Search Results - (( property distribution methods algorithm ) OR ( a distribution function algorithm ))

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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Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

“…Also, a one-step iterative algorithm, called expected rank algorithm, is introduced which is more complicated than the estimation-approximation algorithm, but more accurate. …”
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On a new transmuted three-parameter lindley distribution and its applications by Xi, Yuhang, Lu, Hezhi, Liang, Fei

“…In this paper, a new transmuted three-parameter Lindley distribution (TTHPLD) is established using the transmutation map method, which includes the Lindley distribution, two-parameter Lindley distribution, transmuted two-parameter Lindley distribution and three-parameter Lindley distribution as special cases. …”
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A Novel Approach Of Groebner Bases Computation For Safety Analysis Of Distributed Discrete Controllers by Alwi @ Suhaimi, Saifulza, Salleh, Mohd Rizal, Jaafar, Hazriq Izzuan, Ab Ghani, Mohd Ruddin, Md Fauadi, Muhammad Hafidz Fazli

“…In this research, our main objective is to generate a new model checking computation method based on Groebner bases algorithm for safety property analysis of distributed discrete controllers. …”
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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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Modeling The Modified Internal Rate Of Return (Mirr) For Long-Term Investment Strategy By The Assumption Of Gamma Distribution by Sayed, Amani Idris A

“…The findings demonstrate that the gamma distribution provides a good fit for modeling the transformed MIRR over a long-term investment period. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…In addition, the classifier is also optimized such that it has a good generalization property. The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…Lastly, in order to externally judge the validity of these types of clustering algorithms, there is a need for a method to correctly and efficiently evaluate their variant multiclass clustering results. …”
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High performance visualization of human tumor growth software by Alias, Norma, Mohd. Said, Norfarizan, Khalid, Siti Nur Hidayah, Sin, Dolly Tien Ching, Phang, Tau Ing

“…The platform for high performance computing of the parallel algorithms run on a distributed parallel computer system. …”
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Enhanced Automated Framework For Cattle Tracking And Classification by Williams, Bello Rotimi

“…Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness.…”
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High performance simulation for brain tumors growth using parabolic equation on heterogeneous parallel computer systems by Pheng, H. S., Alias, Norma, Mohd. Said, Norfarizan

“…This paper focuses on the implementation of parallel algorithm for the simulation of brain tumours growth using one dimensional parabolic equation, design on a distributed parallel computer system. …”
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohammad Fadhil, Abas, Bayuaji, Luhur

“…This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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Geometric feature descriptor and dissimilarity-based registration of remotely sensed imagery by Kahaki S.M.M., Arshad H., Nordin M.J., Waidah Ismail

High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system by Pheng H. S., Norma Alias, Norfarizan Mohd Said

“…This paper focuses on the implementation of parallel algorithm for the simulation of brain tumours growth using one dimensional parabolic equation, design on a distributed parallel computer system. …”
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Ayop Azmi, Nurnajmin Qasrina Ann, Pebrianti, Dwi, Abas, Mohammad Fadhil, Bayuaji, Luhur

“…This study proposes an automated-tuned hyper�parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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Statistical modelling of time series of counts for a new class of mixture distributions / Khoo Wooi Chen by Khoo, Wooi Chen

“…Parameter estimation with the maximum likelihood estimation via the Expectation-Maximization algorithm is discussed and compared with the conditional least squares method. …”
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Predicting the popularity of tweets using the theory of point processes. by Tan, Wai Hong

“…The mode of the posterior distribution is used as the estimator of the finite-dimensional parameter, and suitable functionals of the predictive distribution for the number of retweets implied by the estimated model are used to predict the tweet popularity. …”
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A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources by Ahmadipour M., Ali Z., Ramachandaramurthy V.K., Ridha H.M.

New Curve Fitting Method for Petroleum Engineering Applications by Mohd Rosli, Muhammad Aizuddin

“…Curve fitting is the process of creating a curve or mathematical function that will produce the best fit to a series of data points. …”
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Predictive Framework for Imbalance Dataset by Megat Norulazmi, Megat Mohamed Noor

“…Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. …”
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