فهرست مطالب anita hadizadeh

Along with the technological changes and the growing number of users of communication and information networks, there has been a surge in the diversity of available services and their demands in these networks. This growth has resulted in current networks encountering various technical, security, and operational challenges. Hence, acknowledging the necessity of transitioning towards the future Internet, research endeavors in this domain have been launched globally. The objective of this research is to examine and analyze the programs and actions of leading countries in the development of the future Internet and to address the perspectives of the United States, China, South Korea, as well as the European Union on the future Internet by examining its potential in addressing current challenges. Furthermore, based on conducted studies, recommendations for the development of the future Internet in Iran have also been provided. The findings of this research suggest that the constraints related to Internet architecture, security, and scalability have impacted the effectiveness and quality of service provision. Consequently, countries have developed visions, policies, and measures aimed at prioritizing public interests and addressing the needs of users in this regard. Therefore, it can be concluded that to establish a suitable infrastructure for the digitalization process, greater attention should be devoted to the topic of the future Internet and its various dimensions.

Nowadays, the stock market plays an important role in the economy of different countries. The abundance of data in the stock market and the need for fast and correct data processing and making appropriate decisions have made the use of computers inevitable. In this article, using deep reinforcement learning, a model is designed to present the duties of a trader in the Iranian stock market with regard to liquidated shares. In the first step, the history of stock prices along with the indicators based on it are given as inputs to the convolutional neural network. In the next step, in order to calculate the matching rate of the convolution output with the expected output, the sum of squared error cost function is used, which, in turn, is minimized in the optimization process. Since the data in the Iranian stock market is limited, using the convolution model instead of the Q table in the deep reinforcement model prevents the over fitting of the model. In order to evaluate the model, the data of Tehran Stock Exchange was used in the period of 1390 to 1400. The performance of the proposed method was compared with the buy and hold strategy. The results show, in some cases, the profit from the proposed method in contrast with buy and hold strategy is 21% and -7%, respectively.

Cyber-health is one of the most important results of IT revolution by enabling the communication between health workers & patients. So far several countries & enterprises have offered different services to their consumers. In Iran, many island activities have been carried out to conducting cyber-health too. But they are not as progressed as Iran ICT capabilities. In this study we are on the way of identifying & ranking essential criteria for cyber-health. Since it is not implementable for all medical specialties, we have chosen cardiovascular disease users, including cardiovascular medicine, patients & also their families. So in this paper after a brief literature review on cyber-health, cardiovascular criteria will be identified & collected. Then using the methods of Multiple Criteria Decision Making, collected criteria, according to Iran local conditions, will be ranked. At last due to ranked criteria, a framework on cyber-health will be presented.