Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

“…Furthermore, CCE-YOLOv7 reduced the number of parameters by 2 million and lowered computational complexity by 5.7 GFLOPs, offering an efficient and lightweight model suitable for real-time clinical applications. …”
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A lightweight weed detection model for cotton fields based on an improved YOLOv8n by Jun Wang, Zhengyuan Qi, Yanlong Wang, Yanyang Liu

“…Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.…”
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Adaptive multilevel attention deeplabv3+ with heuristic based frame work for semantic segmentation of aerial images using improved golden jackal optimization algorithm by Anilkumar P, Venugopal P, Satheesh Kumar S, Jagannadha Naidu K

“…Multi-level attention unit has been included in the Atrous spatial pyramid pooling module in the encoder section of deeplabv3+ to bridge the semantic feature gap among encoders output. To put more weights on relevant features squeeze and excitation units has been included in the decoder section of deeplabv3+. …”
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Smoke Detection Transformer: An Improved Real-Time Detection Transformer Smoke Detection Model for Early Fire Warning by Baoshan Sun, Xin Cheng

“…Considering the limited computational resources of smoke detection devices, Enhanced Channel-wise Partial Convolution (ECPConv) is introduced to reduce the number of parameters and the amount of computation. …”
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A Heterogeneous Image Registration Model for an Apple Orchard by Dongfu Huang, Liqun Liu

“…Then, we used the Sinkhorn AutoDiff algorithm to iteratively optimize and solve the optimal transmission problem, achieving optimal matching between feature points. Finally, we carried out network pruning and compression operations to minimize parameters and computation cost while maintaining the model’s performance. …”
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Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods by Vinh‐Binh Truong, Analía Fernández Herrero, Kas Andrle, Victor Soltwisch, Philipp Hönicke

“…Abstract With advancements in the semiconductor industry, the complexity of three‐dimensional (3D) nanostructures becomes higher with continuously decreasing feature sizes. In order to monitor the processing steps, it is crucial to accurately determine the critical dimensions and composition of these nanostructures. …”
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Research on Correction Method of pin-by-pin Homogenization Environmental Effect of PWR Based on Machine Learning by LI Tianya1, 2, , LUO Qi3, , YAO Dong2, HE Caiyun1, CHAI Xiaoming2, CAI Yun2, ZHANG Bin2, ZHANG Hongbo2, LIAO Hongkuan1, DUAN Yongqiang1

“…By analyzing the machine learning model’s feature sensitivity, the combination features of pin material, location, surrounding assembly, energy spectrum, and leakage-related information were determined. …”

Research on PCB defect detection algorithm based on LPCB-YOLO by Haiyan Zhang, Haiyan Zhang, Yazhou Li, Yazhou Li, Dipu Md Sharid Kayes, Dipu Md Sharid Kayes, Zhaoyu Song, Zhaoyu Song, Yuanyuan Wang, Yuanyuan Wang

“…The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. …”
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A lightweight large receptive field network LrfSR for image super-resolution by Wanqin Wang, Shengbing Che, Wenxin Liu, Yangzhuo Tuo, Yafei Du, Zixuan Zhang

“…However, existing methods often suffer from issues such as large number of parameters, intensive computation, and high latency, which limit the application of deep convolutional neural networks on devices with low computational resources. …”
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A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition by Guohua Li, An Liu, Huajie Shen

“…Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. …”
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TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

“…Subsequently, a weight computation (WC) module, leveraging SSM and GAM, is developed to generate robust band weights by sequentially leveraging multi-scale LST features. …”
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OptWake-YOLO: a lightweight and efficient ship wake detection model based on optical remote sensing images by Runxi Qiu, Nan Bi, Chaoyue Yin

“…A Shared Lightweight Object Detection Head (SLODH) using parameter sharing and Group Normalization.ResultsExperiments on the SWIM dataset show OptWake-YOLO improves mAP50 by 1.5% (to 93.2%) and mAP50-95 by 2.9% (to 66.5%) compared to YOLOv11n, while reducing parameters by 40.7% (to 1.6M) and computation by 25.8% (to 4.9 GFLOPs), maintaining 303 FPS speed.DiscussionThe model demonstrates superior performance in complex maritime conditions through: RCEA's multi-branch feature extraction. …”
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A lightweight high-frequency mamba network for image super-resolution by Tao Wu, Wei Xu, Yajuan Wu

“…It can better incorporate local and global information and has linear complexity in the global feature extraction branch. Experiments on multiple benchmark datasets demonstrate that the network outforms recent SOTA methods in SISR while using fewer parameters. …”
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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
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An enhanced YOLOv8‐based bolt detection algorithm for transmission line by Guoxiang Hua, Huai Zhang, Chen Huang, Moji Pan, Jiyuan Yan, Haisen Zhao

“…Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. …”
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HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles by Xiaona Song, Bin Fan, Haichao Liu, Lijun Wang, Jinxing Niu

“…This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. …”
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Quantitative Ultrasound Texture Analysis of Breast Tumors: A Comparison of a Cart-Based and a Wireless Ultrasound Scanner by David Alberico, Lakshmanan Sannachi, Maria Lourdes Anzola Pena, Joyce Yip, Laurentius O. Osapoetra, Schontal Halstead, Daniel DiCenzo, Sonal Gandhi, Frances Wright, Michael Oelze, Gregory J. Czarnota

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Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate by Milad Jajarmizadeh, Sobri bin Harun, Shamsuddin Shahid, Shatirah Akib, Mohsen Salarpour

“…The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. …”
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Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments by Yi Wang, Xiang Li

“…First, we introduce the high-low frequency (HiLo) attention into the intra-scale feature interaction module to enhance the extraction of both high- and low-frequency features, reduce computational complexity, and improve detection performance. …”
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GCB‐YOLO: A Lightweight Algorithm for Wind Turbine Blade Defect Detection by Zhiming Zhang, Chaoyi Dong, Ze Wei, Xiaoyan Chen, Weidong Zan, Yao Xue

“…Initially, a GhostNet network was employed to replace a portion of the YOLOv5s backbone network responsible for feature extraction. This replacement serves to reduce the network's parameter size and computational load, thereby achieving compression of the feature extraction network. …”
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