درخت حوزه‌های تخصصی

The purpose of this work is to use structural analysis to create believable scenarios of Iranian food tourism. In the present study, to formulate plausible scenarios, first by reviewing the literature and interviewing experts, 37 factors affecting food tourism were extracted. The variables affecting the future of food tourism, directly and indirectly, were calculated using a questionnaire and analysis by MICMAC software. Finally, considering the two most critical factors called food festivals and sanctions/international relations, plausible scenarios for the future of Iranian food tourism were developed. Based on the results, four scenarios are presented. These scenarios include Golden Scenario, Limited Scenario, Neglected Scenario and Chaotic Scenario. In Golden scenario, which depicts the ideal conditions, as many suitable food festivals as possible are being held in a country, and sanctions are not imposed. According to Limited Scenario, holding more and better food festivals is on the officials’ agenda, but on the other hand, the country is under sanctions and pressure. In Neglected Scenario, holding festivals is the same as in the past, and the country is not sanctioned. In Chaotic Scenario, depicting the worst-case scenario, the festival process is the same as in the past and has not been developed quantitatively and qualitatively. Also, the country is in a problematic situation of sanctions. Creating Credible The industry's major players and beneficiaries greatly benefit from Iranian food tourism scenarios, which enable them to provide flexible preparation-based programmers in response to shifting circumstances. The techniques and factors that influence the growth of the business will be known to the participants and recipients of food tourism, and this will open up a multitude of opportunities for the players involved.

Most real-time speech signals are frequently disrupted by noise such as traffic, babbling, and background noises, among other things. The goal of speech denoising is to extract the clean speech signal from as many distorted components as possible. For speech denoising, many researchers worked on sparse representation and dictionary learning algorithms. These algorithms, however, have many disadvantages, including being overcomplete, computationally expensive, and susceptible to orthogonality restrictions, as well as a lack of arithmetic precision due to the usage of double-precision. We propose a greedy technique for dictionary learning with sparse representation to overcome these concerns. In this technique, the input signal's singular value decomposition is used to exploit orthogonality, and here the ℓ1-ℓ2 norm is employed to obtain sparsity to learn the dictionary. It improves dictionary learning by overcoming the orthogonality constraint, the three-sigma rule-based number of iterations, and the overcomplete nature. And this technique has resulted in improved performance as well as reduced computing complexity. With a bit-precision of Q7 fixed-point arithmetic, this approach is also used in resource-constrained embedded systems, and the performance is considerably better than other algorithms. The greedy approach outperforms the other two in terms of SNR, Short-Time Objective Intelligibility, and computing time.

The objective of this study is to present a framework to increase the return and profitability and reduce credit risk of Mellat Bank customers by developing the RFM model. In this study, which was conducted as a case study in Mellat Bank of Iran, first the variables of RFM model were identified. In the next step, relevant weights of RFM variables were calculated using AHP technique. In the next step, using the K-means algorithm, customers were clustered based on weighted RFM and extended RFM. The result included customer clusters. The results indicated that the three clusters 5, 1, and 7 obtained the highest scores for receiving facilities and the coefficients for receiving facilities were equal to 0.271, 0.173, and 0.556, respectively. By determining the facility coefficient for the cluster and consequently for the customers presented in these top groups, granting facility becomes more transparent and more purposeful, and therefore, it will help the company increase profitability, reduce the churn among high-efficiency customers, and create value for customers. This research demonstrates a systematic method for granting facilities to recognize the true value based on the capability and prevention of arbitrary acts