مطالب

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.

INTRODUCTION: The increase in the number of natural disasters has made different societies constantly discover and invent solutions to control the adverse effects of disasters with more emphasis on prevention or minimize them by improving the quality of the rescue process (in the response phase). In the last few decades, the need for social participation in order to manage risks has been developing in the form of community-based activities. This study was conducted to present a community-based crisis management model focusing on the process of relief and rescue in natural disasters in Iran, 2020. METHODS: This qualitative study was performed based on the grounded theory method defined by Strauss and Corbin. The statistical population of the study included all crisis management and non-governmental organizations experts in universities, the Crisis Management Organization, general managers of provincial crisis management, and senior managers of the Red Crescent Society. The samples were selected using the purposive and theoretical method of snowball sampling while taking into account the geographical, climatic, cultural, social, and religious diversity of different regions of the country. As a result, the sample size was obtained at 22 people. The required data were collected through a semi-structured interview. In the analysis of qualitative information, open coding, axial coding, and selective coding were used manually). FINDINGS: The results of the study showed that the causal conditions of community-based crisis management included "legal obligation", "strategic planning", "attracting public participation", "forming and developing expert teams", and "acting (prevention, preparedness, needs analysis, planning, and response)"; and contextual conditions consisted of "geographical conditions", "social capacity building", and "creating cohesion and empathy". Moreover, intervening conditions involved "general sensitization", "preparation", and "logistics and equipment"; strategies included "risk management", "human resource management", "training (general and professional)" and "barrier removal"; and the consequences of community-based crisis management consisted of "increasing public awareness in the face of emergencies", "reducing the risk of areas "increasing community resilience", "improving the quality of post-disaster assessment", "accelerating the response process", "determining priorities appropriately", "saving the golden time of rescue", "increasing the number of survivors", "accelerating relief operations" "distributing relief items fairly", "improving the quality of rescue operations", "decreasing the amount of damage", "managing resources and facilities properly", "reducing response costs", "decreasing the adverse psychological effects of the disaster", "organized presence of grassroots groups", "eliminating the shortcomings and completing the performance of the responsible agencies", and "increasing the level of public satisfaction". CONCLUSION: Community-based crisis management is a new and effective approach that its implementation has a positive impact on increasing resilience and thus reducing the vulnerability of communities. The proper and successful implementation of the community-based approach requires structural changes, amendment of laws, and development of relevant bylaws. It also needs measures that should be implemented at the national level, in the country's crisis management system, and at the local level.

Heart disease pretends great danger to people, as heart disease has recently become a dangerous disease that acts as a threat to humans. It usually affects all groups from young to old. The biggest challenge in this paper is data pre-processing and discovering a solution to the failure of records Clinical heart, where an effective high-performance model is proposed to enhance heart disease and treat failure in the clinical heart failure records. The current authors applied the techniques of clustering with k-means, expectation-maximization clustering, DBSCAN, support vector clustering, and random clustering herein. Using cluster techniques, we gained good enough results for significantly predicting and improving the performance of heart disease. The goal of the model is a suggestion of a reduction method to find features of heart disease by applying several techniques. Our most important results are to predict faster and better. It indicates that the proposed model is excellent and gives excellent results. This model demonstrated a great superiority over its counterparts through the results obtained in this research. We obtained some values of 130, 980, 183, 125.133, 133, 203, and 125.800. It confirms that this model will predict significantly and improve the performance of the data that we have worked on this.