Search Results - higher order compact ((window algorithm) OR (_ algorithm))

Application Of Higher Order Compact Finite Difference Methods To Problems In Fluid Dynamics by Yap, Wen Jiun

“…Numerical algorithms based on higher order finite difference schemes that can achieve fourth-order accuracy in space have been developed. …”
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The compact genetic algorithm for likelihood estimator of first order moving average model by Al-Dabbagh, R.D., Baba, M.S., Mekhilef, Saad, Kinsheel, A.

“…In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). …”
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Adaptive firefly algorithm for hierarchical text clustering by Mohammed, Athraa Jasim

“…In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. …”
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New Quasi-Newton Equation And Method Via Higher Order Tensor Models by Gholilou, Fahimeh Biglari

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A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

“…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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Neural networking analysis of thermally magnetized mass transfer coefficient (MTC) for Carreau fluid flow: A comparative study by Ur Rehman, Khalil, Shatanawi, Wasfi, Asghar, Zeeshan, Abdul Rahman, Mohd Kasim

“…The compact equations are further solved by the shooting method. …”
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Empirical relationship on vortex formation and heat transfer coefficient (HTC) using synthetic jet cooling / Nawal Radhiah Mohamad Nasir by Mohamad Nasir, Nawal Radhiah

“…The solver used is Semi-Implicit Method for Pressure-Linked Equations (S.I.M.P.L.E) algorithm meanwhile the turbulence model that has been used wasK-co SST. …”
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Multi-directional matrix converter for low power application using field-programmable gate array by Toosi, Saman

“…The main emphasis in proposed full-silicon MDMC structure was minimizing the total number of bidirectional switches and subsystems to achieve higher operating efficiency and more compact designs, and to reduce weight and volume of the resultant systems. …”
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