دانلود مقاله انگلیسی رایگان:تشخیص سیستم بحرانی سیل بر اساس اینترنت اشیا، داده های بزرگ و شبکه عصبی عمیق پیچشی - 2020
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  • Detection of flood disaster system based on IoT, big data and convolutional deep neural network Detection of flood disaster system based on IoT, big data and convolutional deep neural network
    Detection of flood disaster system based on IoT, big data and convolutional deep neural network

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Detection of flood disaster system based on IoT, big data and convolutional deep neural network


    ترجمه فارسی عنوان مقاله:

    تشخیص سیستم بحرانی سیل بر اساس اینترنت اشیا، داده های بزرگ و شبکه عصبی عمیق پیچشی


    منبع:

    Sciencedirect - Elsevier - Computer Communications, 150 (2020) 150-157: doi:10:1016/j:comcom:2019:11:022


    نویسنده:

    M. Anbarasan a, BalaAnand Muthu b,∗, C.B. Sivaparthipan c, Revathi Sundarasekar d, Seifedine Kadry e, Sujatha Krishnamoorthy f, Dinesh Jackson Samuel R. g, A. Antony Dasel


    چکیده انگلیسی:

    Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next, the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural Network (DNN), the proposed system gives is very accurate result than other methods.
    Keywords: Hadoop distributed file system (HDFS) | Convolutional deep neural network (CDNN) | Normalization | Rule generation | Missing value imputation


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 8
    حجم فایل: 1140 کیلوبایت

    قیمت: رایگان


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