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  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

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  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)

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  3. Book ; Online: Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack

    Majumder, Reek / Khan, Sakib Mahmud / Ahmed, Fahim / Khan, Zadid / Ngeni, Frank / Comert, Gurcan / Mwakalonge, Judith / Michalaka, Dimitra / Chowdhury, Mashrur

    2021  

    Abstract: Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic sign images can be misclassified by an adversarial attack on machine ... ...

    Abstract Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic sign images can be misclassified by an adversarial attack on machine learning models used by AVs for traffic sign recognition. To make classification models resilient against adversarial attacks, we used a hybrid deep-learning model with both the quantum and classical layers. Our goal is to study the hybrid deep-learning architecture for classical-quantum transfer learning models to support the current era of intermediate-scale quantum technology. We have evaluated the impacts of various white box adversarial attacks on these hybrid models. The classical part of hybrid models includes a convolution network from the pre-trained Resnet18 model, which extracts informative features from a high dimensional LISA traffic sign image dataset. The output from the classical processor is processed further through the quantum layer, which is composed of various quantum gates and provides support to various quantum mechanical features like entanglement and superposition. We have tested multiple combinations of quantum circuits to provide better classification accuracy with decreasing training data and found better resiliency for our hybrid classical-quantum deep learning model during attacks compared to the classical-only machine learning models.

    Comment: 16 pages, 7 figures
    Keywords Quantum Physics ; Computer Science - Cryptography and Security ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-08-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    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.

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