discriminative features of the target according to the input image under the guidance of azimuth-parameterized convolutional kernels. …”
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StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training by Ziqi Yang, Yiran Liao, Ziao Chen, Zhenzhen Lin, Wenyuan Huang, Yanxi Liu, Yuling Liu, Yamin Fan, Jie Xu, Lijia Xu, Jiong Mu

“…Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…Specifically, most existing studies use a convolutional neural network that only captures the local context of an image hindering it from learning the global context of an image. …”
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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. …”
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Construction and application of foundational models for intelligent processing of microseismic events in mines by Anye CAO, Maotao LI, Xu YANG, Yao YANG, Sen LI, Yaoqi LIU, Changbin WANG

“…This technological breakthrough establishes a robust framework for intelligent monitoring and precise early warning of mine dynamic disasters, effectively overcoming the limitations of traditional methods in complex geological environments.…”
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STATISTICAL APPROACH TO EVALUATE HELICOPTER COMPETITIVENESS AT THE EXTERNAL DESIGN PHASE AND WHILE MAKING A DECISION TO PURCHASE A HELICOPTER by V. T. Bobronnikov, I. Yu. Nikulina

“…The methodology of helicopter competitiveness evaluation based on statistical data on volume of sales dynamics in money terms and performance of helicopter modifications has been developed. …”
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Research on anti-occlusion tracking method for underground mine personnel based on adaptive link optimization by LU Yang, DONG Lihong, YE Ou

“…Additionally, after performing time-domain block processing on the trajectory pair input, a channel prior convolutional attention mechanism was added to enhance the time-domain representation capability. …”
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Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach by Shu Xu, Tao Wang, Zenghui Ding, Yu Wang, Tongsheng Wan, Dezhang Xu, Xianjun Yang, Ting Sun, Meng Li

“…The proposed method combines time-frequency domain analysis through continuous wavelet transform (CWT) with a hybrid architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (Bi-LSTM), and a one-dimensional convolutional residual network (1D Conv ResNet). This integration enhances feature extraction, noise suppression, and temporal dependency modeling, particularly for non-stationary and nonlinear signals in dynamic environments. …”
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A Human-Centric, Uncertainty-Aware Event-Fused AI Network for Robust Face Recognition in Adverse Conditions by Akmalbek Abdusalomov, Sabina Umirzakova, Elbek Boymatov, Dilnoza Zaripova, Shukhrat Kamalov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, Taeg Keun Whangbo

“…A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. …”
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A Welding Defect Detection Model Based on Hybrid-Enhanced Multi-Granularity Spatiotemporal Representation Learning by Chenbo Shi, Shaojia Yan, Lei Wang, Changsheng Zhu, Yue Yu, Xiangteng Zang, Aiping Liu, Chun Zhang, Xiaobing Feng

“…A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. …”
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FDC-TA-DSN Ship Classification Model and Dataset Construction Based on Complex-Valued SAR by Gui Gao, Yucong He, Jinghao Zhao, Sijie Li, Meixiang Wang, Gang Yang, Xi Zhang

“…To solve the above problems, a complex-valued SAR deep learning model, FDC-TA-DSN, based on four-dimensional dynamic convolution (FDC) and triple attention (TA) mechanism, is proposed. …”
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HorGait: Advancing Gait Recognition With Efficient High-Order Spatial Interactions in LiDAR Point Clouds by Jiaxing Hao, Yanxi Wang, Zhigang Chang, Hongmin Gao, Zihao Cheng, Chen Wu, Xin Zhao, Peiye Fang, Rachmat Muwardi

“…Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. …”
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DCP-YOLOv7x: improved pest detection method for low-quality cotton image by Yukun Ma, Yajun Wei, Minsheng Ma, Zhilong Ning, Minghui Qiao, Uchechukwu Awada

“…In addition, the model detection head part is replaced with a DyHead (Dynamic Head) structure, which dynamically fuses the features at different scales by introducing dynamic convolution and multi-head attention mechanism to enhance the model's ability to cope with the problem of target morphology and location variability.ResultsThe model was fine-tuned and tested on the Exdark and Dk-CottonInsect datasets. …”
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