درخت حوزه‌های تخصصی

The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).

Organizations nowadays focus on, not implementing ERP systems, but also leveraging ERP systems as part of their digital strategy. They holistically address people, processes, and technology for a digital transformation. Meanwhile, higher education institutions (HEIs) are also facing an imperative need for the implementation of modern technologies to stay competitive and differentiate them as an innovation leader. Higher education management is challenged with maintaining high-level information systems. These systems can generate complex real-time reports for effective resource allocation and better decision making. Enterprise Resource Planning (ERP) systems can help HEIs manage their resources and operations effectively. A study of ERP among 112 HEIs in Egypt was conducted. This study originally investigates the Egyptian HEIs’ perception of the ERP system as a new integrating tool for its value. The results showed that Egyptian HEIs are still at the embryonic level as the majority have not adopted these systems yet. However, ERP value has been undoubtedly perceived by HEIs’ managers. Therefore, the present study fruitfully reflected HEIs’ understanding of the imperative need of ERP systems as strategic systems for their competitiveness. Consequently, the study suggests that Egyptian HEIs and ERP vendors take steps to remove any barriers and accelerate the ERP adoption process. Also, this research contributes to the advancement of ERP concepts and characteristics from HEIs' standpoint and a grants practical verification to the higher education context. Overall, the study advances existing knowledge and research on ERP, strategic management systems, and HEIs.

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.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.