A New HadoopBased Network Management System withPolicy Approach

In recent years with the improvement in the field of network technology and decreasing of technology cost, lots of data are produced. This massive amount of data needs mechanism for processing and mining information rapidly. In this paper a new Hadoop based network management system with policy approach which is considered hierarchical manager is presented. Storing and processing massive data efficiently are two capability of Hadoop technology by using HDFS and MapReduce. In this paper, processing time is considered as a main factor. As a result it is proved that this management system using policy approach increases the performance of entire system without putting on extra cost for implementation. This system in contrast with pure Hadoop and centralized system is several times more rapid

Comparative Analysis of Estimating Monthly Reference Evapotranspiration Using Kernel and Tree-Based Data Mining Models Versus Empirical Methods

Because direct measurement of evapotranspiration is costly and time-consuming, researchers have turned to the estimation of evapotranspiration via indirect approaches. The aim of this study is to investigate the capability of kernel-based, tree-based, bagging-based data-driven, and empirical models to estimate reference evapotranspiration. For this purpose, data related to meteorological parameters such as average temperature, hours of sunshine, maximum and minimum temperature, wind speed, precipitation, and relative humidity were collected over a period of 39 years. A correlation matrix, relief algorithm, and trial and error based on the author’s own experience were used to select input scenarios. The performance of these methods was evaluated using correlation coefficient (R2), root mean square error (RMSE), scattering index (SI), Nash Sutcliffe (NS), and Wilmot indexes (WI). Based on the results, scenario 13 includes maximum temperature and monthly time index based on the relief algorithm was selected as the best scenario, also on the other hand the random tree model with R=0.99, RMSE=0.04 mm/day, and SI=0.01 was selected as the superior method. Thus, the maximum temperature was defined as the efficient meteorological parameter for the reference evapotranspiration modeling

Estimation of Daily Reference Evapotranspiration in Humid Climates Using Data-Driven Methods of Gaussian Process Regression, Support Vector Regression and Random Forest

Accurate estimation of reference evapotranspiration has great importance in irrigation scheduling. Moreover, the lack of availability of lysimetric data has led researchers to use indirect methods, including data-driven approaches. In the present study, the ability of Gaussian process regression (GPR), support vector regression (SVR) and random forest (RF) data-driven methods was investigated to estimate the evapotranspiration of the reference plant. For this purpose, meteorological data on average temperature, wind speed, relative humidity and sunny hours in the period 2013-18 were collected in nine northern stations of Iran including Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr, Sari, Turkmen port, Gorgan, and Gonbad Kavous. Evapotranspiration calculated using FAO-Penman-Montith method was considered as the target output and four combined scenarios of meteorological parameters were considered to calibrate and validate the studied methods. The accuracy of the mentioned methods was compared using the statistical parameters of correlation coefficient, scatter index, and Wilmott’s coefficient. The results showed that GPR4 model with scatter index in the range of 0.132 to 0.179 in Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr and Sari stations, SVR4 model with dispersion index of 0.116 to 0.120 in Turkmen and Gonbad Kavous stations and the Hargreaves-Samani method with a scatter index of 0.509 at Gorgan station had much more accurate estimates of the evapotranspiration of the reference plant

Performance Analysis of Hydrological and Data Based models in Estimation of Suspended Sediment Rate

Data driven models are proposed as an alternative to hydrological methods in sediment estimation calculations. The aim of this study was to compare the performance and accuracy of hydrological and data-based methods in estimating the amount of suspended sediment. For this purpose, discharge and sediment data were collected in the period of 20 yr (2001-2011) and then the amount of suspended sediment of Bagh Kalayeh hydrometric station on Alamut River in Qazvin province was estimated. In this study hydrological methods including Smearing, FAO and Sediment Rating Curves versus data driven methods including Gene Expression Programming, Instance-Based Learning with parameter K and Linear Regression methods were used. The model performances were compared using two statistical methods of RRMSE and NS. The results showed that two techniques such as IBK model with evaluation criteria of (R = 0.94, RRMSE = 0.29 and NS = 0.24) and the GEP model with (R = 0.85, RRMSE = 0.59 and NS = 0.65) estimated suspended sediment in more accurate way than other studies methods. Thus, the superiority of data-driven methods in estimating the amount of suspended sediment in the study area was proved. Therefore, the use of data-based techniques as a competitor and alternative to hydrological methods to estimate the amount of suspended sediment in areas similar to the study area is recommended

Dietary pattern as identified by factorial analysis and its association with lipid profile and fasting plasma glucose among Iranian individuals with spinal cord injury

<p><b>Objectives</b>: Plasma lipids (triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C)) may be associated with dietary intakes. The purpose of this study was to identify the most common food patterns among Iranian persons with spinal cord injury (SCI) and investigate their associations with lipid profile.</p> <p><b>Design</b>: Cross-sectional.</p> <p><b>Setting</b>: Tertiary rehabilitation center.</p> <p><b>Participants</b>: Referred individuals to Brain and Spinal Injury Research Center (BASIR) from 2011 to 2014.</p> <p><b>Outcome Measures</b>: Dietary intakes were assessed by 24-hour dietary recall interviews in three non-consecutive days. Principal component analysis (PCA) was used to identify dietary patterns.</p> <p><b>Results</b>: Total of 100 persons (83 male and 17 female) entered the study. Four food patterns were detected. The most common dietary pattern (Pattern 1) included processed meat, sweets desserts and soft drink and was similar to ‘Western’ food pattern described previously. Pattern 1 was related to higher levels of TC and LDL-C (<i>r</i> = 0.09; P = 0.04 and <i>r</i> = 0.11; P = 0.03 for TC and LDL-C, respectively) only in male participants. Pattern 2 which included tea, nuts, vegetable oil and sugars had a positive association with TC level (<i>r</i> = 0.11; P = 0.02) again in male participants. Pattern 3 which represented a healthy food pattern showed no significant influence on lipid profiles.</p> <p><b>Conclusion</b>: In this study, the four most common dietary patterns among Iranian individuals with SCI have been identified. Western food pattern was the most common diet and was associated with increased TC and LDL-C. The healthy food pattern, in which the major source of calories was protein, was not associated with variance in lipid profile.</p