Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external envir...Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and not in others. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper focuses on the same factors to assess the impact of the macroeconomic indicators （extemal factors） on the parameters of static models predicting a local approximation of the crisis at the plant. To accomplish the purpose, a Spark set of 100 companies was compiled, including 50 companies which officially declared bankruptcy in the period of 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive1 The author compared two data sets： （1） microeconomic indicators--money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes （EBIT） to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, and current assets to revenue; and （2） external factors--index of real gross domestic product （GDP）, industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard ＆ Poor （S＆P） index, the Russian Trading System （RTS） index, and region. The aim of the comparison results paging classes ＂insolvent＂ and ＂non-bankrupt＂ is achieved using two methods： classification and discrimination. In both methods, computational procedures are realized with the use of algorithms linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors, impedes models about two times.展开更多
Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, an...Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search（GS）, which is a search based on a genetic algorithm（GA）, to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na？ve Bayes（NB）, functional trees（FT）, J48, random forest（RF）, and multilayer perceptron（MLP）. Among these classifiers, FT gave the highest accuracy（95%） and true positive rate（TPR）（96.7%） with the use of only six features.展开更多
文摘Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and not in others. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper focuses on the same factors to assess the impact of the macroeconomic indicators （extemal factors） on the parameters of static models predicting a local approximation of the crisis at the plant. To accomplish the purpose, a Spark set of 100 companies was compiled, including 50 companies which officially declared bankruptcy in the period of 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive1 The author compared two data sets： （1） microeconomic indicators--money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes （EBIT） to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, and current assets to revenue; and （2） external factors--index of real gross domestic product （GDP）, industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard ＆ Poor （S＆P） index, the Russian Trading System （RTS） index, and region. The aim of the comparison results paging classes ＂insolvent＂ and ＂non-bankrupt＂ is achieved using two methods： classification and discrimination. In both methods, computational procedures are realized with the use of algorithms linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors, impedes models about two times.
基金supported by the Ministry of Science,Technology and Innovation of Malaysia,under the Grant e Science Fund(No.01-01-03-SF0914)
文摘Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search（GS）, which is a search based on a genetic algorithm（GA）, to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na？ve Bayes（NB）, functional trees（FT）, J48, random forest（RF）, and multilayer perceptron（MLP）. Among these classifiers, FT gave the highest accuracy（95%） and true positive rate（TPR）（96.7%） with the use of only six features.
