جستجوی مقالات مرتبط با کلیدواژه « هوش مصنوعی » در نشریات گروه « صنایع »

In this study, the usual kriging method as a linear statistical estimator and two intelligent methods of artificial neural network ANN and adaptive neural fuzzy inference system ANFIS were evaluated in predicting the amount of electric conductivity and nitrate in groundwater. In order to conduct studies, nitrate concentration in 40 wells in Lanjanat plain of Isfahan was measured by spectrophotometer and electrical conductivity. The input data of the artificial neural model, including the length and width of the geographies, the nitrate concentration, and the electrical conductivity value were determined as the output of the model. In order to investigate the performance and efficiency of artificial intelligence models in predicting qualitative information, qualitative information of 50% of the wells was used for calibration and 50% of the wells were used for validating the models. Finally, the output of the models was compared with the value measured in the observation wells based on the mutual error evaluation criteria. The results showed that the ANFIS model performed better than the other two interpolation models in predicting the value of electrical conductivity and nitrate, respectively, with the root mean square error (RMSE) and (mg/l) of 5.362, with the mean bias error (MBE) 2.365 with a correlation coefficient (R) of 0.767. Also, the ANN model had far better results than the usual kriging method. Based on this, ANFIS model is proposed for spatial prediction of electrical conductivity and nitrate in the study area.

Delisted companies, despite their importance in the economic and social issues of society, is less considered in the financial literature. This issue is important because for each country, one of the criteria for economic measurement is the size of the capital market. Therefore, the delisted companies not only destroys the company's reputation, its stock price and the market for the sale of its shares, but also affects the growth of the market and the economy of each country. The present study seeks to review the financial statements and audit reports of active companies and compare it with delisted companies to design a model for forecasting delisted companies in the Tehran Stock Exchange with the help of artificial intelligence modeling techniques.

In this study, which was conducted on companies of the Tehran Stock Exchange, data related to three years before the delisting of 73 companies removed from the stock exchange from 2003 to 2019 in the first group and data of 148 active companies that are continuously. They were present in the stock market in the second group and were selected by systematic elimination method. Then, with data mining techniques, which are among the most efficient and up-to-date models of artificial intelligence, and with the help of multi-layered perceptron neural network classifiers, decision tree, and Bayesian theory classifiers, stock delisted companies have been predicted.

The findings show that the Bayesian classifier had the best performance and the multilayer perceptron neural network was in the second place and the decision tree classifier was in the third place.

Originality/Value:

 Little research has been done in the field of predicting delisted companies from the Iran capital market. This study by filling this gap, suggests to researchers to use other classifiers, combine several classifiers together to better cover the errors of each, combine classifiers with each other and weigh in a way that is more accurate, add other variables influential in the dismissal of companies, including the ownership structure and shareholder composition can have other results.

The emergence of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) in China on December 2019 has led to a global outbreak of COVID-19, which spread worldwide and became an international public health issue. People all over the world must fight in this unexpected battle and the part each individual plays, is important. The health care system has done an excellent job, and the government has done various actions to help the community control the virus spreads as well. On the other hand, in most cases people help to improve the situation alongside the policies. However, the role of information technology in helping various social institutions to fight COVID-19 is hidden and is not well appreciated. The purpose of this study is to discover the hidden role of information technology (IT) that ultimately helps to control the epidemic. Research has shown that strategies for using potential technologies can be beneficial. These IT strategies can also be tailored either to control the epidemic or to support community exclusion during the epidemic which in turn, helps control the spread of infection. This article sheds light on the impact of various technologies that help health care systems, government and public in different aspects to fight COVID-19. In addition to the technologies implemented, this paper also deals with the potential unexpected technologies that can be effective in controlling epidemic conditions for future applications. It has also tried to provide IT-based solutions to deal with the disease epidemic as well.

The emergence of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) in China on December 2019 has led to a global outbreak of COVID-19, which spread worldwide and became an international public health issue. People all over the world must fight in this unexpected battle and the part each individual plays, is important. The health care system has done an excellent job, and the government has done various actions to help the community control the virus spreads as well. On the other hand, in most cases people help to improve the situation alongside the policies. However, the role of information technology in helping various social institutions to fight COVID-19 is hidden and is not well appreciated. The purpose of this study is to discover the hidden role of information technology (IT) that ultimately helps to control the epidemic. Research has shown that strategies for using potential technologies can be beneficial. These IT strategies can also be tailored either to control the epidemic or to support community exclusion during the epidemic which in turn, helps control the spread of infection. This article sheds light on the impact of various technologies that help health care systems, government and public in different aspects to fight COVID-19. In addition to the technologies implemented, this paper also deals with the potential unexpected technologies that can be effective in controlling epidemic conditions for future applications. It has also tried to provide IT-based solutions to deal with the disease epidemic as well.

To reduce of fossil fuels and increasing global pollution the development of new and renewable energies is growing. Among renewable energies wind energy is one of the most affordable and permanent. According to the forecast of the year 2020 about 10 percent of world electricity produced from wind energy. Among these are wind turbines most applications in systems Convert wind energy into electrical energy. Hence it is that try as much as possible wind turbines in front of the wind speeds of different and mechanical stress caused by the high speed of the wind that causes destruction or fractures of the turbines are to be controlled and protected. On the other hand the maximum electric power production at low wind speeds will also be considered. This paper presents an intelligent control methods for overcoming the effect of changes in wind speed on the parameters wind turbines and them controller and optimization output power wind turbine provides.

Internet of Things (IoT) as an emerging technology in the field of Information and Communication Technology is the next revolution related to the Internet application. IoT focuses on the communication of things such as sensors, drivers, devices, etc., with the potential to collect data and control or communicate remotely rather than focusing on the communication of people. Applying intelligence solutions and new technologies of IoT in agriculture can pave the way to a new paradigm of farming called “Smart Agriculture” by making fundamental change in all aspects of current practices. IoT-based Smart Agriculture can improve agricultural productivity with more food production through the optimal utilization of the resources, minimizing environmental impacts, reducing the costs, and increasing the incomes with linking to the business market that facilitates sustainable agricultural development. IoT-based data is a collection of large data called “Big Data” that cannot be processed and managed by traditional databases and conventional management tools. IoT and Big Data technologies are interconnected and it can be predicted that the future of optimal agriculture in the world would not be possible to meet the food demand and sustainability of production without these technologies and Smart Agriculture. This article introduces IoT and Big Data technologies, as well as their relationship from the vision of Smart Agriculture. Moreover, the article aims to assist in the decision-making of the strategy from the pre-production stage to the business marketing in the country by assessing Hype Cycle and trends of the technologies. Some of the big IoT data applications in the Smart Agriculture cycle are also introduced.

The emergence of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) in China on December 2019 has led to a global outbreak of COVID-19, which spread worldwide and became an international public health issue. People all over the world must fight in this unexpected battle and the part each individual plays, is important. The health care system has done an excellent job, and the government has done various actions to help the community control the virus spreads as well. On the other hand, in most cases people help to improve the situation alongside the policies. However, the role of information technology in helping various social institutions to fight COVID-19 is hidden and is not well appreciated. The purpose of this study is to discover the hidden role of information technology (IT) that ultimately helps to control the epidemic. Research has shown that strategies for using potential technologies can be beneficial. These IT strategies can also be tailored either to control the epidemic or to support community exclusion during the epidemic which in turn, helps control the spread of infection. This article sheds light on the impact of various technologies that help health care systems, government and public in different aspects to fight COVID-19. In addition to the technologies implemented, this paper also deals with the potential unexpected technologies that can be effective in controlling epidemic conditions for future applications. It has also tried to provide IT-based solutions to deal with the disease epidemic as well.