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.

The paper focuses on the urgency of the implementation of cloud technologies, which are a necessary condition for the development of enterprise management systems, give rise to a complex of insufficiently studied phenomena and processes and determine the need to find new tools in making and implementing reasonable management decisions. In the process of research, the sequence of construction and the overall structure of the enterprise management system, based on the use of cloud technologies, are determined, which allowed to build a mathematical model for calculating the probability of making an error-free decision, evaluating the efficiency of decision-making, a model of making a management decision for a certain time with the parallel method of operation of elements of the enterprise management system.

Internet users all over the world are increasing day by day and showing great interest for online shopping. The main reason for the high usage of the internet is the affordable price of mobile gadgets and low internet tariff plans. Consumer behavior is influenced by various factors such as culture, social class, reference groups relationship, family, income level and income independency, age, gender, etc. The purpose of this study was to find out the differences in buying behaviors among the university students. The study was carried out using google survey with a sample size of 236 students randomly selected from the university of India and Saudi Arabia. The study shows that University students of both countries have more online shopping experience because they use the internet more frequently and they have larger internet usage. Nowadays Students are more computer professionals and those who use the internet for their study work and assignments work are more active in online shopping. As per student’s opinion, they said few things remember in our mind when coming to payment options credit card is the safest option or using online services like pay pal and google wallet services also a good way but finally, cash on delivery is the best way of shopping online. The result of this study would contribute marketers who want to penetrate the market in India and in Kingdom of Saudi Arab, who are already present in the market and desire to take care of the loyalty and satisfaction of their customers.

The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.

Developed an integrated intellectual computerized system of ecological-economic monitoring, modeling, and managing the life cycle of the products of technogenic enterprises of transport engineering, which is presented in the form of a 3-equation structure, functioning in conditions of instability. The proposed paradigm system life cycle management applicable to any other control system of large and complex systems, such as techno-genic type. This new paradigm of complex systems and process management, including technical systems. The system is based on the following findings: concepts, principles, a set of non-linear models, decision-making methods, and the environmental and economic governance, integral criteria. As an example, in this paper offer solution to the problem of estimating the cost of the product life cycle of railway transport.

The backing of the fuzzy ideal is normal ideal in some ring and in same time there fuzzy set whose is not fuzzy ideal and it backing set is ideal, i.e., it crisp is normal ideal. Consequently, in this paper we constructing a fuzziness function which defined on fuzzy sets and assigns membership grade for every fuzzy set whose it backing set are crisp ideal. Now, Let  be collection of all fuzzy subsets of ring  and , and the function  defines from  to, such that the value of the function is greater than zero if the crisp set of fuzzy set is ideal, and the value of the function is equal to one when the crisp set is maximal ideal or is the ring  itself. But if the support of the fuzzy set did not ideal then the value may be equal to or large than 0. Therefore, we add another condition to the fuzziness function to be more determined with respect to the fuzzy set. From above we try to find relation between the fuzzy ideal and its crisp set. This concept is derives from the open grade for all fuzzy set in fuzzy topological space which called smooth topology.

The Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this paper, we have attempted to answer the question whether the truth is relevant. We conducted an experimental study in which we analyzed and compared the results of two different truth discovery methods: Relevance-based sources ranking and Majority vote. We have found that the truth is not always held by the most relevant sources on the web. Sometimes the truth is given by the majority vote of the crowd. In addition, we have proposed a method of presenting the results of truth discovery with gradual degrees of belief. A method that allows to configure and target the desired level of trust.

To bridge the gap between invention and innovation, prenovation has to be focused. One of the elements of prenovation is emphasizing and creating policies for institutions. This research paper highlights the importance of having an intrapreneurial culture in higher education institutions. The purpose of this paper is to review literature related to intrapreneurial culture and find out the positive aspects of having intrapreneurial culture through think tanks in higher education institutes.

The special issue aims to cover the latest research topics in designing and deploying visual information analytics and management techniques using deep learning. It is intended to serve as a platform to researchers who want to present research in deep learning. The special issue focuses explicitly on deep learning and its application in visual computing and signal processing. It emphasizes on the extent to which Deep Learning can help specialists in understanding and analyzing complex images and signals. The field of Visual Information Analytics and Management is considered in its broadest sense and covers both digital and analog aspects. This involves development of techniques for image analysis, understanding and restoration. Deep learning techniques are effective for visual analytics. Deep learning is a fast growing area and is gaining impetus for application in various fields. Therefore, in this special issue, the objective is to publish articles related to deep learning in various problems of visual information analytics and management.