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Showing 61 - 72 results of 72 for search '(((( complement past algorithm ) OR ( element method algorithm ))) OR ( agent modeling algorithm ))', query time: 0.08s Refine Results
  1. 61

    An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System by Alkhatib, Osama

    Published 2019
    “…Historical data of Dubai Toll Collection System is utilized to investigate multiple machine learning algorithms. Experiment is performed using Azure Machine Learning (ML) platform to test and assess the most efficient model that would predict the failure of system elements and predict the abnormality of the operation. …”
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  2. 62
  3. 63

    The role of Reinforcement Learning in software testing by Amr Abo-eleneen (17032284)

    Published 2023
    “…</p><h3>Results</h3><p dir="ltr">This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques.…”
  4. 64

    A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks by Sakib Mahmud (15302404)

    Published 2025
    “…We present a structured taxonomy covering value-based, policy-based, actor-critic, model-based, and advanced multi-agent and multi-objective approaches, and link algorithms to tasks such as dispatch, microgrid coordination, real-time pricing, load balancing, and demand–response. …”
  5. 65
  6. 66

    An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

    Published 2001
    “…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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  7. 67

    An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

    Published 2001
    “…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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  8. 68

    Approximate XML structure validation based on document–grammar tree similarity by Tekli, Joe

    Published 2015
    “…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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  9. 69

    Approximate XML structure validation technical report by Tekli, Joe

    Published 2014
    “…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?…”
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  10. 70

    The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions by Abdulmalik Alwarafy (17984104)

    Published 2022
    “…To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. …”
  11. 71
  12. 72

    Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

    Published 2023
    “…Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. …”
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