A Method for Predicting Trajectories of Concealed Targets via a Hybrid Decomposition and State Prediction Framework by Zhengpeng Yang, Jiyan Yu, Miao Liu, Tongxing Peng, Huaiyan Wang

“…The RBMO further refines critical parameters within the ISVMD-ELM pipeline, ensuring adaptability and computational efficiency across diverse scenarios. …”
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LEAF-YOLO: Lightweight Edge-Real-Time Small Object Detection on Aerial Imagery by Van Quang Nghiem, Huy Hoang Nguyen, Minh Son Hoang

“…LEAF-YOLO-N achieves 21.9% AP.50:.95 and 39.7% AP.50 with only 1.2M parameters. LEAF-YOLO achieves 28.2% AP.50:.95 and 48.3% AP.50 with 4.28M parameters. …”
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Dense skip-attention for convolutional networks by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

“…Notably, it achieves these improvements without significantly increasing model parameters or computational cost, maintaining minimal impact on both aspects.…”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

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Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5 by Zhixu Wang, Guangyu Hou, Zhihui Xin, Guisheng Liao, Penghui Huang, Yonghang Tai

“…Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the neck part is replaced with Ghost convolution. …”
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An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via 18F-FDG PET/CT: a multicenter study by Xinyu Zhu, Denglu Lu, Yang Wu, Yanqi Lu, Liang He, Yanyun Deng, Xingyu Mu, Wei Fu

“…We aimed to develop and validate an interpretable machine learning model that integrates clinical data, 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) parameters, radiomic features, and deep learning features to predict BMI in lymphoma patients. …”
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Multimodal Prompt-Guided Bidirectional Fusion for Referring Remote Sensing Image Segmentation by Yingjie Li, Weiqi Jin, Su Qiu, Qiyang Sun

“…Multimodal feature alignment is a key challenge in referring remote sensing image segmentation (RRSIS). …”
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A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling by Enze Zhang, Yan Li, Haifeng Lin, Min Xia

“…In this context, technology based on deep learning has made substantial breakthroughs in change-detection performance by automatically extracting high-level feature representations of the data. However, although the existing deep-learning models improve the detection accuracy through end-to-end learning, their high parameter count and computational inefficiency hinder suitability for real-time monitoring and edge device deployment. …”
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CMCD: A Consistency Model-Based Change Detection Method for Remote Sensing Images by Xiongjie Li, Weiying Xie, Jiaqing Zhang, Yunsong Li

“…Furthermore, these methods utilize diffusion networks to extract key features from dual-temporal remote images and generate change maps, yet they often overlook the model's parameter size and the time cost associated with iterative sampling. …”
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Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen, Qinghua Liu

“…It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. In addition, a Global Attention Mechanism (GAM) is inserted between the backbone and the feature fusion layers to enrich semantic representations and improve the detection of occluded fruits. …”
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Dynamic convolutional model based on distribution-collaboration strategy for remote sensing scene classification by Chenjun Xu, Jingqian Shu, Zhenghan Wang, Jialin Wang

“…Secondly, an adaptive enhanced attention mechanism based on the Lie Group feature covariance matrix is designed to aggregate the essential attribute feature (EAF) of HRRSI, which can effectively deal with HRRSI without increasing the computational complexity and delay of the model with the increase of HRRSI resolution. …”
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Detection algorithm for wearing safety helmet under mine based on improved YOLOv5s by Yuanbin WANG, Sixiong WEI, Huaying WU, Yu DUAN, Meng LIU

“…Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed. Due to the computer system, the global context information of the image is easily lost when the convolutional neural network extracts the features, resulting in poor detection effect of the downhole small target safety helmet. …”
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A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering by Aryan Sharma, Tanmay Khade, Shashank Mouli Satapathy

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Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement by Junfeng Wang, Shenghui Huang, Zhanqiang Huo, Shan Zhao, Yingxu Qiao

“…DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6 $$\%$$ and SSIM by 3.6 $$\%$$ compared to FLW-Net, reduced parameters by 98.78 $$\%$$ , and improved computational speed by 52.64 $$\%$$ compared to the classical KIND.…”
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Optimizing Monkeypox Lesions Detection With a Lightweight Hybrid Model by Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Abduljabbar S. Ba Mahel, Ahmed Aziz, Mashael M. Khayyat, Ammar Muthanna

“…Grad-CAM, saliency maps, and other visualization techniques help improve clinical trust by highlighting relevant regions in input images, emphasizing key features of the lesions. The proposed model is lightweight with only 630,204 parameters, making it <inline-formula> <tex-math notation="LaTeX">$92.3\times $ </tex-math></inline-formula> smaller than ResNet152V2 (58,152,004 parameters) and <inline-formula> <tex-math notation="LaTeX">$28.7\times $ </tex-math></inline-formula> smaller than DenseNet201 (18,100,612 parameters). …”
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Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. by Glen J Weiss, Balaji Ganeshan, Kenneth A Miles, David H Campbell, Philip Y Cheung, Samuel Frank, Ronald L Korn

“…<h4>Background</h4>Non-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. …”
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Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease by Francesca Lussana, Ettore Lanzarone, Giulia Villa, Alfonso Mastropietro, Anna Caroli, Elisa Scalco

“…Additionally, lower-order features, including those computed from histograms and co-occurrence matrices, demonstrate higher reproducibility than other texture features.…”
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Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction by Jian Zhu, Qian Li, Shi Ying

“…This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. …”
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Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach by Aqeel Abdulazeez Mohammed, Ahlam Hanoon Al-sudani, Alaa M. Abdul-Hadi, Almuntadher Alwhelat, Basheera M. Mahmmod, Sadiq H. Abdulhussain, Muntadher Alsabah, Abir Hussain

“…Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. …”
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