LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Back to previous search Search history
Advanced search

Your last searches

  1. AU="Pollard, Jacquan"
  2. AU="Damas, Joana"

Search results

Result 1 - 2 of total 2

Search options

  1. Article: Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models.

    Majumder, Reek / Pollard, Jacquan / Salek, M Sabbir / Werth, David / Comert, Gurcan / Gale, Adrian / Khan, Sakib Mahmud / Darko, Samuel / Chowdhury, Mashrur

    Environmental health insights

    2024  Volume 18, Page(s) 11786302241227307

    Abstract: The environmental impacts of global warming driven by methane ( ... ...

    Abstract The environmental impacts of global warming driven by methane (CH
    Language English
    Publishing date 2024-02-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2452946-1
    ISSN 1178-6302
    ISSN 1178-6302
    DOI 10.1177/11786302241227307
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Book ; Online: Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

    Majumder, Reek / Pollard, Jacquan / Salek, M Sabbir / Werth, David / Comert, Gurcan / Gale, Adrian / Khan, Sakib Mahmud / Darko, Samuel / Chowdhury, Mashrur

    2023  

    Abstract: The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) ... ...

    Abstract The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-12-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top