سید امیر رضا ابطحی

In recent years, credit scoring has been one of the main methods of financial institutions to assess credit risk. The main problem that limits the effectiveness of credit scoring methods is the unbalanced distribution of data, which means that in the data set, the number of samples of good customers is far more than the number of samples of bad customers. This study deals with the credit scoring of customers active in the tourism industry using an entropy-based algorithm with the aim of overcoming the problem of data imbalance, which evaluates the data in terms of the entropy of customer validation indicators and defines a criterion that can measure how good or bad a customer is by considering only the good cases of the data set and the sample of the applicants for facilities. In this research study, 204 active customers of the tourism industry of Bank Melli Iran were selected as the data set. The results showed that the entropy model has a good prediction power and is an effective model for validating customers.

Supply chain sustainability is necessary for businesses today, so organizations pay attention to social and environmental goals in addition to economic goals. Therefore, to ensure the realization of these goals, measuring the sustainability efficiency of the supply chain is considered inevitable. The problem of assessing the performance of supply chain sustainability, on the one hand, is affected by the inherent conflicts of interest among sustainability goals, which increase the complexity of the problem, and on the other hand, it should be configured in a way that can simultaneously provide sufficient knowledge of the efficiency of overall supply chain sustainability and sustainability goals. In this study, a combined mathematical model of data envelopment analysis and game theory is presented to evaluate the sustainability efficiency of the supply chain and the efficiency of sustainability goals by considering the trade-offs among sustainability goals. The trade-offs among sustainability goals are formulated using the Nash bargaining game in such a way that the ability to provide simultaneous results is related to the overall efficiency of supply chain sustainability and the efficiency of each sustainability goal. The proposed model was evaluated under a case study for appraising the efficiency of supply chain sustainability of an Iranian pharmaceutical company. The results of the model implementation indicate that the proposed model is firstly capable of simultaneously providing efficiency values of supply chain sustainability and each sustainability goal, and secondly, it provides the necessary and sufficient knowledge in comparison of these values for efficient and inefficient products.

Over the past decades, women have been more exposed to sexual discrimination than men. This is because women did not have the capabilities and facilities needed to empower  themselves and reduce poverty. Empowerment is essential for the empowerment of low-income people to achieve their rights. Therefore, it is necessary to identify and introduce womenchr('39')s empowerment methods.

The present research is applied in terms of purpose and is descriptive. The present research is a quantitative and descriptive-analytical study. This research seeks to identify and prioritize the ways to empower female-headed households with Fuzzy Delphi (F-DELPHI) and Network Analysis Process (NAP). Data collection tools were interviews and questionnaires. the interview and questionnaires were conducted in the well-being office of the  city of Savadkuh (Polsefid).

Based on the results of library studies and interviews, possible ways of empowering female-headed households were identified and categorized into three levels of person, family, and society. After distributing the questionnaire among the experts of the field and prioritizing the possible ways, the main and important ways identified were promoting social health, providing professional training, providing life skills training, creating markets, rehabilitation, providing the facilities, providing insurance and social security services, providing necessary goods, allocating more Funds for organizations to support women and offering allowances to improve individual welfare.

The results showed that personal empowerment-based ways have a higher priority than ways to empower through direct financial assistance. These ways attracted the experts attention.

One of the most important features of transportation efficiency is Reliability. Despite the risks that threaten a safe shipping in different parts, insurance can cover risks and increase reliability in this area. Insurance companies, for various reasons, including pricing correctly and avoiding irrational risks, tend to have an appropriate image of risk factors. In this study, marine shipping insurance has been investigated and it the factors influencing the risk of occurrence in the maritime transport sector and rated has been identified. Initially, risk factors were identified through prior studies, questionnaires and supplementary interviews with experts such as transport insurance managers, damage assessors and captains. Then DEMATEL method is used to identify the internal relations and the dependence of the criteria and the analytic network process (ANP) to find the weight of the criteria and the final ranking of the effective factors. Also, to take into account the uncertainty in decision making, fuzzy theory has been used. The proposed framework for identifying, ranking and prioritizing the risk factors in the shipping sector in the field of maritime shipping insurance can help insurance companies to make accurate decisions about covering these risks effectively.

In sustainable supply chain management, supplier selection based on sustainability criteria is among the most important problems. Sustainability is considered as a key element in competitive advantage. There are many methods in the field of supplier selection. Some of these methods are the analytical hierarchical process, the best-worst method, the topsis method, mathematical programming, Quality Function Deployments (QFD), and a combination of these methods. In the QFD method, the characteristics of the product to be purchased from the supplier are identified and then seek to determine the supplier's assessment criteria. In this study, the performance of four suppliers in chemical industry has been evaluated using fuzzy quality function deployments in two phases.

