This paper considers the problem of predicting the number, length and distribution of traffic flows some time into the future, based upon packets collected in the present. Three methods– the standard Expectation-Maximization algorithm, a distributed version of the Expectation-Maximization algorithm, and a Particle Filter– are used to predict the mean flow length and complete flow distributions ...

Purpose: This study aims to identify the right combination of network architecture, learning rate, and epoch in making predictions at each rainfall post Maros Regency. In addition, this also predicts monthly profile 2021-2025 Regency.Methods: The method is backpropagation neural algorithm learn predict data. BPNN one most commonly used non-linear methods recently. data from 2000-2020 as trainin...

Recent network analysis research has made remarkable progress on studying complex networks in a social context with machine learning techniques. One example of such a complex network is the system data of the New York Citibike bike sharing program, which consists of the data from 49 million bike trips in New York City since July 2013. By inspecting the dataset, we find out that significantly mo...

In this work, the time series modeling was used to predict the Tazareh coal mine risks. For this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. Next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. After extrusion of the trend, seasonali...

the planning of water resources is based on the volume of water extracted from the aquifer and accurate estimate of this volume considerably helps to development. in this study, the bayesian networks model using continues and clustering structures was used to simulate the groundwater level of birjand aquifer. bayesian networks was calibrated with five input variables of aquifer recharge, water ...

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network Sulaimani Governorate. Eight models were developed, for each as prediction. The accuracy prediction obtain excellent with correlation coefficients between predicted and measured values ranged from (90% 97.2%). are found after many trials those highest coefficient selected...