يعرض 601 - 608 نتائج من 608 نتيجة بحث عن '(( elements method algorithm ) OR ((( data scheduling algorithm ) OR ( based models algorithm ))))*', وقت الاستعلام: 0.08s تنقيح النتائج
  1. 601

    Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks حسب Abbas, Nadine

    منشور في 2018
    "…We also propose efficient sub-optimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra dense networks. …"
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    article
  2. 602
  3. 603

    Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations حسب Md Mosarrof Hossen (21399056)

    منشور في 2025
    "…To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. Leveraging FL enables efficient model training across diverse end-user applications while preserving data privacy. …"
  4. 604

    Optimal Trajectory and Positioning of UAVs for Small Cell HetNets: Geometrical Analysis and Reinforcement Learning Approach حسب Mohammad Taghi Dabiri (16904658)

    منشور في 2023
    "…To this end, we first characterize a detailed three-dimensional (3D) modeling of the dynamic UAV-assisted HetNet, by taking into account the random positions of small cell base stations (SBSs), spatial angles between THz links, real antenna pattern, and UAV’s vibrations in the 3D space. …"
  5. 605

    Design and Analysis of Lightweight Authentication Protocol for Securing IoD حسب Saeed Ullah Jan (9079260)

    منشور في 2021
    "…We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. …"
  6. 606

    A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass حسب Uzma Nawaz (21980708)

    منشور في 2025
    "…By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.…"
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