Search Results - (( evolution identification learning algorithm ) OR ( data distribution function algorithm ))

Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus by Darus, Zamzuhairi

“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.] by Wan Xing, Sultan Mohd, Mohd Rizman, Johari, Juliana, Ahmat Ruslan, Fazlina

“…Deep learning-based object detection algorithms can be categorized into three main types: end-to-end algorithms, two-stage algorithms, and one-stage algorithms. …”
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Enhanced Cuckoo Search Algorithm With Metaheuristic Components For Extracting The Maxima Of The Orientation Distribution Function by Shehab, Mohammad Mohammad Said

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The exploration of hybrid metaheuristics-based approaches: A bibliometric analysis by Nur Hidayah, Azmidi, Noryanti, Muhammad, Rozieana, Khairuddin

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Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems by Franco, Daniel Jose Da Graca Peceguina

“…Another problematic aspect is related to the intrusion detection solutions that are based on machine learning cluster algorithms to learn systems’ specifications and extract general state-based rules for attacks identification. …”
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Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

“…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 by Saeed, Sana

“…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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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection by Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya

“…In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). …”
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A new Gompertz-three-parameter-lindley distribution for modeling survival time data by Liang, Fei, Lu, Hezhi, Xi, Yuhang

“…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 by Salvinder Singh, Karam Singh, Shahrum, Abdullah, Nik Abdullah, Nik Mohamed

“…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 by Karimi, Mostafa

“…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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Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

“…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 by Shakeel Ahmed, Kamboh

“…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 by Nawwal , Ahmad Bukhari

“…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 by Ali, A., Mohd Nor, N., Ibrahim, T., Fakhizan Romlie, M.

“…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 by Ali, A., Mohd Nor, N., Ibrahim, T., Fakhizan Romlie, M.

“…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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Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal by Zainal, Mohamad Izwan

“…In addition, objective function using the same CSSA algorithm were applied i.e., Vmin and Ploss as the objective function, and multi-objective involves Vmin and Ploss as the objective function. …”
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Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching by Low, Yeh Ching

“…A popular distribution for the modelling of discrete count data is the Poisson distribution. …”
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Discovery of SIP/DRIP approach in distributed inter process communication by Hamid H., Jais J.

“…This paper made experiments on the combination of SIP/DRIP algorithm with DIPC distributed system to increase the computation speed of the method. …”

Bayesian inference for the bivariate extreme model by Mohd Amin, Nor Azrita, Adam, Mohd Bakri

“…Maximum likelihood method and a Markov chain Monte Carlo (MCMC) technique, Multiple-try Metropolis algorithm are implemented into the data analysis. MTM algorithm is the new alternative in the field of Bayesian extremes for summarizing the posterior distribution. …”
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