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    Effectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters

    Source: Journal of Environmental Engineering:;2002:;Volume ( 128 ):;issue: 006
    Author:
    T. R. Neelakantan
    ,
    Srinivasa Lingireddy
    ,
    Gail M. Brion
    DOI: 10.1061/(ASCE)0733-9372(2002)128:6(533)
    Publisher: American Society of Civil Engineers
    Abstract: A neural network approach was employed to relate risky
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      Effectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters

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    http://yetl.yabesh.ir/yetl1/handle/yetl/57231
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    • Journal of Environmental Engineering

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    contributor authorT. R. Neelakantan
    contributor authorSrinivasa Lingireddy
    contributor authorGail M. Brion
    date accessioned2017-05-08T21:36:11Z
    date available2017-05-08T21:36:11Z
    date copyrightJune 2002
    date issued2002
    identifier other%28asce%290733-9372%282002%29128%3A6%28533%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/57231
    description abstractA neural network approach was employed to relate risky
    publisherAmerican Society of Civil Engineers
    titleEffectiveness of Different Artificial Neural Network Training Algorithms in Predicting Protozoa Risks in Surface Waters
    typeJournal Paper
    journal volume128
    journal issue6
    journal titleJournal of Environmental Engineering
    identifier doi10.1061/(ASCE)0733-9372(2002)128:6(533)
    treeJournal of Environmental Engineering:;2002:;Volume ( 128 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian