Mathematical modelling
Farhad Shariffar; Peyman Pirmohabbati; Amir Hossein Refahi Sheikhani
Abstract
One of the fields studied in the science of heat physics is the thermoelectric phenomenon. This phenomenon is in fact the interaction between the current of electricity and the thermal properties of a system. In simpler terms, it is a phenomenon in which the direct conversion of a temperature difference ...
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One of the fields studied in the science of heat physics is the thermoelectric phenomenon. This phenomenon is in fact the interaction between the current of electricity and the thermal properties of a system. In simpler terms, it is a phenomenon in which the direct conversion of a temperature difference to voltage occurs. In this paper, we introduced a method based on the finite difference technique for solving a fractional differential equation in the field of thermal physics which describes the thermoelectric phenomena, numerically. For this purpose, we used fractional order derivatives with the definitions of Caputo, finite differences with the second order central finite-difference approach, and the first order central finite-difference. By using this method, we translate the desired differential equation to a system of nonlinear differential equations which can be solved. Finally, some numerical are used to demonstrate the effective and accuracy of the scheme. The obtained numerical results show that our proposed method is highly accurate.
Data mining
Shookoofa Mostofi; Sohrab Kordrostami; Amir Hossein Refahi; Marzieh Faridi Masooleh; Soheil Shokri
Abstract
Existing systems for diagnosing heart disease are time consuming, expensive, and prone to error. In this regard, a diagnostic algorithm has been proposed for the causes of heart disease based on a frequent pattern with the B-mine algorithm optimized by association rules. Initially, a data set of disease ...
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Existing systems for diagnosing heart disease are time consuming, expensive, and prone to error. In this regard, a diagnostic algorithm has been proposed for the causes of heart disease based on a frequent pattern with the B-mine algorithm optimized by association rules. Initially, a data set of disease is used to select a feature, so that it deals with a set of training features. Then, association rules are used to classify educational and experimental sets, and then the factors affecting heart disease are analyzed. The numerical results from the experiments of real and standard datasets of cardiac patients show that the average accuracy of the proposed method is approximately 98%, which has been tested on the Cleveland database that includes 76 features in the case of heart disease dataset, 14 features of which are related to heart disease. This paper also uses four common categories such as decision tree to build the model. The data set studied in this article contains 270 records as well as 14 features. The accuracy of predicting the results of the support vector machine classifications, k nearest neighbor, decision tree and simple Bayesian is 81.11%, 66.67%, 59.72% and 19.85%, respectively, which are relatively satisfactory results.
