يعرض 1 - 10 نتائج من 21 نتيجة بحث عن '"Ather, Abbas"', وقت الاستعلام: 1.16s تنقيح النتائج
  1. 1
    دورية أكاديمية
    Long short-term memory models of water quality in inland water environments

    المؤلفون: JongCheol Pyo, Yakov Pachepsky, Soobin Kim, Ather Abbas, Minjeong Kim, Yong Sung Kwon, Mayzonee Ligaray, Kyung Hwa Cho

    المصدر: Water Research X, Vol 21, Iss , Pp 100207- (2023)

    مصطلحات موضوعية: Long short-term memory, Inland water, Water quality, Ensemble LSTM, Deep learning models, Environmental technology. Sanitary engineering, TD1-1066

    وصف الملف: electronic resource

    Relation: http://www.sciencedirect.com/science/article/pii/S2589914723000439; https://doaj.org/toc/2589-9147

    URL الوصول: https://doaj.org/article/c57d1792701f4337b2ac592906a46a05

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  2. 2
    دورية أكاديمية
    Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

    المؤلفون: Do Hyuck Kwon, Seok Min Hong, Ather Abbas, Sanghyun Park, Gibeom Nam, Jae-Hyun Yoo, Kyunghyun Kim, Hong Tae Kim, JongCheol Pyo, Kyung Hwa Cho

    المصدر: GIScience & Remote Sensing, Vol 60, Iss 1 (2023)

    مصطلحات موضوعية: super-resolution, convolutional neural network (cnn), generative adversarial network (gan), remote sensing, chlorophyll-a, Mathematical geography. Cartography, GA1-1776, Environmental sciences, GE1-350

    وصف الملف: electronic resource

    Relation: https://doaj.org/toc/1548-1603; https://doaj.org/toc/1943-7226

    URL الوصول: https://doaj.org/article/3f12053e68594d26bfbef3c5634fd85b

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  3. 3
    Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions

    المؤلفون: Zeeshan Haider Jaffari, Ather Abbas, Muhammed Umer, Eun-Sik Kim, Kyung Hwa Cho

    المصدر: Journal of Materials Chemistry A. 11:9009-9018

    مصطلحات موضوعية: Renewable Energy, Sustainability and the Environment, General Materials Science, General Chemistry

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::658345c924660f42962b6fda47c436aa
    https://doi.org/10.1039/d3ta00019b

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  4. 4
    Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts

    المؤلفون: Muhammad Umer, Sohaib Umer, Mohammad Zafari, Miran Ha, Rohit Anand, Amir Hajibabaei, Ather Abbas, Geunsik Lee, Kwang S. Kim

    المصدر: Journal of Materials Chemistry A. 10:6679-6689

    مصطلحات موضوعية: Renewable Energy, Sustainability and the Environment, General Materials Science, General Chemistry

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::d710a117182c6154f92cf359e91ac09c
    https://doi.org/10.1039/d1ta09878k

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  5. 5
    In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

    المؤلفون: Ather Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, Kyung Hwa Cho

    المصدر: Hydrology and Earth System Sciences. 25:6185-6202

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::2024dedcb2d2bd9b16ea8422d7e7f3c7
    https://doi.org/10.5194/hess-25-6185-2021

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  6. 6
    An open-source deep learning model for predicting effluent concentration in capacitive deionization

    المؤلفون: Moon Son, Nakyung Yoon, Sanghun Park, Ather Abbas, Kyung Hwa Cho

    المصدر: The Science of the total environment. 856(Pt 2)

    مصطلحات موضوعية: Ions, Environmental Engineering, Deep Learning, Electricity, Environmental Chemistry, Pollution, Waste Management and Disposal, Electrodes, Water Purification

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f793cc786c4c8e3ab8259d432da086f2
    https://pubmed.ncbi.nlm.nih.gov/36191701

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  7. 7
    Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

    المؤلفون: Sangsoo Baek, Ather Abbas, Minji Park, Kyung Hwa Cho

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::8878041ff300df27583095e91e6a167d
    https://doi.org/10.21203/rs.3.rs-1643745/v1

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  8. 8
    Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

    المؤلفون: Jiye Lee, Ather Abbas, Gregory W. McCarty, Xuesong Zhang, Sangchul Lee, Kyung Hwa Cho

    المصدر: Journal of Hydrology. 617:128916

    مصطلحات موضوعية: Water Science and Technology

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::eccc872a3abfefc7eb49169079d68c68
    https://doi.org/10.1016/j.jhydrol.2022.128916

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  9. 9
    Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green

    المؤلفون: Zeeshan Haider, Jaffari, Ather, Abbas, Sze-Mun, Lam, Sanghun, Park, Kangmin, Chon, Eun-Sik, Kim, Kyung Hwa, Cho

    المصدر: Journal of Hazardous Materials. 442:130031

    مصطلحات موضوعية: Machine Learning, Environmental Engineering, Health, Toxicology and Mutagenesis, Environmental Chemistry, Wastewater, Bismuth, Pollution, Waste Management and Disposal, Humic Substances

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::877ea9b75f1e4c006c59039440a09df4
    https://doi.org/10.1016/j.jhazmat.2022.130031

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  10. 10
    AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

    المؤلفون: Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, Kyung Hwa Cho

    المساهمون: Géosciences Environnement Toulouse (GET), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

    المصدر: Geoscientific Model Development
    Geoscientific Model Development, 2022, 15, pp.3021-3039. ⟨10.5194/gmd-15-3021-2022⟩

    مصطلحات موضوعية: [SDU]Sciences of the Universe [physics], ComputerApplications_MISCELLANEOUS

    URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a06d906664ddf02b1c528eac14fe61ce
    https://hal-insu.archives-ouvertes.fr/insu-03661482

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