Ramadan Group (RG) Transform Coupled with Projected Differential Transform for Solving Nonlinear Partial Differential Equations
Mohamed A. Ramadan,
Adel R. Hadhoud
Issue:
Volume 2, Issue 2, May 2017
Pages:
39-47
Received:
7 January 2017
Accepted:
20 January 2017
Published:
21 February 2017
DOI:
10.11648/j.ajmcm.20170202.11
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Abstract: In this article a combination of integral transform method (Ramadan group transform) and projected differential transform is considered to solve partial differential equations. The method can easily be applied to many nonlinear problems and is capable of reducing the size of computational work. The fact that the suggested hybrid method solves such nonlinear partial differential equations without using He’s polynomials or Adomian’s polynomials is a clear advantage over these decomposition methods. Numerical examples are performed by this hybrid method are presented. The results reveal that the suggested method is simple and effective.
Abstract: In this article a combination of integral transform method (Ramadan group transform) and projected differential transform is considered to solve partial differential equations. The method can easily be applied to many nonlinear problems and is capable of reducing the size of computational work. The fact that the suggested hybrid method solves such ...
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Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm
Issue:
Volume 2, Issue 2, May 2017
Pages:
48-59
Received:
8 December 2016
Accepted:
19 December 2016
Published:
24 February 2017
DOI:
10.11648/j.ajmcm.20170202.12
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Abstract: This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.
Abstract: This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertensi...
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Dynamic Load Balancing Using Periodically Load Collection with Past Experience Policy on Linux Cluster System
Sharada Santosh Patil,
Arpita Nirbhay Gopal
Issue:
Volume 2, Issue 2, May 2017
Pages:
60-75
Received:
21 October 2016
Accepted:
9 January 2017
Published:
9 March 2017
DOI:
10.11648/j.ajmcm.20170202.13
Downloads:
Views:
Abstract: Fast execution of the applications achieved through parallel execution of the processes. This is very easily achieved by high performance cluster (HPC) through concurrent processing with the help of its compute nodes. The HPC cluster provides super computing power using execution of dynamic load balancing algorithm on compute nodes of the clusters. The main objective of dynamic load balancing algorithm is to distribute even workload among the compute nodes for increasing overall efficiency of the clustered system. The logic of dynamic load balancing algorithm needs parallel programming. The parallel programming on the HPC cluster can achieve through massage passing interface in C programming. The MPI library plays very important role to build new load balancing algorithm. The workload on a HPC cluster system can be highly variable, increasing the difficulty of balancing the load across its compute nodes. This paper proposes new idea of existing dynamic load balancing algorithm, by mixing centralized and decentralized approach which is implemented on Rock cluster and maximum time it gives the better performance. This paper also gives comparison between previous dynamic load balancing algorithm and new dynamic load balancing algorithm.
Abstract: Fast execution of the applications achieved through parallel execution of the processes. This is very easily achieved by high performance cluster (HPC) through concurrent processing with the help of its compute nodes. The HPC cluster provides super computing power using execution of dynamic load balancing algorithm on compute nodes of the clusters....
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