Math. Comput. Appl., Volume 17, Issue 3 (December 2012) – 7 articles , Pages 182-246

Several vibration analysis procedures are used for determination of the level of bone loss, status of implant stability, modal damping factor and numerous other properties of tissues. The detection methods of bone properties are to compare the results of theoretical work with practical results. So, there are many options for processing of image data and establishing the finite element (FE) model that differentiation of calculated outputs is inevitable. Uncertainty of outputs can lead to mistakes while mechanical parameters or behaviors of tissue are determined. In this study, the effect of Micro-CT scanning intensity in connection with the reconstruction process on properties of the modal behavior of bone tissue were investigated. Results have shown that examined parameters have important effects on numerical values of the natural frequencies and modal behaviors. Furthermore, it has been revealed that numerical values and mode shapes must be considered together for properly understanding the natural vibration analysis of bone tissue. Full article

In this study, a fuzzy robust regression method is proposed to construct a model that describes the relation between dependent and independent variables in insurance. Fuzzy robust regression suggested as an alternative to not only ordinary least squares but also classical robust regression. Fuzzy robust regression is finally investigated and discussed by an example with real data arose from a well-known insurance company in Turkey. Full article

The major task of medical science is to prevent or diagnose disease. Medical diagnosis is usually made by using some blood metrics and in addition, to be able to reach better results, one can benefit from different scientific methods. In this paper a Bayesian network method is proposed. This method is a hybrid that uses simple correlation and according to dependent variable type either simple linear regression or logistic regression for constructing a Bayesian topology. The Bayesian network is a method for representing probabilistic relationships between variables associated with an outcome of interest. To develop a Bayesian network, a structure must first be constructed. To build the topology of the Bayesian network, some alternative method can be used. One is using domain experts who usually have a good grasp of the conditional dependencies in the domain to develop the structure of the Bayesian network. Another is using structure learning algorithms, such as genetic algorithms, to construct the network topology from training data. In this paper a different construction method is proposed by using correlation analysis and one of the simple linear regression or logistic regression analyses. First, correlations of the examined variables are found. Then according to the significant correlation coefficients, the degree and direction of the interactions between these variables are established by using either simple linear regression or logistic regression. Finally the Bayesian network model is constructed by using this information. For evaluating our model, another model which does not have any relation between the input variables is also constructed. And these two models are compared by using an original thyroid data set. It is concluded that our proposed model provides a high degree of performance and good explanatory power and it may prove useful for clinicians in the medical field. Full article

In this paper, combined Laplace transform–Adomian decomposition method is presented to solve differential equations systems. Theoretical considerations are being discussed. Some examples are presented to show the ability of the method for linear and non-linear systems of differential equations. The results obtained are in good agreement with the exact solution and Runge-Kutta method. Full article

Data Envelopment Analysis (DEA) provides a method to evaluate the relative efficiency of peer Decision Making Units (DMUs) that have multiple inputs and outputs. Production process in two-stage DEA is performed in the two consecutive phases and DMUs have intermediate measures, in addition to their inputs and outputs. A unique feature of the intermediate measures is that the outputs in the first stage are being treated as inputs in the second stage. The aim of this paper is to determine the returns to scale (RTS) classification and scale elasticity (SE) in two-stage DEA. Therefore an approach is introduced for estimating the RTS situation of DMUs with two-stage structure based on the consideration of SE quantity in each of the individual stages. The utilization of the proposed approach is demonstrated with a real data set. Full article

The purpose of this study was to investigate the relationship between travel reduction and tractive performance and to illustrate how artificial neural networks (ANNs) could play an important role in the prediction of these parameters. The experimental values were taken in a soil bin. A 1-4-6-2 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the tractive performance of a driven tire in a clay loam soil under varying operating and soil conditions. The input parameter of the network was travel reduction. The output parameters of the network were net traction ratio and tractive efficiency. The relationships were investigated using non-linear regression analysis and ANNs. The performance of the neural network-based model was compared with the performance of a non linear regression-based model using the same observed data. It was found that the ANN model consistently gave better predictions compared to the non linear regression-based model. Based on the results of this study, ANNs appear to be a promising technique for predicting tire tractive performance. Full article