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  1. 481
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  3. 483

    Data selection strategies for minimizing measurement time in materials characterization by Alexander Liehr, Kristina Dingel, Daniel Kottke, Sebastian Degener, David Meier, Bernhard Sick, Thomas Niendorf

    Published 2025-04-01
    “…Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. …”
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  4. 484
  5. 485

    Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

    Published 2024-10-01
    “…This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. …”
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  6. 486

    AIN-Based Action Selection Mechanism for Soccer Robot Systems by Yin-Tien Wang, Zhi-Jun You, Chia-Hsing Chen

    Published 2009-01-01
    “…This AIN-based action-select provides an efficient and robust algorithm for robot role selection. …”
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  7. 487

    Bio-Inspired Swarm Confrontation Algorithm for Complex Hilly Terrains by He Cai, Fu Ma, Ruifeng Ni, Weiyuan Xu, Huanli Gao

    Published 2025-04-01
    “…This paper explores a bio-inspired swarm confrontation algorithm specifically designed for complex hilly terrains in the context of electronic games. …”
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  8. 488

    Geological characteristics and key technologies for exploration and development of the Yanchuannan coalbed methane field, Ordos Basin by Xipeng HE, Cui XIAO, Yuqiao GAO, Xin LI, Tao GUO, Xiao CAI

    Published 2025-03-01
    “…MethodsFocusing on the exploration and exploitation practice of middle-deep, thin coal seams in the Yanchuannan CBM field within the Ordos Basin, this study systematically analyzed the geological characteristics of the CBM field, summarized the primary factors controlling CBM enrichment and high productivity, and established a series of geology-engineering integrated technologies for efficient exploration and exploitation of middle-deep, thin coal seams. …”
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  9. 489

    SMEA-YOLOv8n: A Sheep Facial Expression Recognition Method Based on an Improved YOLOv8n Model by Wenbo Yu, Xiang Yang, Yongqi Liu, Chuanzhong Xuan, Ruoya Xie, Chuanjiu Wang

    Published 2024-11-01
    “…In response to challenges such as missed detections, false positives, and low recognition accuracy in sheep facial expression recognition, this paper introduces an enhanced algorithm based on YOLOv8n, referred to as SimAM-MobileViTAttention-EfficiCIoU-AA2_SPPF-YOLOv8n (SMEA-YOLOv8n). Firstly, the proposed method integrates the parameter-free Similarity-Aware Attention Mechanism (SimAM) and MobileViTAttention modules into the CSP Bottleneck with 2 Convolutions(C2f) module of the neck network, aiming to enhance the model’s feature representation and fusion capabilities in complex environments while mitigating the interference of irrelevant background features. …”
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  10. 490

    Firefly Algorithm based Feature Selection for Arabic Text Classification by Souad Larabi Marie-Sainte, Nada Alalyani

    Published 2020-03-01
    “…Most of feature selection based text classification methods rely on building a term-frequency inverse-document frequency feature vector which is not usually efficient. …”
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  11. 491

    Quality Improvement in Critical Care: Selection and Development of Quality Indicators by Carla A. Chrusch, Claudio M. Martin, The Quality Improvement in Critical Care Project

    Published 2016-01-01
    “…Six domains of ICU function were identified: safe, timely, efficient, effective, patient/family satisfaction, and staff work life. …”
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  12. 492

    Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

    Published 2025-03-01
    “…Furthermore, the proposed MSF-CrackNet also significantly reduces computational complexity, with only 2.39 million parameters and 8.58 GFLOPs, making it a practical and efficient solution for real-world crack detection tasks, especially in scenarios with limited computational resources.…”
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  13. 493

    Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning by Łukasz Jeleń, Izabela Stankiewicz-Antosz, Maria Chosia, Michał Jeleń

    Published 2025-01-01
    “…The results demonstrate that optimized feature sets, paired with SVM classifiers, achieve classification performance comparable to those of CNNs while significantly reducing computational complexity. This finding underscores the potential of feature reduction techniques in creating efficient diagnostic frameworks. …”
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  14. 494

    Investigation and selection of initial materials as possible sources for obtaining sorbents by I. A. Таgaev, S. U. Tursunova, L. S. Andriyko

    Published 2018-11-01
    “… The information about selection of initial materials based on bentonite, brown coal and technical soot for obtaining new sorbents is presented in this article. …”
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  15. 495

    The analysis of landscape design and plant selection under deep learning by Lian Li, JiYon Lee

    Published 2025-08-01
    “…Abstract This paper explores the application of deep learning (DL) techniques in landscape design and plant selection, aiming to enhance design efficiency and quality through automated plant leaf image recognition (PLIR). …”
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  16. 496

    EIM: A Novel Evolutionary Influence Maximizer in Complex Networks by Vahideh Sahargahi, Vahid Majidnezhad, Saeid Taghavi Afshord, Yasser Jafari

    Published 2025-01-01
    “…This article introduces a double-chromosome evolutionary algorithm to tackle this challenge efficiently. This method introduces a smart operator for stochastic selection based on the node degree to initialize the primary solutions. …”
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  17. 497

    Selective electrocatalytic nitrate reduction to dinitrogen: Mechanisms, catalysts, and challenges by Jiayu Chen, Anni Wu, Yongfeng Xie, Yumin Zhang, Hu Zheng, Wei Teng

    Published 2025-08-01
    “…While extensive research has focused on ammonia production, selective N2 production remains challenging due to complex pathways, limitations in detection, and the difficulty of controlling reaction on the electrocatalysts. …”
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  18. 498

    Enhanced multi-objective Evolutionary Mating Algorithm with improved crowding distance and Levy flight for optimizing comfort index and energy consumption in smart buildings by Muhammad Naim Bin Nordin, Mohd Herwan Sulaiman, Nor Farizan Zakaria, Zuriani Mustaffa

    Published 2025-06-01
    “…While current Evolutionary Mating Algorithms (EMA) primarily focus on single-objective optimization and rely on weighted functions for handling multiple objectives, such approaches prove impractical for the complex trade-offs between comfort index and energy efficiency. …”
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  19. 499

    Complex interspecific and intersexual patterns of wing design in Heliconius butterflies by Luis Mendoza-Cuenca

    Published 2025-08-01
    “…In butterflies, interspecific and intersexual variation in wing design morphology modifies aerodynamic efficiency and individuals’ flight costs. Wing design involves traits like wing shape and size, which determine flight performance and behavioral ability, which determine individuals’ flight performance and behavioural capacity under their ecological and reproductive requirements. …”
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  20. 500

    A YOLO-Based Method for Head Detection in Complex Scenes by Ming Xie, Xiaobing Yang, Boxu Li, Yingjie Fan

    Published 2024-11-01
    “…This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training.…”
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