درخت حوزه‌های تخصصی

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets.

The reconstruction of 3D images is always a difficult task for the researchers. The 3D reconstruction of the image is a core technique of various fields such as Computer graphics, computer vision, CAD systems, medical science, computer application, etc. Reconstruction of the 3D image allows us to gather the quantitative features of the objects such as the shape, size, and volume of the objects. The existing computer algorithms need spatial dimension information to make the distinguished inference from the given 3D image which is not always possible. This paper simplifies the 3D reconstruction of the image. This research paper introduced a novel algorithm for the representation of the Three Dimensional images into a textual form.  The syntactic approach is used for the extraction of the features of the image and these are called knowledge vectors. The knowledge vector consists of the direction information and length information. This a new approach in the field of image processing where images can be represented as a knowledge vector and it could be a great contribution in the field where security is a major concern. Further, the knowledge vector is used for the reconstruction of the 3D image. The performance of the algorithm is evaluated on the PASCAL 3D + and example-based Synthesis of the 3D Object Arrangements dataset. According to the obtained results, the proposed methodology is having better accuracy, and the processing time of reconstruction of the original 3D image is 1.02 Seconds. Single-pass is sufficient for reconstructing the original image

The aim of this study is to develop a conceptual model for purchase intention of foreign food products in the Iranian context. Based on an in-depth review of past literature, the sub-factors related to customer’s purchase intention were extracted. Then, exploratory factor analysis and confirmatory factor analysis were applied to identify and confirm the factors affecting purchase intention of foreign food products. The obtained results illustrate the thirteen factors as follows: “corporate social responsibility”, “customer knowledge and awareness”, “perceived risk”, “retailer’s commercial image”, “customer’s personality characteristics”, “social identity”, “product features”, “attitude”, “country-of origin”, “perceived value”, “subjective norm”, “loyalty”, and “perceived behavioral control”. The proposed model could contribute to international food companies and retailers to understand customers’ expectations and to gain more market share in Iran. Designing a conceptual model for purchase intention of foreign food products in Iran based on a comprehensive set of the effective factors, developing the theoretical literature in the field of the factors affecting customer’s purchase intention, and presenting valuable results for academicians, marketing managers, and retailers.