The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.

Background: Healthcare statistics, issued by various international organizations, show that medical errors in health centers impose high costs on patients and hospitals and increase the rates of morbidity and mortality around the world. Due to the potential risks of cardiovascular diseases, the occurrence of any errors can potentially endanger the patients’ lives and incur costs on them, as well as hospitals. On the other hand, anesthesia is one of the priorities for risk management in clinical care. Objectives: This study aimed to identify, classify, and evaluate anesthesia failures in open heart surgeries, using the healthcare failure mode and effects analysis (HFMEA) technique. Methods: The anesthesia process in open heart surgery was reviewed using the HFMEA technique, and four processes, 25 sub-processes, 95 activities, and 204 risks were extracted. The causes of failure were also identified, and four failure modes were determined as the most important failures, based on the qualitative and quantitative methods; finally, some solutions were proposed. Changes in the level of healthcare workers’ knowledge and competence, computer use and timing, and the amount of administered medications were identified as the potential risk factors and errors. Results: The inadequate awareness and knowledge of healthcare workers, non-use of computers, prescription errors, technique errors, and timing and amount of medication administration were identified as the errors and risk factors. Based on the present findings, another expert needs to evaluate the design, feasibility, and prioritization of techniques, including continuing medical education for anesthesia professionals and experts, statutory documentation, and control of the individuals’ activities. Conclusions: Based on the present findings, establishing a risk management committee seems essential to identify errors and improve the design and plan of different techniques so as to execute, monitor, control, and review errors in a cycle of continuous improvement.

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This paper presents a systematic approach for evaluating the performance of a project based organization. We applied a two level fuzzy Data Envelopment Analysis (DEA) technique in project based organizations. In order to determine the required inputs and outputs, important indicators have selected using both expert judgments and statistical analysis. Then the two-level DEA model is successfully adapted. In this model by considering the outputs through a hierarchical process, a large number of sub indicators have provided and then rolled up to the higher level. Since the exact amount cannot be attributed to the indicators and they includes interval of values during the project life cycle, the interval DEA model will be discussed as a model help to determine the most preferred solution. At the end, some of the projects have been successfully evaluated throughout the approach proposed in this paper.

Today with swift growing of plastic cards industry in the world, variety and volume of data stored in the database is growing strongly, this issue reminds the growing need of banks and financial institutions in applying knowledge discovery processes on value creation services. The original approach of this paper, is step by step implementing process of data mining in real-life transaction of debit cards, with the aim of customer profiling. In this study profiling is applied with two approaches of explorative and predictive analysis. In explorative model SOM and TwoStep clustering techniques are used. Also in predictive model four decision tree techniques are applied, the C5.0, Chi-square Automatics Interaction Detection (CHAID), Quest, classification and regression. Finally, the optimal models details are more analyzed to discover the knowledge in transactions done.