Math. Comput. Appl., Volume 13, Issue 3 (December 2008) – 7 articles , Pages 137-202

Data mining methodologies have been developed for exploration and analysis of large quantities of data to discover meaningful patterns and rules. This paper presents a new approach, that employs data mining, to find association rules an effective method for discovering Hyperlipidemia. The propose system has been projected from the biochemistry blood parameters which will be very helpful for and will make everything easier for the physicians in the diagnosis of Hyperlipidemia. The basic characteristic of the lipide parameters that is Total cholesterol, LDL, Triglyceride, HDL and VLDL parameters are used in the process of entering the system and finally Hyperlipidemia (T) and Hyperlipidemia (F) results have been evaluated at the end of this process. Data of 492 patients are evaluated in the projected system. The results of the decision support system have completely matched with those of the physicians decisions.

In this study, Artificial Neural Network approach to prediction of diffusion bonding behavior of Ni-Ti alloys, manufactured by powder metallurgy process, were obtained using a back-propagation neural network that uses gradient descent learning algorithm. Ni-Ti composite manufactured with a chemical composition of 51 % Ni – 49 % Ti in weight percent as mixture with a average dimension of 45μm. Diffusion welding process have been made under argon atmosphere, with a constant load of 5 MPa, under the temperature of 850, 875, 900 and 925ºC and, in 20, 40 and 60 minutes experiment time. Microstructure examination at bond interface were investigated by optical microscopy, SEM and EDS analysis. Specimens were tested for shear strength and metallographic evaluations. After the completion of experimental process and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. In neural networks training module, different temperatures and welding periods were used as input, shear strength of bonded specimens at interface were used as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of diffusion bonding shear strength and behavior.

This paper presents a sensitivity investigation of the expected busy period for a controllable M/G/1 queueing system by means of a factorial design statistical analysis. We studies the effect of four important factors (parameters) that influence the expected busy period of an M/G/1 system, in which the server operates <p,N>-policy in his idle period. A 2⁴ factorial experimental design is used to evaluate the sensitivity analysis of parameters on the expected busy period of a controllable M/G/1 queue. Based on the analysis of variance, we find the main effect and interaction effect of the significant factors on the system characteristics.

This paper presents a multiobjective fuzzy genetic algorithm optimization approach to design the submersible induction motor with two objective functions: the full load torque and the manufacturing cost. A multiobjective fuzzy optimization problem is formulated and solved using a genetic algorithm. The optimally designed motor is compared with an industrial motor having the same ratings. The results of optimal design show the reduction in the manufacturing cost, and the improvement in the full load torque of the motor.

In this paper an iterative method for solving linear fraction programming (LFP) problem is proposed, it will be shown that this method can be used for sensitivity analysis when a scalar parameter is introduced in the objective function coefficients and our task of this sensitivity investigation is to maintain the optimality of the problem under consideration. A simple example is given to illustrate this proposed method.

This study uses a mathematical method to develop an efficient rule for expediting scrap-or-rework decision making in economic production quantity (EPQ) model with failure-in-repair. The expected overall production-inventory costs and the optimal lot sizes for EPQ models with/without rework process are derived and compared. With straightforward numerical derivations, this paper develops an efficient rule including the exact critical value of unit repair cost for each reworked item and its approximation equations, to assist in determining whether it is beneficial to rework the repairable items. Numerical example with sensitivity analysis and discussion are provided to demonstrate their practical usages.