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Showing 1 - 20 results of 76 for search '(( data segmentation algorithm ) OR ( data recommendation algorithm ))', query time: 0.11s Refine Results
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    Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? by Saravanan Thirumuruganathan (11038038)

    Published 2021
    “…The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. …”
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    Machine Learning Approach for the Design of an Assessment Outcomes Recommendation System by Abu Zitar, Raed

    Published 2021
    “…We research and test the design of the right neural networks that achieves our goal. A modern algorithm was improvised for this reason. For our proposed recommendation system, a database program was created to store data and include details in the analysis of course learning outcomes. …”
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    Detecting latent classes in tourism data through response-based unit segmentation (REBUS) in Pls-Sem by Assaker, Guy

    Published 2016
    “…This research note describes Response-Based Unit Segmentation (REBUS), a “latent class detection” technique used in partial least squares–structural equation modeling (PLS-SEM) to examine data heterogeneity. …”
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    Big Data Energy Management, Analytics and Visualization for Residential Areas by Gupta, Ragini

    Published 2020
    “…Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor and visualize the energy consumption in their respective grid segments on daily, monthly, and yearly basis. A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. …”
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    Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations by Shameem A. Puthiya Parambath (14150997)

    Published 2022
    “…The best available recommendation algorithms are based on using the observed preference information among collaborating entities. …”
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    A comparison of data mapping algorithms for parallel iterative PDE solvers by Mansour, Nashat

    Published 1995
    “…We review and evaluate the performances of six data mapping algorithms used for parallel single-phase iterative PDE solvers with irregular 2-dimensional meshes on multicomputers. …”
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    A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

    Published 2025
    “…New developments in the deep learning field contributed to the development of new application domains and this has made segmenting nuclei possible. In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. …”
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    VEGAWES: variational segmentation on whole exome sequencing for copy number detection by Samreen Anjum (19651882)

    Published 2015
    “…In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data. …”
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    Building power consumption datasets: Survey, taxonomy and future directions by Yassine Himeur (14158821)

    Published 2020
    “…The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. …”
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    Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations by Md Mosarrof Hossen (21399056)

    Published 2025
    “…To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. …”