Showing 1 - 20 results of 94 for search '(((( element data algorithm ) OR ( element g algorithm ))) OR ( statistical modeling algorithm ))', query time: 0.14s Refine Results
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    Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

    Published 2023
    “…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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    Using genetic algorithms to optimize software quality estimation models by Azar, Danielle

    Published 2004
    “…Most such models are constructed using statistical or machine learning techniques. …”
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    masterThesis
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    Detection of statistically significant network changes in complex biological networks by Raghvendra Mall (581171)

    Published 2017
    “…</p><h3>Methods</h3><p dir="ltr">In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate <i>p</i>-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. …”
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    Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy by Haitao Xu (435549)

    Published 2023
    “…This study investigates the gradient-based, evolutionary, and Bayesian-based optimization algorithms. Combining statistical and ranking analyses confirms that the Levenberg–Marquardt (LM) is the most efficient optimization technique for training the MLPNN model. …”
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    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

    Published 2002
    “…Given a graph G = (V, E), a clique is a complete subgraph in G. …”
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    article
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    Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms by Humna Khan (17541972)

    Published 2022
    “…Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R<sup>2</sup>), were used to assess the prediction accuracy of the models. …”
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    A stochastic iterative learning control algorithm with application to an induction motor by Saab, Samer S.

    Published 2004
    “…The simulation results show good tracking performance in the presence of noise with erroneous model parameters and noise statistics. An open-loop control is also proposed to improve the tracking rate of the proposed ILC algorithms.…”
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    article
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    XBeGene: Scalable XML Documents Generator by Example Based on Real Data by Harazaki, Manami

    Published 2012
    “…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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    conferenceObject
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    A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai by ALGHANEM, HANI SUBHI MOHD

    Published 2024
    “…The proposed framework comprises key elements: Important Decisions derived from interviews with transportation leaders, Knowledge Management enhanced by AI algorithms, Data Mining/Analysis utilizing historical data, the Fleet Management System employing Oracle ERP, and a Data-Driven Decision Support Framework that leans towards the extended framework approach. …”
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