Showing 21 - 40 results of 64 for search '(((( complement forest algorithm ) OR ( elements data algorithm ))) OR ( level making algorithm ))', query time: 0.09s Refine Results
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    Correlation Clustering with Overlaps by Fakhereldine, Amin

    Published 2020
    “…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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    masterThesis
  4. 24

    Boosting the visibility of services in microservice architecture by Ahmet Vedat Tokmak (17773479)

    Published 2023
    “…Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.…”
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    Predicting Plasma Vitamin C Using Machine Learning by Daniel Kirk (17302798)

    Published 2022
    “…<p dir="ltr">Precision Nutrition makes use of personal information about individuals to produce nutritional recommendations that have more utility than general population level recommendations. …”
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    Student advising decision to predict student's future GPA based on Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

    Published 2015
    “…Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. …”
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    conferenceObject
  9. 29

    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. …”
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    A novel approach for real time flows scheduling by Fawaz, W.

    Published 2006
    “…A plethora of packet-scheduling algorithms have been proposed in the literature in order to meet the stringent time constraints of real time flows at an IP router level. …”
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    conferenceObject
  11. 31

    Recursive Parameter Identification Of A Class Of Nonlinear Systems From Noisy Measurements by Emara-Shabaik, Husam

    Published 2020
    “…The model structure is made up of two linear dynamic elements separated by a nonlinear static one. The nonlinear element is assumed to be of the polynomial type with known order; The identification is based on input/output data where the output is contaminated with measurement noise. …”
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    article
  12. 32

    Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma by Rawan AlSaad (14159019)

    Published 2019
    “…However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). …”
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    Genetic Fuzzimetric Technique (GFT) by Kouatli, Issam

    Published 2012
    “…Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. …”
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    conferenceObject
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    A survey and comparison of wormhole routing techniques in a meshnetworks by Al-Tawil, K.M.

    Published 1997
    “…Although an extremely wide number of routing algorithms have been proposed and implemented in hardware and software, it is difficult for the designer of a multicomputer to choose the best routing algorithm given a particular architectural configuration. …”
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    article
  15. 35

    Structural similarity evaluation between XML documents and DTDs by Tekli, J.

    Published 2007
    “…The automatic processing and management of XML-based data are ever more popular research issues due to the increasing abundant use of XML, especially on the Web. …”
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    conferenceObject
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    Adaptive false discovery rate for wavelet denoising of pavement continuous deflection measurements by Katicha, Samer W.

    Published 2016
    “…The algorithm minimizes the classification error of features in the wavelet transform domain by adaptively selecting the level at which to control the false discovery rate. …”
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    article
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