Search Results - (( data distribution means algorithm ) OR ( parameter optimization mead algorithm ))

Fitting time-varying coefficients SEIRD model to Covid-19 cases in Malaysia by Zulkarnain, Norsyahidah, Abdul Hadi, Muhammad Salihi, Mohammad, Nurul Farahain, Shogar, Ibrahim

“…Nelder-Mead outperforms the other optimization algorithm with the least error. …”
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Single and Multiple variables control using Tree Physiology Optimization by Halim, A.H., Ismail, I.

“…In the proposed method, each shoot from each branch search for possible solution in parallel and the fitness is evaluated based on all best values found by branch search. The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”

Modelling transmission dynamics of covid-19 during Pre-vaccination period in Malaysia: a predictive guiseird model using streamlit by Zulkarnain, Norsyahidah, Abdul Hadi, Muhammad Salihi, Mohammad, Nurul Farahain, Shogar, Ibrahim

“…The mathematical model is solved using Scipy odeint function, which uses Livermore Solver for Ordinary Differential Equations with an Automatic method switching (LSODA) algorithm. The time-varying coefficients of SEIRD model that best fit the real data of COVID-19 cases are obtained using the Nelder-Mead optimization algorithm. …”
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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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Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data by Hamza, Abubakar

“…The finding reveals that the Weibull distribution is well-suited to describing the investment behaviour of the MPS based on the estimates via the SA algorithm. …”
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Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data by Rostami, Mohammad

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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis by Tie, K. H., A., Senawi, Chuan, Z. L.

“…Overall, the k-means outperforms the Gaussian mixture distribution in selecting smaller feature subsets. …”
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A dynamic replication aware load balanced scheduling for data grids in distributed environments of internet of things by Bakhshad, Said, Noor, Rafidah Md, Akhundzada, Adnan, Saba, Tanzila, Ahmedy, Ismail, Haroon, Faisal, Nazir, Babar

“…Grid computing is a powerful distributed and scalable computing infrastructure that deals with massive data-intensive applications. …”
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Replica Creation Algorithm for Data Grids by Madi, Mohammed Kamel

“…This thesis presents a new replication algorithm that improves data access performance in data grids by distributing relevant data copies around the grid. …”
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Power-efficient wireless coverage using minimum number of uavs by Sawalmeh A., Othman N.S., Liu G., Khreishah A., Alenezi A., Alanazi A.

Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…One of the main issues in genetic k-means based algorithms is their sensitivity to outliers and unevenly distributed clusters due to the mean compromised computations. …”
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Dynamic replication aware load blanced scheduling in distributed environment / Said Bakhshad by Said Bakhshad, Bakhshad

“…Grid computing is an effective distributed and adaptable processing network that manages a huge number of data applications. …”
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Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

“…For example, the distribution analogues to the normal distribution in linear data is known as circular normal distribution. …”
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Development of an effective clustering algorithm for older fallers by Goh, Choon Hian, Wong, Kam Kang, Tan, Maw Pin *, Ng, Siew Cheok, Chuah, Yea Dat, Kwan, Ban Hoe

“…The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. …”
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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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Locating fault using voltage sags profile for underground distribution system by Mokhlis, Hazlie, Li, H.Y., Bakar, A.H.A., Mohamad, H.

“…This paper presents an alternative fault location algorithm to estimate short-circuit faults location in electrical distribution networks using only voltage sags data. …”
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Modified sequential fences for identifying univariate outliers by Wong, Hui Shein

“…The modified sequential fences method is found can accurately detect the outliers in positively skewed distribution. In addition, this proposed method also estimates trimmed mean and trimmed standard deviation with smaller bias and smaller root of mean squares error. …”
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Fuzzy Soft Set Clustering for Categorical Data by Iwan Tri Riyadi, Yanto, Ani, Apriani, Rofiul, Wahyudi, Cheah, Wai Shiang, Suprihatin, in, Rahmat, Hidayat

“…Conventional clustering, such as k-means, cannot be openly used to categorical data. …”
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