Search Results - normal distribution model algorithm
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1
Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. The percentage level 5% under original bandwidth used is developed on policing and shaping algorithms to control bandwidth used. …”
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2
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…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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3
Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution: article / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fitted the best 2-parameter CDF compared to WeibuII, Normal and Rician distribution model. The percentage level 5% under original bandwidth used is developed on policing and shaping algorithms to control bandwidth used. …”
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4
A study on advanced statistical analysis for network anomaly detection
Published 2005“…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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Monograph -
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Development of classification model between clean water and polluted water based on capacitance properties using Levenberg Marquardt (LM) algorithm of artificial neural network / M...
Published 2020“…For both cases, the statistical analysis data show that the p-value is more than 0.05, which indicates that the data are normally distributed. These measurement inputs were then going through the process of classification in ANN to generate the optimized models by using LM algorithm. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…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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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…One of the important assumptions of the linear model is that the error terms are normally distributed. …”
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8
Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023“…This implementation will be based on parallel distributed model, which will reduce the complexity of each microcontroller to solve large complex problem and increase problem solving speed. � 2008 IEEE.…”
Conference paper -
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic: article / Aini Azmi
Published 2016“…Four best traffic model is identified which are Extreme Value, Weibull, Normal and Rician traffic model. …”
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic / Aini Azmi
Published 2016“…Four best traffic model is identified which are Extreme Value, Weibull, Normal and Rician traffic model. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…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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12
Statistical approach on grading: mixture modeling
Published 2006“…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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13
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model
Published 2013“…The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. …”
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Broken Conductor Detection on Power Distribution Feeder
Published 2013“…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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The effect of dose calculation algorithms on the normal tissue complication probability values of thoracic cancer
Published 2015“…The NTCP values of the LKB model are slightly higher than RS model for both cases even though using different kind of algorithm. …”
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Monograph -
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High Impedance Fault Detection on Power Distribution Feeder
Published 2012“…This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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Entropy in portfolio optimization / Yasaman Izadparast Shirazi
Published 2017“…The purpose of this new model is to overcome the limitations as observed in a traditional model; that is, having performance close to Markowitz’s mean-variance (MV) model when data comes from a normal distribution, but exhibit better performance when data comes from a non-normal distribution. …”
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Modeling The Modified Internal Rate Of Return (Mirr) For Long-Term Investment Strategy By The Assumption Of Gamma Distribution
Published 2023“…It offers greater flexibility compared to the commonly used normal distribution.…”
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Identification of non-equilibrium growth for bitcoin exchange rate: mathematical derivation method in Islamic financial engineering
Published 2017“…Graphical method indicates the first difference of data distribution is a non-normal distribution. Then, Shapiro-Wilk statistical test confirmed that first difference of data distribution deviate from normal distribution. …”
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Statistical approach on grading the student achievement via normal mixture modeling
Published 2006“…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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