Search Results - (( data distribution function algorithm ) OR ( processes evaluation study algorithm ))
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Examination invigilation timetable using Genetic Algorithm / Mohamad Fakhrullah Ibrahim
Published 2025“…To overcome these issues, a system utilizing a Genetic Algorithm (GA) was developed to automate and optimize the timetabling process. …”
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Peak pressure analysis of foot plantar distribution based on image processing algorithm
Published 2018“…The other main goal of this work is to create an algorithm which has the ability to formulate accurately and reliably the distribution of pressure over the foot plantar. …”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…Providing a balanced partitioning with equal weights are widely studied. However, in heterogeneous architecture, process heterogeneity demands partitions with different weights. …”
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Robust Kernel Density Function Estimation
Published 2010“…Finally this thesis also addresses the estimation of Mutual Information (MI) for mixture distribution which prone to create two distant groups in the data. …”
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Geometric feature descriptor and dissimilarity-based registration of remotely sensed imagery
Published 2024journal::journal article -
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Universiti malaysia pahang autonomous shuttle Development : Lane classification analysis using convolutional neural network (CNN)
Published 2022“…Four significant operations involve in developing the system which are data acquisition, data pre-processing, data training and data testing. …”
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Universiti Malaysia Pahang autonomous shuttle development : Lane classification analysis using Convolutional Neural Network (CNN)
Published 2022“…Four significant operations involve in developing the system which are data acquisition, data pre-processing, data training and data testing. …”
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Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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A new Gompertz-three-parameter-lindley distribution for modeling survival time data
Published 2025“…The statistical properties of the proposed distribution including the shape properties, cumulative distribution, quantile functions, moment generating function, failure rate function, mean residual function, and stochastic orders are studied. …”
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. …”
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Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The main contribution of this research is developing statistical approaches, and introducing new algorithms and resampling methods for analysing interval-censored data through AFT models.…”
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Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar
Published 2015“…The development of a hydrodynamic distributed model is designed to simulate discharge and water levels as a function of space and time. …”
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Slice sampler algorithm for generalized pareto distribution
Published 2018“…In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…In order to evaluate the scalability at specific data size the appropriate regression models are fitted through the measured data as functions of number of workers. …”
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Parameter-driven count time series models / Nawwal Ahmad Bukhari
Published 2018“…A key property of our model is that the distributions of the observed count data are independent, conditional on the latent process, although the observations are correlated marginally. …”
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Sizing and placement of solar photovoltaic plants by using time-series historical weather data
Published 2018“…To predict the output from the PV modules, 15 years of solar data were modeled with the aid of a beta probability density function. …”
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Sizing and placement of solar photovoltaic plants by using time-series historical weather data
Published 2018“…To predict the output from the PV modules, 15 years of solar data were modeled with the aid of a beta probability density function. …”
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