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Showing 441 - 460 results of 2,295 for search '((\ source selection functions\ ) OR (( resourcesss OR resource) detection function\ ))', query time: 0.25s Refine Results
  1. 441

    Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung

    Published 2025-01-01
    “…VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. …”
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    Article
  2. 442

    Uncovering functional deterioration in the rhizosphere microbiome associated with post-green revolution wheat cultivars by Monique E. Smith, Vanessa N. Kavamura, David Hughes, Rodrigo Mendes, George Lund, Ian Clark, Tim H. Mauchline

    Published 2025-06-01
    “…Of the 113 functional genes that were differentially abundant between heritage and modern cultivars, 95% were depleted in modern cultivars and 65% of differentially abundant reads best mapped to genes involved in staurosporine biosynthesis (antibiotic product), plant cell wall degradation (microbial mediation of plant root architecture, overwintering energy source for microbes) and sphingolipid metabolism (signal bioactive molecules). …”
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  3. 443
  4. 444

    Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region by Beijing XIE, Heng LI, Zheng LUAN, Zhen LEI, Xiaoxu LI, Zhuo LI

    Published 2025-02-01
    “…Compared to various lightweight architectures and advanced detection models, this method demonstrates excellent accuracy, lower computational costs, and better real-time performance, providing a feasible coal mine pedestrian-vehicle detection method for resource-constrained coal mine scenarios, meeting the deployment requirements of coal mine video surveillance and enabling real-time alerts for intelligent inspection of coal mine pedestrian-vehicles.…”
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    Article
  5. 445

    Energy-Efficiency using Critical Nodes Detection Problem in Industrial Wireless Sensor Networks (IWSNs) by Karima MOULEY, Mohamed Amin TAHRAOUI, Abdelaziz KELLA

    Published 2025-03-01
    “…Experiments simulation validates our proposed approach, approving its efficiency in reducing significant energy consumption while preserving connectivity and functionality for industrial systems. Furthermore, the results highlight the potential of using critical node analysis to support sustainable and efficient operations in resource-constrained industrial environments. …”
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    Article
  6. 446

    Simple Single-Person Fall Detection Model Using 3D Pose Estimation Mechanisms by Jinmo Yang, R. Young Chul Kim

    Published 2024-01-01
    “…Although various technologies with wearables and vision systems that utilize artificial intelligence (AI) have been developed to detect falls, many AI models are complex and resource-intensive. …”
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  7. 447

    A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

    Published 2025-02-01
    “…Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. …”
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    Article
  8. 448

    Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

    Published 2024-01-01
    “…To address this problem, machine learning (ML) based intrusion detection systems (IDSs) have been proposed. However, existing IDSs suffer from low detection accuracy, limited real-time response, and high resource requirements. …”
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    Article
  9. 449

    F-OSFA: A Fog Level Generalizable Solution for Zero-Day DDOS Attacks Detection by Muhammad Rashid Minhas, Qaisar M. Shafi, Shoab Ahmed Khan, Tahir Ahmad, Subhan Ullah, Attaullah Buriro, Muhammad Azfar Yaqub

    Published 2025-01-01
    “…The third component is a signature-based resource usage analyzer to counter attacks mimicking normal traffic. …”
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    Article
  10. 450
  11. 451

    DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

    Published 2025-06-01
    “…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. Entropy and throughput metrics are employed as decision conditions at each sensing channel, enhancing uncertainty measurement and maintaining detection robustness under low Signal-to-Noise Ratio (SNR) conditions. …”
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  12. 452
  13. 453

    TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

    Published 2025-05-01
    “…Finally, the EIOU loss function is introduced to measure the overlap between the predicted box and the real box more accurately and improve the detection accuracy. …”
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  14. 454

    YOLOv8n-DDSW: an efficient fish target detection network for dense underwater scenes by Jinwang Yi, Wei Han, Fangfei Lai

    Published 2025-04-01
    “…Therefore, the YOLOv8n-DDSW fish target detection algorithm was proposed in this article to resolve the detection difficulties resulting from fish occlusion, deformation and detail loss in complex intensive aquaculture scenarios. (1) The C2f-deformable convolutional network (DCN) module is proposed to take the place of the C2f module in the YOLOv8n backbone to raise the detection accuracy of irregular fish targets. (2) The dual-pooling squeeze-and-excitation (DPSE) attention mechanism is put forward and integrated into the YOLOv8n neck network to reinforce the features of the visible parts of the occluded fish target. (3) Small detection is introduced to make the network more capable of sensing small targets and improving recall. (4) Wise intersection over union (IOU) rather than the original loss function is used for improving the bounding box regression performance of the network. …”
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  15. 455
  16. 456

    Securing Wireless Communications with Energy Harvesting and Multi-antenna Diversity by Nguyen Quang Sang, tran Cong Hung, tran Trung Duy, minh Tran, byung Seo Kim

    Published 2025-04-01
    “…A time-switching protocol enables the source node S to alternate between energy harvesting and secure data transmission. …”
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  17. 457
  18. 458

    Investigating the utilization of biomaterials and bioengineering methods in developing functional cardiac tissue for heart repair by Omar Elsaka

    Published 2024-12-01
    “…The primary purpose of the present research is to examine the use of biomaterials and bioengineering methods to create functioning cardiac tissue. Techniques for tissue maturation, scaffold design, cell sources, and biomaterial selection are important subjects. …”
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  19. 459

    Elucidating the role of compositional and processing variables in tailoring the technological functionalities of plant protein ingredients by Lorenzo Barozzi, Stella Plazzotta, Ada Nucci, Lara Manzocco

    Published 2025-01-01
    “…However, other extraction, purification, and drying methods can be properly combined, resulting in specific PP ingredient functionalities. Overall, this review highlights that, besides protein purity and source, knowledge of the processing history is required to select PP ingredients with desired functionalities.…”
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  20. 460

    Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. by Chisom Soremekun, Tafadzwa Machipisa, Opeyemi Soremekun, Fraser Pirie, Nashiru Oyekanmi, Ayesha A Motala, Tinashe Chikowore, Segun Fatumo

    Published 2023-01-01
    “…<h4>Conclusions</h4>Using multivariate GWAS method improves the power to detect novel genotype-phenotype associations for liver functions not found with the standard univariate GWAS in the same dataset.…”
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