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Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching
Published 2016“…A popular distribution for the modelling of discrete count data is the Poisson distribution. …”
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Dynamic replication algorithm in data grid: Survey
Published 2008“…Data replication is a common method used to improve the performance of data access in distributed systems. …”
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Binary vote assignment on grid quorum replication technique with association rule
Published 2018“…Organizations need to provide current data to users who may be geographically remote and to handle a volume of requests of data distributed around multiple sites in distributed environment. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…The von Mises distribution is the most commonly used probability distribution of a circular random variable, and the concentration of a circular data set is measured using the mean resultant length. …”
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Statistical modelling of time series of counts for a new class of mixture distributions / Khoo Wooi Chen
Published 2016“…Finally, some real life data sets from different disciplines have been applied to illustrate the analyses. …”
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Design and Implementation of Data-at-Rest Encryption for Hadoop
Published 2017“…It is shown that the implementation of AES encryption algorithm is capable to secure data stored in HDFS to some extent.…”
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Design and Implementation of Data-at-Rest Encryption for Hadoop
Published 2017“…It is shown that the implementation of AES encryption algorithm is capable to secure data stored in HDFS to some extent.…”
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Machine learning in botda fibre sensor for distributed temperature measurement
Published 2023“…To make the sensor more reliable, temperature data must be collected over the length of the cable, or distributed data rather than point data. …”
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Modified sequential fences for identifying univariate outliers
Published 2016“…The existence of outliers in data set can bring some impacts on statistical data analysis and affect decision making. …”
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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data
Published 2013“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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Comparison of diverse ensemble neural network for large data classification
Published 2015“…DRT is an enhanced algorithm based on distributed random for different neural networks. …”
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Survey on job scheduling mechanisms in grid environment
Published 2015“…Grid systems provide geographically distributed resources for both computational intensive and data-intensive applications.These applications generate large data sets.However, the high latency imposed by the underlying technologies; upon which the grid system is built (such as the Internet and WWW), induced impediment in the effective access to such huge and widely distributed data.To minimize this impediment, jobs need to be scheduled across grid environments to achieve efficient data access.Scheduling multiple data requests submitted by grid users onto the grid environment is NP-hard.Thus, there is no best scheduling algorithm that cuts across all grids computing environments.Job scheduling is one of the key research area in grid computing.In the recent past many researchers have proposed different mechanisms to help scheduling of user jobs in grid systems.Some characteristic features of the grid components; such as machines types and nature of jobs at hand means that a choice needs to be made for an appropriate scheduling algorithm to march a given grid environment.The aim of scheduling is to achieve maximum possible system throughput and to match the application needs with the available computing resources.This paper is motivated by the need to explore the various job scheduling techniques alongside their area of implementation.The paper will systematically analyze the strengths and weaknesses of some selected approaches in the area of grid jobs scheduling.This helps researchers better understand the concept of scheduling, and can contribute in developing more efficient and practical scheduling algorithms.This will also benefit interested researchers to carry out further work in this dynamic research area.…”
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A spawn mobile agent itinerary planning approach for energy-efficient data gathering in wireless sensor networks
Published 2017“…Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. …”
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Pile Capacity Checking Tool based on Distributed Fibre Optic Sensing for Instrumented Pile Load Test
Published 2023“…Experts are reconciling with different data processing algorithms to assess pile capacity (t-z curve) from the DOFS data and verify it, leading to a demand for standard practice. …”
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A bayesian via laplace approximation on log-gamma model with censored data
Published 2016“…Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. …”
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Markov chain Monte Carlo convergence diagnostics for Gumbel model
Published 2016“…The MCMC technique, Metropolis-Hastings algorithm is used for posterior inferences of Gumbel distribution simulated data.…”
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Solving the vehicle routing problem with stochastic demands via hybrid genetic algorithm-tabu search
Published 2008“…The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. …”
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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…In this case, the estimation was developed based on the exponential and Weibull distributions using the right and interval censoring types; and 2) when covariates were incorporated into the analysis through the scaleparameter of the exponential distribution only using the same types of data censoring. …”
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