Showing 61 - 79 results of 79 for search '(((( element method algorithm ) OR ( complement i algorithm ))) OR ( level coding algorithm ))', query time: 0.10s Refine Results
  1. 61

    Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review by Loay A. Salman (14150322)

    Published 2023
    “…All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.…”
  2. 62

    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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  7. 67

    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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  8. 68

    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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  9. 69

    UML-based regression testing for OO software by Mansour, Nashat

    Published 2010
    “…For the second phase, we present algorithms for detecting system level changes in the interaction overview diagram. …”
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  10. 70

    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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  11. 71

    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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  12. 72
  13. 73

    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. …”
  14. 74

    Dynamic multiple node failure recovery in distributed storage systems by Itani, May

    Published 2018
    “…In this work, we address the problem of multiple failure recovery with dynamic scenarios using the fractional repetition code as a redundancy scheme. The fractional repetition (FR) code is a class of regenerating codes that concatenates a maximum distance separable code (MDS) with an inner fractional repetition code where data is split into several blocks then replicated and multiple replicas of each block are stored on various system nodes. …”
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  15. 75

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben Romdhane, Haifa

    Published 2023
    “…SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. …”
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  16. 76

    Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE by Ben-Romdhane, Haïfa

    Published 2023
    “…SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. …”
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  17. 77

    Assessment of calcified aortic valve leaflet deformations and blood flow dynamics using fluid-structure interaction modeling by Armin, Amindari

    Published 2017
    “…However, implementation of this approach is difficult using custom built codes and algorithms. In this paper, we present an FSI modeling methodology for aortic valve hemodynamics using a commercial modeling software, ANSYS. …”
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  18. 78

    Assessment of calcified aortic valve leaflet deformations and blood flow dynamics using fluid-structure interaction modeling by Amindari, Armin

    Published 2017
    “…However, implementation of this approach is difficult using custom built codes and algorithms. In this paper, we present an FSI modeling methodology for aortic valve hemodynamics using a commercial modeling software, ANSYS. …”
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  19. 79

    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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