One of the most important pillars for improving health care services is supplemental health insurance, which increases people's access to health care. Supplemental health insurance plans, which cover health expenses above basic medical coverage, with nearly 18% of the Iranian insurance industry portfolio, plays an important role in the Iranian health system. It is worth mentioning that in addition to profitability for insurance companies, supplemental health insurance must be customer-driven. This requires more attention and exploration in the two categories of customer demand assessment and the insurance company’s limitations to meet customer’s expectations and needs. In this research, by analyzing customer’s expectations and needs using the fuzzy QFD and FMEA hybrid method, a model for designing of supplemental health insurance for treatment has been presented and risk factors faced by insurance companies have been identified and rated.

Through focusing on and blending human capabilities and environmental management programs, Will have significant impacts on improving the environment. Since education and development are considered to be the facilitator of "Green Human Resource Management", this paper based on "Green Human Resource Management" approach attempts to identify and prioritize the requirements of "green training courses" held by organizations. An applied research in its goal, this paper benefits from group survey methodology. And 14 main education requirements were identified in this research based on performing an extended review of literature. Chosen according to snowball sampling, 10 education experts in Iran form the statistical population of the research; and utilizing five-level Likert scale we gathered their opinions. Using multi-criteria decision-making techniques as well as AHP-DEA-FCE model, out the 14 requirements, 8 are identified as the most significant ones. Additionally, the main requirements for improving employee's green behavior in educational courses were prioritized according to ANP model. As a result, Sustainable development, ISO14001 and green purchasing were identified as the most important and most effective needs or training courses. Accordingly, the managers of educational courses can benefit from the findings of this research to apply "Green Human Resource Management".

Life insurances in Iran, despite following a growing trend, still constitute an insignificant part of insurance industry compared to that of the global average, or the industry vision's targeted countries. The purpose of this article is to find the roots of this problem and to determine the related dynamic mechanisms. Previous studies and interviews with experts are used as the framework to establish and simulate a system dynamics model for the problem. Analysis of the simulation results show that not only is it possible to maintain the current growing trend in the industry for the next 10 years, but also the most important factors which will have effects on the future of life insurance industry are: scale of sales network and agents, measures which affect their sales productivity, and careful management of financial resources to fulfill the generated commitments, to name but a few. According to the analyses, the future of life insurance is in the hands of its policy and decision makers rather than being controlled by exogenous variables.

In order to gain more illicit profit, some traders in the stock market try to make a targeted impact on prices by placing fake orders and false advertising. Due to the high customer population, it is not possible to discover these frauds using traditional methods. The present study seeks to provide a system for preventing the frauds in future market-trading coins based on Bayesian classifier model for Iran Mercantile Exchange. The proposed model has polynomial time complexity and high accuracy because of considering important dependencies among different features of data. The primary labeling of data has been done by Kmeans clustering. The test of model shows 94.55 percent similarity between model's output and labeled data. Using this system can helps to identify the fraudulent from non-fraudulent traders.

Traffic Asset Maintenance Management is a strategic and systematic process of operating, maintaining, upgrading and expanding physical assets effectively throughout their life cycle. It helps governments achieve high performance and safety indexes, even though they spend a tight budget. Reaching to the confidential outputs would merge dangerous points or segments. With the engineering decisions, these points could be vanished. Traffic signs are various and have several usages, such as; warning, obligation, yield sing. Majority of signs have vital role in traffic control and safety. Therefore, with this importance, traffic signs should be organized and categorized in accurate data bank Data gathering is the most important part of traffic sign inventory. This article will outline a traffic asset maintenance management model. The data gathering method is advised for achieving our purpose easily, which is accurate and useful .

The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty. In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.

This paper uses neural network to predict corrosion rate. Corrosion can not modeled easily, because of wide range of causes either known or unknown. In mechanistic approach, physical, chemical and electrochemical reactions and processes are considered to model and predict. But as stated before this models are not practically successful in prediction because of unknown parameters. This paper uses genetic optimized neural network to predict corrosion rate. Among different neural networks, multi layer neural network with gradient descent learning algorithm has been selected. After developing the network, learning process has been done, using an oil refinery'S data. Then evaluation and test have been performed. After preparing the network Garson's algorithm and sensitivity analysis have been used for knowledge extraction. According to results, neural network approach can predict corrosion rate with acceptable correlation coefficient (R) and mean squared error (MSE). Sensitivity analysis depicts the strength of each oil parameter influence on corrosion rate. Among these results, salt and sulphur are the most affecting parameters in corrosion rate.