Showing 201 - 220 results of 433 for search '(((( develop next algorithm ) OR ( elements data algorithm ))) OR ( data models algorithm ))', query time: 0.13s Refine Results
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    Practical Multiple Node Failure Recovery in Distributed Storage Systems by Itani, M.

    Published 2016
    “…The problem is solved using genetic algorithms that search within the feasible solution space. …”
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  4. 204

    A Novel Internal Model Control Scheme for Adaptive Tracking of Nonlinear Dynamic Plants by Khan, T.

    Published 2006
    “…The U-model utilizes only past data for plant modelling and standard root solving algorithm for control law formulation. …”
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    article
  5. 205

    Modelling surface currents in the Eastern Levantine Mediterranean using surface drifters and satellite altimetry by Issa, Leila

    Published 2016
    “…We present a new and fast method that blends altimetric and drifter positions data in order to predict the surface velocity in the Eastern Levantine Mediterranean. …”
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  6. 206

    Global smart cities classification using a machine learning approach to evaluating livability, technology, and sustainability performance across key urban indices by Aya Hasan Alkhereibi (17151070)

    Published 2025
    “…The methodology involves data preparation with <u>imputation</u> and normalization, followed by training 9 supervised ML models. …”
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    Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS by Tekli, Joe

    Published 2018
    “…To do so, we design and construct a semantic-aware inverted index structure called SemIndex, extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex, in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. …”
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    Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma by Rawan AlSaad (14159019)

    Published 2019
    “…<h3>Background</h3><p dir="ltr">Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. …”
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    Single channel speech denoising by DDPG reinforcement learning agent by Sania Gul (18272227)

    Published 2025
    “…It achieves this performance by utilizing data that is 7 times smaller than that required by other models. …”
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    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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    An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study by Ayman Hassan (14426412)

    Published 2024
    “…</p><h3>Results</h3><p dir="ltr">In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. …”
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    C-3PA: Streaming Conformance, Confidence and Completeness in Prefix-Alignments by Raun, Kristo

    Published 2023
    “…The aim of streaming conformance checking is to find dis crepancies between process executions on streaming data and the refer ence process model. The state-of-the-art output from streaming confor mance checking is a prefix-alignment. …”
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    Optimising Nurse–Patient Assignments: The Impact of Machine Learning Model on Care Dynamics—Discursive Paper by Mutaz I. Othman (21186827)

    Published 2025
    “…<h3>Background</h3><p dir="ltr">Machine learning (ML) models can enhance patient–nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. …”