Considering the importance of VALIDATION of customers in the CROSS-dock and since this is one of the problems of implementing CROSS-dock system in Iran, this study attempted to extract customer VALIDATION criteria. The purpose of the research is to eliminate the distrust of distributors in receiving the funds of the sent items and the statistical sample of this research is the experts of the system of distribution of goods and VALIDATION, indicators were collected by using Delphi method and questionnaire and AHP method was used to calculate the weight and the rank of indexes.

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating CROSS-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include CROSSterm, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the minimum CROSS entropy (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.

Background and purpose: Intelligent methods such as artificial neural networks (ANN) have been recently used as an efficient model for prediction and classification of tumors. Diagnosis of benign and malignant breast tumors based on morphological, clinical and demographic features without using invasive paraclinical methods is very important. The aim of this study was to provide a neural network model to predict the status of breast tumors and compare its efficacy with the common regression model.Materials and methods: In this study, Wisconsin breast cancer database was used. It was obtained from cytology results of the breast tumors of 683 patients. In the proposed model different features such as clump thickness, uniformity of cell size, uniformity of cell shape, etc. were used as input variables. We applied the genetic algorithm (GA) for determination of the best structure and training of multi-layer NN model was implemented in MATLAB. The performance of proposed NN model was compared appling logistic regression (LR) in SPSS.5-fold CROSS VALIDATION was used for accurate calculation of the performance of the models.Results: The results found GA capable of determining the best structure for a multi-layer NN that could also train it properly. In different performances the best NN structure was NN (9-8-6-1) with an average accuracy, sensitivity, specificity, and AUC (area under ROC curve) of 0.971, 0.988, 0.962, and 0.9955, respectively, while the values of the corresponding parameters for LR were 0.968, 0.975, 0.964 and 0.9954, respectively.Conclusion: The achieved ANN model could be used as a method with high sensitivity and specificity alongside common non-invasive diagnostic methods as a diagnosis support system to identify benign and malignant breast tumors.