درخت حوزه‌های تخصصی

The Special Issue of the Journal of Information Technology Management (JITM) is publishing very selective papers on information management, Internet of Things (IoT), Algorithms, Quality of Service (QoS),Tourists Perception , Technology in higher education, integrated systems, enterprise management, Self-Service Technology (SST) , cultural thoughts, strategic contributions, management information systems, and cloud computing. We received numerous papers for this special issue but after an extensive peer-review process, eight papers were finally selected for publication. In the digital age, the management of electronic archives became a trend as well as the focus of management development in many institutions.

Nowadays, the tourism industry accounts for approximately 10% of the global GDP, while it only contributes 3% of the economy in Iran. Since the pressure of US sanctions increases day after day on the Iranian economy, the necessity of paying attention to this industry as a source of foreign currency is felt more than ever. The purpose of this research is to analyze the reviews of users of social commerce websites by using a combination of text mining and data mining techniques. For this purpose, the database of TripAdvisor website (TripAdvisor.com) was evaluated, and all profile information of users who commented on hotels in Iran was collected. These comments on all the content of the website, such as hotels, restaurants, and attractions, were then extracted and analyzed. The optimal number of clusters was considered four clusters by calculating the Davies-Bouldin index, namingly water therapy tourists, boutique hotels style and Iran urban tourists, travelholics and food tourists, business and health tourists. Every single cluster possesses unique attributes and features. Afterward, the association rules were further identified for each cluster according to the characteristics of each cluster and the information in the users' profiles. Finally, a solution is proposed to increase the participation of the users on the website, and targeted promotional plans are expressed in accordance with the well-known features of each cluster.

Blockchain technology is one of the revolutionary technologies in service-based industries. E-health services are considered as a major economic and social activity in the world in which patients, the medical community and service providers such as insurance organizations play a key role. In Iran, e-health services have been introduced for more than a decade, but it is seriously lagging behind the leading countries. According to the Blockchain Opportunity window, a technological and economic catch-up is possible in e-health services in the country. Decentralization, data transparency, security and privacy are the key features of Blockchain technology. Therefore, in this research, through case study strategy and using documents, scientific articles, evidence, statistics and events and opinions of experts, policy factors were presented to catch-up through the Blockchain technology opportunity window. The dimensions of the proposed policy framework were presented in 4 categories: macro policies, market, financing, and technology learning and development. This study presents technological catch-up policy framework by Blockchain technology to policymakers for using in e-health services.

The main goal of the given research is to conduct cross-cultural analysis of financial support mechanisms in Europe in order to develop practical recommendations, focused on SME recovery, for emerging markets, in particular Ukraine. The article examines the main effects of pandemic on economy, focusing on business disruption, in particular SME. The financial flows as a response on anti-pandemic prevention measures were analyzed all over the world through digital tools, namely BI, as well as differentiation of support measures. The general fall in the main macroeconomic indicators was indicated for European countries and the deep analysis of financial support mechanisms provided by states was conducted. Taking into account research results, it was revealed that complex policy mechanisms were developed mainly by advanced economies while emerging markets, in particular Ukraine, do not have the effective mechanisms aimed at eliminating negative effects of pandemic. So, on the basis of conducted analysis the practical recommendations for emerging markets, in particular Ukraine, were developed using relevant European experience

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

The article is devoted to solving the problems of predicting the education of Ukrainian staff in the current COVID-2019 conditions in order to improve the efficiency of their work. Theoretical approaches regarding the characteristics of modeling and prediction of education have been studied, systematic and summarized. Factors that significantly complement the theory and methodology of the scientific provisions of the economy have been identified, taking into account the peculiarities of their work in the modern economic environment, structural changes, and new changes. The pros and cons of studying modeling approaches have been identified. The main features of structural change have been studied and it has been established that the modeling and forecasting of staff education has significant features.

The accuracy of brain tumor detection and segmentation are greatly affected by tumors’ location, shape, and image properties. In some situations, brain tumor detection and segmentation processes are greatly complicated and far from being completely resolved. The accuracy of the segmentation process significantly influences the diagnosis process, such as abnormal tissue detection, disease classification, and assessment. However, medical images, in particular, the Magnetic Resonance Imaging (MRI), often include undesirable artefacts such as noise, density inhomogeneity, and partial volume effects. Although many segmentation methods have been proposed, the accuracy of the segmentation results can be further improved. Subsequently, this study attempts to provide very important properties about the size, initial location and shape of tumors known as Region of Interest (RoI) to kick-start the segmentation process. The MRI consists of a sequence of images (MRI slices) of a particular person and not one image. Our method chooses the best image among them based on the tumor size, initial location and shape to avoid the partial volume effects. The selected algorithms to test our method are Active Contour and Otsu Thresholding algorithms. Several experiments are conducted in this research using the BRATS standard dataset that consist of 100 samples. These experiments comprised of MRI slices of 65 patients. The proposed method is evaluated by the similarity coefficient as a standard measure using Dice, Jaccard, and BF scores. The results revealed that the Active Contour algorithm has higher segmentation accuracy when tested across the three different similarity coefficients. Moreover, the achieved results of the two algorithms verify the ability of the proposed method to choose the best RoIs of the MRI samples.

The objective of this research is to identify the technology of cloud computing in terms of its concept, its development, its objectives, its components, models, classifications, and the advantages of its use in the teaching process at the University of Samarra, as well as to identify the most important challenges and obstacles that teachers face in using University of Samarra. The researcher used the descriptive approach in his research. This research was carried out on an objective sample of 40 teaching staff members in the Department of Life Sciences and English at the Faculty of Education / Samarra University. A questionnaire consisting of 22 questions were used as a data collection tool, which included two axes: The first axis: the advantages of using cloud computing technology in the process of teaching at the University of Samarra. The second axis: the challenges of using cloud computing technology in the process of teaching at the University of Samarra. The researcher used the statistical program (SPSS 20) in order to examine the differences between the responses of the members of the research sample. The results revealed that the estimates of the teaching of the advantages of the use of cloud computing technology in the teaching process at the University of Samarra, which included the search tool, came very much, and the results revealed the most important challenges which stand in the way of the use of cloud computing technology in university education. The study recommended institutions of higher learning and universities with a set of important recommendations regarding the use of cloud computing technology in educational institutions.