Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging by Chun Wang, Zejun Wang, Lijiao Chen, Weihao Liu, Xinghua Wang, Zhiyong Cao, Jinyan Zhao, Man Zou, Hongxu Li, Wenxia Yuan, Baijuan Wang

“…Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. …”
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DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer by Deepali, Neelam Goel, Padmavati Khandnor

“…Methods DeepOmicsSurv builds on the DeepSurv model, incorporating multi-head attention convolutional layers, dropout, pooling, and batch normalization to boost its strength and precision. …”
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BlindFG: Learning Contextual Fishing Trajectories for Unreported Fishing Gear Classification by Changha Lee, Chan-Hyun Youn

“…FCSom utilizes the completed trajectories within a 1D convolutional classification framework. By aggregating candidate predictions across multiple fishing type hypotheses, the system effectively discriminates among various fishing behaviors, even in the presence of partial AIS data. …”
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Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real... by Felix Adrian Tjokro Atmodjo, Kestrilia Rega Prilianti, Hendry Setiawan

“…Pada penelitian ini dikembangkan metode perbaikan citra dengan algoritma Partial Least Square Regression (PLSR) sebagai solusi atas permasalahan tersebut. …”
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Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes by Yufeng Xu, Yan Fu

“…At the same time, the GSEAM module and GSCConv were introduced into the neck network to compensate for the loss caused by differences in lighting levels by combining depth-wise separable convolution and residual connections, enabling it to extract effective features from visual data with poor illumination levels. …”
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Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies by Vladislav Liubčuk, Virginijus Radziukynas, Gediminas Kairaitis, Darius Naujokaitis

“…Poor PQ is a partial case of power system impact on society and the environment. …”
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Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network. by Chunhua Yang, Wenxia Yuan, Qiang Zhao, Zejun Wang, Bowu Song, Xianqiu Dong, Yuandong Xiao, Shihao Zhang, Baijuan Wang

“…The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution. …”
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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…C2f-GR achieves lightweight design while effectively capturing detailed features of multi-scale information by replacing partial convolutions in C2f with Ghost Modules. Additionally, RepConv is incorporated into C2f-GR to avoid the complexity of multi-branch structures and enhance gradient flow capability. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. …”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…Using a dataset comprising 136 chickpea varieties, the research compares the performance of several state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs), and compares the most effective model, CNN, against the traditional Partial Least Squares Regression (PLSR) method. …”
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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini

“…Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). …”
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Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents by Raimund BÜrger, Gerardo Chowell, Elvis GavilÁn, Pep Mulet, Luis M. Villada

“…In some cases the movement is not only directed by the gradient of a density (as in the standard diffusive case), but also by a non-local convolution of density values as proposed, in another context, in [R.M. …”
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HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method by Haiyan Tu, Zhengkun Qiu, Kang Yang, Xiaoyue Tan, Xiujuan Zheng

“…Additionally, the Reparameterized Network with Cross-Stage Partial Connections and Efficient Layer Aggregation Network (RepNCSPELAN4) module was incorporated into the detection head, boosting accuracy in detecting small-sized targets through multi-scale convolution and reparameterization techniques while accelerating inference speed. …”
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PPLA-Transformer: An Efficient Transformer for Defect Detection with Linear Attention Based on Pyramid Pooling by Xiaona Song, Yubo Tian, Haichao Liu, Lijun Wang, Jinxing Niu

“…Additionally, the incorporation of partial convolution into the model improves local feature extraction, further enhancing detection precision. …”
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Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO by Di Zhao, Yulin Cheng, Sizhe Mao

“…Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. …”
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Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…Furthermore, the DRBNCSPELAN (Dilated Reparam Block with Cross-Stage Partial and Efficient Layer Aggregation Networks) module is introduced to ensure efficient information flow, and a lightweight shared convolution (LSC) detection head is developed. …”
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Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…In the backbone, a Receptive-Field Attention Convolution (RFAConv) module is introduced to retain detailed features during the downsampling process. …”
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Disease Detection Algorithm for Tea Health Protection Based on Improved Real-Time Detection Transformer by Zhijie Lin, Zilong Zhu, Lingling Guo, Jingjing Chen, Jiyi Wu

“…Faster-LTNet employs partial convolution and hierarchical design to optimize computational resources, while the CG Attention Module enhances multi-head self-attention by introducing grouped feature inputs and cascading operations to reduce redundancy and increase attention diversity. …”
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SDNet: a lightweight ship detection network in remote sensing images by super-resolution enhancement and detail completion by Yu Tong, Jun Liu, Guixing Cao, Leyang Li, Yufei Wang

“…The main detector branch uses the adaptive cross-stage partial convolution (ACPC) module to form an efficient backbone. …”
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Energy-efficient and fast memristor-based serial multipliers applicable in image processing by Seyed Erfan Fatemieh, Bahareh Bagheralmoosavi, Mohammad Reza Reshadinezhad

“…The proposed multipliers have improved significantly compared to State-Of-the Art (SOA) by applying the proposed Partial Product Units (PPUs) and overlapping computational steps. …”
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