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1
Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The main difficulty with the existing rank-based methods is that they involve nonparametric estimation of the probability distribution of the model’s error terms. …”
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Thesis -
2
Estimation of transformers health index based on condition parameter factor and hidden Markov model
Published 2018“…Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. …”
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3
Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates
Published 2017“…In this thesis, we considered two methods via the classical maximum likelihood estimation (MLE) and the Bayes estimation using the Gibbs sampling (G-S) algorithm to estimate the parameters of BKBX, KBX and Beta-Weibull (BWB) models. …”
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4
Robust Kernel Density Function Estimation
Published 2010“…However, the weakness of IRWLS based estimator is that its computation time is very long. …”
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5
Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated. …”
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Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
Published 2006“…The mutual information algorithms used in this work has been developed and experimented in the system and has proven to yield more accurate and stable results than other algorithms. …”
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8
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…To enhance the selection of most highly ranking features, irrelevant features are ‘pruned’ based on determined boundary threshold. In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. …”
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9
Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…In this method, firstly, Weibull density function is utilized to model the wind speed and then several methods are applied to estimate the parameters of the wind speed distribution. …”
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10
Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models
Published 2004“…Since the volatility of the KLCI’s returns series is non-uniform, the data is split into three periods of time. In the beginning, the division of the data is based on the plot of the returns, but for the later part, it is based on the distribution of the returns. …”
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11
Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023Conference Paper -
12
An empirical study of density and distribution functions for ant swarm optimized rough reducts
Published 2011“…To describe relative probability of different random variables, Probability Density Function (PDF) and the Cumulative Density Function (CDF) are capable to specify its own characterization of Gaussian distributions. …”
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Book Chapter -
13
The effect of dose calculation algorithms on the normal tissue complication probability values of thoracic cancer
Published 2015“…Purpose: To identify the effect of dose calculation algorithms on the Normal Tissue Complication Probability values of thoracic cancer. …”
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Monograph -
14
Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Additionally, the traffic are relying on the markers and scheduling algorithms to the service classes at the routers. The higher level priority agreements give a higher or equal probability than the lower level, this technique is perfect at a core router by scheduling algorithm. …”
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15
Solving the vehicle routing problem with stochastic demands via hybrid genetic algorithm-tabu search
Published 2008“…This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. …”
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16
Hybrid FFT-ADALINE algorithm with fast estimation of harmonics in power system
Published 2016“…In the proposed method, both of the aforementioned algorithms are combined for harmonic estimation where it is able to respond immediately to any change of the measured harmonics and the settling time is reduced to half cycle of the measurement signal. …”
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17
Fault section detection and location on distribution network using analytical voltage sags database
Published 2006“…By doing this all the possible sections due to the fault can be selected. Finally, the most probable faulty section is identified using probability approach.This paper presents the implemented algorithms and the test of the algorithms on typical distribution networks. …”
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Conference or Workshop Item -
18
A Chaos-Based Substitution Box (S-Box) Design with Improved Differential Approximation Probability (DP)
Published 2018“…The proposed S-box shows very low differential approximation probability as compared to other chaos-based S-box designed recently, while maintaining good cryptographic properties and high value of linear approximation probability. …”
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19
A Chaos-Based Substitution Box (S-Box) Design with Improved Differential Approximation Probability (DP)
Published 2018“…The proposed S-box shows very low differential approximation probability as compared to other chaos-based S-box designed recently, while maintaining good cryptographic properties and high value of linear approximation probability. …”
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20
Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms / Yuliant Sibaroni, Sri Suryani Prasetiyowati and Iqbal Bahari Sudrajat
Published 2020“…The activation function in this neural network model then estimated using genetic algorithms. Determination of the best factor is carried out in a genetic algorithm by combining several parameters of the crossover probability (Pc) and mutation probability (Pm). …”
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