علی محمدی

In recent years, industrial clusters have received considerable attention from economists and industry analysts because they are seen as the main reason for certain economic regions' economic growth and success. For many Industrial States Organization, the selection of industrial clusters has become a critical strategic consideration due to the Budget allocation priority. In this paper, an extended qualitative flexible multiple (QUALIFLEX) methods is used to solve problems regarding the priority among this cluster using probability hesitant fuzzy information, which can lead to allocating the budget for industrial clusters more effectively. For more accuracy, we have applied a Hesitant fuzzy Topsis for prioritizing. Both rankings have been aggregated by the Copeland method. From our research results, the Larestan Muscat is of great importance, and Abade Inlaid Wood, Citrus packaging, Shiraz Marquetry, and Niriz stone have ranked respectively.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.