LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection by Dibin Zhou, Honggang Xu, Wenhao Liu, Fuchang Liu

“…Second, we optimized the computational load of the backbone network, effectively reducing the overall scale of the model. …”
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Hyperelastic behavior of cable gland silicon shroud: finite element analysis by Ganesh Bhoye, Ishant Jain

“…These findings highlight the silicon shroud as a superior alternative to conventional polyvinyl chloride shrouds, offering enhanced flexibility, reduced weight, and improved environmental resistance. The hyperelastic material model developed in this study provides a robust tool for predicting the shroud’s behavior under varied loading conditions, enabling optimized designs and ensuring long-term performance.…”
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Uncertainty evaluation of surface profile measurement error based on adaptive sparse grid polynomial chaos expansion by Ke Zhang, Xinya Zheng, Ruiyu Zhang

“…First, the surface profile error of the car door outer surface is initially evaluated using a NURBS surface fitting and segmentation approximation algorithm. Next, based on the error evaluation results, a polynomial chaos expansion model for the surface profile error is established. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…Finally, the accuracy and efficiency of the classification model were improved by optimizing the combination of base learners. …”
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Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method by Yang CUI, Zhenghong CHEN, Peihua XU

“…Firstly, the machine learning-based Bisecting K-Means(BKM) clustering algorithm is used to reasonably divide the wind farms and PV stations in the region into clusters; Secondly, based on the correlation between of the historical power data of each power station and the total historical power data in the region, a representative power station is selected for each region; Thirdly, after optimizing and correcting the NWP(numerical weather prediction) model of each representative power station, a short-term power prediction framework model is established using BP neural network based on the cluster division of wind farms and PV power plants. …”
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Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning by Anatolii Borzilov, Alexey Skrynnik, Aleksandr Panov

“…We aim to optimize a deep MARL algorithm with minimal modifications to the well-known QMIX approach. …”
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Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees by Cheng Shen, Yuewei Liu

“…The former evaluates the distribution that discriminates defects from all samples based on probability. The detection model is improved by retraining with calibration data that is assumed to be independent and identically distributed (i.i.d) with the test data. …”
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Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios by Zhe Li, Zhenhao Jiang, Xianyou Pan

“…Second, we propose a comprehensive metric model based on multimachine learning algorithms (CMM-MLA) to select the best-derived dataset with the optimal balance ratio and feature combination. …”
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High-Resolution Computed Tomography of Single Breast Cancer Microcalcifications in Vivo by Kazumasa Inoue, Fangbing Liu, Jack Hoppin, Elaine P. Lunsford, Christian Lackas, Jacob Hesterman, Robert E. Lenkinski, Hirofumi Fujii, John V. Frangioni

“…At an optimal detector sampling frequency, the ECBA provided a 28 μm (21%) FWHM improvement in resolution over the FCBA. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…Finally, the deep belief network (DBN) model is employed for HAR. To exhibit the improved performance of the existing IDLTHAR-PDST model, a comprehensive simulation study is accomplished. …”
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Short-Term Scheduling of Pump Station-integrated Cascade Hybrid Pumped Storage Systems by QIAN Jun, TAN Qiaofeng, WEN Xin, ZHENG Mingfen, SHEN Yanqing

“…The traditional step-by-step optimization algorithm was improved to solve this model. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…Existing methods, including post-processing optimization, specific model based improvements, and body part feature based methods, have limitations such as inaccurate handling of heavily occluded positive samples, high computational complexity, and susceptible to background noise. …”
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Maximum Power Extraction in Photovoltaic Systems Using High-Performance Adaptive Control Approach by Ahmed Ouaret, Hocine Lehouche, Adel Oubelaid, Arvind Yadav, Boubekeur Mendil, Ievgen Zaitsev

“…These outcomes underscore the notable performance enhancements facilitated by the proposed algorithm. In conclusion, the developed control algorithm offers superior performance in optimizing power extraction and maintaining precise set point tracking for PV systems, leading to improved system efficiency and increased profitability.…”
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Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response by Elissaios Sarmas, Afroditi Fragkiadaki, Vangelis Marinakis

“…Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. …”
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Multi-Underwater Target Interception Strategy Based on Deep Reinforcement Learning by Wenhao GAN, Yunfei PENG, Lei QIAO

“…Next, the multi-agent proximal policy optimization algorithm was used to construct a scalable state and action space and design a compound reward function, enhancing interception efficiency and cooperation of AUVs. …”
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Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk by Changxiao Zhao, Wei Zhang, Fangzheng Dong, Jun Dai, Lei Dong

“…The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…AI-driven models utilize diverse datasets—including electronic health records, imaging, and biomarkers—to improve clinical decision-making. …”
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Tire-road friction estimation and traction control strategy for motorized electric vehicle. by Li-Qiang Jin, Mingze Ling, Weiqiang Yue

“…Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. …”
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A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices by Hanwen Zhang, Myun Kim, Baitong Li, Yanping Lu

“…The model employs a generative adversarial network (GAN) as the core algorithm, optimizing its generation and recognition modules by decomposing the global loss function and incorporating a feature pyramid, thereby solving the balance challenge in GAN training. …”
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Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area by Yunhao Han, Bin Wang, Jingyi Yang, Fang Yin, Linsen He

“…The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. …”
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