Triplet Spatial Reconstruction Attention-Based Lightweight Ship Component Detection for Intelligent Manufacturing by Bocheng Feng, Zhenqiu Yao, Chuanpu Feng

“…Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. …”
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A Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model by Changhong Hou, Junchuan Yu, Daqing Ge, Liu Yang, Laidian Xi, Yunxuan Pang, Yi Wen

“…In addition, despite the transfer learning approach can transfer SAM feature extraction capability to the landslide segmentation task, but it will consume a lot of computational resources and training time. …”
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A Lightweight Method for Road Defect Detection in UAV Remote Sensing Images with Complex Backgrounds and Cross-Scale Fusion by Wenya Zhang, Xiang Li, Lina Wang, Danfei Zhang, Pengfei Lu, Lei Wang, Chuanxiang Cheng

“…Moreover, the CAA attention mechanism is employed to strengthen the model’s global feature extraction abilities; (2) a cross-scale feature fusion strategy known as GFPN is developed to tackle the problem of diverse target scales in road damage detection; (3) to reduce computational resource consumption, a lightweight detection head called EP-Detect has been specifically designed to decrease the model’s computational complexity and the number of parameters; and (4) the model’s localization capability for road damage targets is enhanced by integrating an optimized regression loss function called WiseIoUv3. …”
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A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children by Sourav Paul, Minhazur Rahman, Anutee Dolley, Kasturi Saikia, Chongtham Shyamsunder Singh, Arifullah Mohammed, Ghazala Muteeb, Rosy Sarmah, Nima D. Namsa

“…Correlation analysis is performed to check the feature-feature and feature-outcome collinearity. …”
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Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network by Mehdi POURSEIEDREZAEI, Ali LOGHMANI, Mehdi KESHMIRI

“…The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. …”
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Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model by Ahmed Abotaleb, Mohamed W. Fakhr, Mohamed Zaki

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A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models by Kai Zhao, Shufan Peng, Yujin Li, Tianliang Lu

“…Despite significant advancements in deep learning, challenges such as feature extraction, object occlusion, and model complexity remain. …”
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Surface morphology segmentation and evaluation of diamond lapping pad based on improved Mask R-CNN by Wenlong SUO, Yanfen LIN, Congfu FANG

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Mixture of prompts learning for vision-language models by Yu Du, Yu Du, Yu Du, Yu Du, Yu Du, Tong Niu, Tong Niu, Tong Niu, Tong Niu, Tong Niu, Rong Zhao, Rong Zhao, Rong Zhao, Rong Zhao, Rong Zhao

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A Fault Diagnosis Framework for Pressurized Water Reactor Nuclear Power Plants Based on an Improved Deep Subdomain Adaptation Network by Zhaohui Liu, Enhong Hu, Hua Liu

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Enhancing Digital Twin Fidelity Through Low-Discrepancy Sequence and Hilbert Curve-Driven Point Cloud Down-Sampling by Yuening Ma, Liang Guo, Min Li

“…We propose a novel down-sampling approach that combines Low-Discrepancy Sequences (LDS) with Hilbert curve ordering to create a method that preserves both global distribution characteristics and local geometric features. Unlike traditional methods that impose uniform density or rely on computationally intensive feature detection, our LDS-Hilbert approach leverages the complementary mathematical properties of Low-Discrepancy Sequences and space-filling curves to achieve balanced sampling that respects the original density distribution while ensuring comprehensive coverage. …”
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Reduced Noise Effect in Nonlinear Model Estimation Using Multiscale Representation by Mohamed N. Nounou, Hazem N. Nounou

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Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory. by Isabel Barradas, Reinhard Tschiesner, Angelika Peer

“…In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity. …”
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Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…It reduces the model’s parameters and computations whilst preserving accuracy, thereby achieving a lightweight model. …”
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RSA modulus length regression prediction based on the Run Test and machine learning in the ciphertext-only scenarios by Ke Yuan, Chenmeng Zhao, Longwei Yang, Hanlin Sun, Sufang Zhou, Chunfu Jia

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Exploring UAV Networking From the Terrain Information Completeness Perspective: A Tutorial by Zhengying Lou, Ruibo Wang, Baha Eddine Youcef Belmekki, Mustafa A. Kishk, Mohamed-Slim Alouini

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Fingerprint-based Wi-Fi indoor localization using map and inertial sensors by Xingwang Wang, Xiaohui Wei, Yuanyuan Liu, Kun Yang, Xuan Du

“…Second, we design a Wi-Fi signal propagation-based cluster algorithm to reduce the amount of computation. The paper gives a parameter called reliability to overcome the skewing of inertial sensors. …”
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EFCNet for small object detection in remote sensing images by Yutong Wang, Zhensong Li, Shiliang Zhu, Xiaotan Wei

“…Experiments conducted on the DOTA and DIOR datasets demonstrate that our model achieves an average precision mean of 75.9% and 80.5%, respectively, with the model’s parameters and computational requirements amounting to 13.4M and 30.2 GFLOPs, respectively. …”
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A Novel Attention-Guided Enhanced U-Net With Hybrid Edge-Preserving Structural Loss for Low-Dose CT Image Denoising by Muhammad Zubair, Helmi Md Rais, Talal Alazemi

“…However, it significantly reduces the quality of LDCT images and introduces noise and artifacts, degrading the diagnostic accuracy of the Computer Aided Diagnostic (CAD) system. This study presents a novel U-Net architecture, featuring several key enhancements. …”
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Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach by Eda Sena Erdol, Hakan Erdol, Beste Ustubioglu, Guzin Ulutas, Iraklis Symeonidis

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