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  1. 921

    Strain promoted azide alkyne cycloaddition, an efficient surface functionalization strategy for microRNA magnetic separation by Djamila Kechkeche, Sirine El Mousli, Claire Poujouly, Emilie Secret, Vincent Dupuis, Isabelle Le Potier, Marie-Emmanuelle Goriot, Julien Siracusa, Sébastien Banzet, Jean Gamby, Jean-Michel Siaugue

    Published 2025-01-01
    “…In an emergency context, the detection of microRNA (miRNA), which are potential biomarkers of cardiovascular and muscular pathologies is hampered by the time required for the various steps involved: treatment of the patient sample, reverse transcription (RT), and finally polymerase chain reaction (PCR). …”
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  2. 922

    A Comprehensive Review of Advanced Sensor Technologies for Fire Detection with a Focus on Gasistor-Based Sensors by Mohsin Ali, Ibtisam Ahmad, Ik Geun, Syed Ameer Hamza, Umar Ijaz, Yuseong Jang, Jahoon Koo, Young-Gab Kim, Hee-Dong Kim

    Published 2025-06-01
    “…Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. …”
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    Article
  3. 923

    Efficient Rescue of Retinal Degeneration in Pde6a Mice by Engineered Base Editing and Prime Editing by Zhiquan Liu, Siyu Chen, Alexander E. Davis, Chien‐Hui Lo, Qing Wang, Tingting Li, Ke Ning, Qi Zhang, Jingyu Zhao, Sui Wang, Yang Sun

    Published 2024-11-01
    “…Notably, the optimal PE system, delivered via dual adeno‐associated virus (AAV), precisely corrects the pathogenic mutation with average 9.4% efficiency, with no detectable bystander editing. This correction restores PDE6A protein expression, preserved photoreceptors, and rescued retinal function in Pde6a mice. …”
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    Article
  4. 924

    Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model by Sofía Rajesh, K. S. Jinesh Babu, M. Chengathir Selvi, M. Chellapandian

    Published 2024-10-01
    “…YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. …”
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  5. 925
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  7. 927

    A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles by Duc Tai Nguyen, Phuoc Bao Long Do, Doan Dang Khoa Nguyen, Wei-Chih Lin

    Published 2025-08-01
    “…Notably, the proposed model outperforms the previous detection models, offering high accuracy while optimizing computational efficiency. …”
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  8. 928

    YOLO-Wheat: A More Accurate Real-Time Detection Algorithm for Wheat Pests by Yongkang Liu, Qinghao Wang, Qi Zheng, Yong Liu

    Published 2024-12-01
    “…Wheat pests exhibit considerable diversity and are often found in complex environmental contexts. Intraspecies variation among wheat pests can be substantial, while differences between species may be minimal, making accurate pest detection a difficult task. …”
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  9. 929

    Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11 by Lei Zhang, Zhipeng Sun, Hongjing Tao, Meng Wang, Weixun Yi

    Published 2024-12-01
    “…In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. …”
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  10. 930

    S₂Head: Small-Scale Human Head Detection Algorithm by Improved YOLOv8n Architecture by Yuteng Sui, Xinghua Shan, Linlin Dai, Hui Jing, Bo Li, Jianjun Ma

    Published 2025-01-01
    “…Existing head detection algorithms face challenges in accurately detecting small and densely packed head targets, resulting in lower detection accuracy in complex scenarios. …”
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  11. 931
  12. 932

    Point-Level Fusion and Channel Attention for 3D Object Detection in Autonomous Driving by Juntao Shen, Zheng Fang, Jin Huang

    Published 2025-02-01
    “…PointPillars transforms point cloud data into a two-dimensional pseudo-image and employs a 2D CNN for efficient and precise detection. Nevertheless, this approach encounters two primary challenges: (1) the sparsity and disorganization of raw point clouds hinder the model’s capacity to capture local features, thus impacting detection accuracy; and (2) existing models struggle to detect small objects within complex environments, particularly regarding orientation estimation. …”
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  13. 933

    Fine-grained detection and segmentation of civilian aircraft in satellite imagery using YOLOv8 by Ramesh Kumar Panneerselvam, Sarada Bandi, Sree Datta Siva Charan Doddapaneni

    Published 2025-06-01
    “…This paper presents that by developing an efficient YOLOv8-based model for aircraft detection, classification, and segmentation within the FAIR1M-2.0 dataset. …”
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  14. 934

    Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments by Qifeng Yu, Yudong Han, Xinjia Gao, Wuguang Lin, Yi Han

    Published 2024-10-01
    “…YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. …”
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  15. 935

    Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

    Published 2024-11-01
    “…However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. …”
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  16. 936

    An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition by Shixin Huo, Na Duan, Zhizheng Xu

    Published 2024-12-01
    “…Abstract Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. …”
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  17. 937

    LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

    Published 2025-08-01
    “…These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. …”
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  18. 938

    Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection by علی محقر, کارو لوکس, ، فرید حسینی, آصف علی منشی

    Published 2009-02-01
    “…It also explains how BI can be effective technology in industries like banking to overcome critical issue like fraud discovery and detection. The methodology, based on BI, in the form of model named BI model for fraud discovery and detection has been devised and suggested at the end of paper along with it its details for overcoming the above mentioned issue in more effective and efficient manner.…”
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  19. 939

    Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks by Wasim Khan, Afsaruddin Mohd, Mohammad Suaib, Mohammad Ishrat, Anwar Ahamed Shaikh, Syed Mohd Faisal

    Published 2025-06-01
    “…This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. …”
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  20. 940

    Enhanced YOLOv5s Model for Improved Multi-Sized Object Detection in Road Scenes by Sangavi Sivanandham, Dharanibai Gunaseelan

    Published 2025-01-01
    “…Detecting objects in complex driving environments is crucial for autonomous vehicles to navigate safely. …”
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