Search Results - (( normal distribution methods algorithm ) OR ( parallel distribution learning algorithm ))

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. …”

Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Wooi, Ping Cheah

“…In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. …”
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Grid portal technology for web based education of parallel computing courses, applications and researches by Alias, Norma, Islam, Md. Rajibul, Mydin, Suhaimi, Hamzah, Norhafiza, Safiza Abd. Ghaffar, Zarith, Satam, Noriza, Darwis, Roziha

“…This paper proposes the web service education technology for postgraduate parallel computing course, e-learning students, real-time solutions and for supervising projects related to the application of parallel computing, that focuses on the fundamental principles to parallel computer architecture, multimedia, communication cost, master-worker model, parallel algorithm, web services and performance evaluations. …”
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Parallel CFD Simulations of Multiphase Systems: Jet into a Cylindrical Bath and Rotary Drum on a Rectangular Bath. by Hasan, Nurul

“…For commercial CFD packages, in many cases the solution algorithms are black boxes, even though parallel computing helps in many cases to overcome the limitations, as shown here. …”
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Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms by Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

“…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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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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Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

“…Statistical approaches which use the Standard Deviation and conditional Bayesian methods are considered to assign the grades. In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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Identification of non-equilibrium growth for bitcoin exchange rate: mathematical derivation method in Islamic financial engineering by Rosbi, Sofian, Abu Bakar, Nashirah

“…Graphical method indicates the first difference of data distribution is a non-normal distribution. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…One of the important assumptions of the linear model is that the error terms are normally distributed. Unfortunately, many researchers are not aware that the performance of the OLS can be very poor when the data set that one often makes a normal assumption, has a heavy-tailed distribution which may arise as a result of the presence of outliers. …”
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Dynamic robust bootstrap method based on LTS estimators by Midi, Habshah, Uraibi, Hassan Sami, Al-Talib, Bashar Abdul Aziz Majeed

“…In order to make reliable inferences about the parameters of a model, require that the parameter estimates are normally distributed. Nevertheless, in real situations, many estimates are not normal and the use of bootstrap method is more appropriate as it does not rely on the normality assumption. …”
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Statistical approach on grading the student achievement via normal mixture modeling by Md. Desa, Zairul Nor Deana, Mohamad, Ismail, Mohd. Khalid, Zarina, Md. Zin, Hanafiah

“…Statistical approaches which used the Standard Deviation (GC) and conditional Bayesian methods are considered to assign the grades. In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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A modified artificial neural network (ANN) algorithm to control shunt active power filter (SAPF) for current harmonics reduction by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Mohd Radzi, Mohd Amran, Mailah, Nashiren Farzilah

“…The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. …”
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The effect of dose calculation algorithms on the normal tissue complication probability values of thoracic cancer by Ahmad, Noor Ashikin

“…Purpose: To identify the effect of dose calculation algorithms on the Normal Tissue Complication Probability values of thoracic cancer. …”
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Statistical approach on grading the student achievement via mixture modelling by Md. Desa, Zairul Nor Deana, Mohamad, lsmail

“…In the conditional Bayesian model, we assume the Normal Mixture distribution where the grades are distinctively separated means and proportions of the Normal Mixture distribution. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah by Abdullah, Mohd Azrul

“…Anderson-Darling (AD) and Goodness of Fit test is used to identify the best fitted distribution model to the real data. Four traffic distribution which are normal, lognormal, Weibull and exponential distribution are fitted and derived. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Thus, using neural network-based semi-supervised stream data learning is inadequate due to capture the changes in the distribution and characteristics of various classes of data while avoiding the effect of the outdated stored knowledge in neural networks (NN). …”
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Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia by Adli Zakaria M.N., Ahmed A.N., Abdul Malek M., Birima A.H., Hayet Khan M.M., Sherif M., Elshafie A.

“…Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. …”

Omega grey wolf optimizer (ωGWO) for optimization of overcurrent relays coordination with distributed generation by Noor Zaihan, Jamal

“…The overall protection coordination is thus very complicated and could not be satisfied using the conventional method moreover for the modern distribution system. …”
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