درخت حوزه‌های تخصصی

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.

In the digital age, e - learning systems have been employed as new equipment in the higher education system in different universities. Consideringthe importance of optimization of this system, this research is aimed at providing a modelfor the effectiveness of e - learning at in higher education systems . The study is a descriptive survey study in terms of its data collection method . The population includes all the students of electronic courses at the University of Tehran . This population includes 1481 students of the University of Tehran in the academic year 2019 - 2020 . Regarding the population size, 300 students were selectedbased onstratified sampling, using Cochran ’ s formula.Lisrel and Amos softwarewere used for data analysis . In the first step, byliterature review, and based on the collected information, 87 components were identified to be related toe - learning effectiveness . Then, based on the highest frequency of the identified components in one hand, and their significance from the experts ’ viewpoints, on the other hand, 14 components were finally selected and classified in three major classes including;pedagogical, individual and technicalrelated factors.

As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of network slices will be cost effective for slice managing and giving the required functionality. So, the aim is to improve the efficiency of mobile network by forming an ideal slice in a multi-service communication network. In this paper, we propose a model to demonstrate network resource allocation system that forms devoted network slices to serve particular types of services independently on shared infrastructure. This model solves the problem of creating a strategy to form multi-service core mobile communication network slices, which allow the providing of a wide scope of services with certain quality indicators according to the effective dynamic configuration of the system. A resource management system model is created, to provide a method that considers costs related with excessive resource allocation, and also reduces the number of network recalculations, allowing for a reasonable proportion of management costs and Qualities of Service.