درخت حوزه‌های تخصصی

The introduction of cloud computing techniques revolutionized the current of information processing and storing. Cloud computing as a competitive edge provides easy and automated access to the vast ocean of resources through standard network mechanisms to businesses and organizations. Due to the vast diversity of service providers and their respective variety of available services with different qualities, top managements often face difficulty for choosing the best available option. So, considering the growing significance of the mentioned issue, this study aims to identify and rank contributing factors in selection of cloud service providers. In that attempt, this research approaches its goal by going through three major phases. Firstly, in phase one, prior studies are reviewed for extracting related elements of selection. Secondly, by employing Fuzzy Delphi method and obtaining results by interviewing experts in this field such as IT managers and technicians, this study tries to finalize the list of contributing factors. Lastly, by utilizing Fuzzy best-worst multi-criteria decision-making method, which is one of the most recent techniques employed to statistically rank variables, this research introduces a list of vital factors for cloud service selection. Based on the findings of this study, there are five major categories involved in the selection process which are: performance, security, data management, personal data protection and environmental-organizational. The finalized result of ranking shows that, performance related factors such as accessibility, response time and capacity are the first priority. The runner-up is security with reliability and governance. Environmental-organizational variables lands in the third place by considering rental and network costs.

Penetration of smartphones and increasing use of social media on always-on devices has attracted the attention of enterprises and organizations to benefit from such platforms for better understanding of customers’ needs and more effectively communicate with potential consumers. For this purpose, the present study investigates the impact of social media adoption and media needs on online purchasing behavior. This study has also examined the moderating role of media type, gender and age in the relationship between variables of social media adoption and online purchasing behavior. A total of 410 questionnaires were collected on social media such as Instagram, Telegram and WhatsApp. SEM technique using PLS used as the analytical method to empirically test the proposed hypotheses. Results show that the effects of tension dimension on online purchasing behavior is mediated by social media adoption. Moreover, the effects of social media adoption on online purchasing behavior are moderated by age and medium type based on Multi-Group Analysis. The IPMA matrix shows that social media adoption had the highest importance, but the lowest performance. This study is significant in terms of innovation due to the use of new indicators effective in the area of statistical analyses. The use of FIMIX, CTA, permutation test, MGA, and IPMA matrix analyses is part of it.

The ability of now-casting and eventuality is the most crucial and vital achievement of big data analytics in the area of policy-making. To recognize the trends and to render a real image of the current condition and alarming immediate indicators, the significance and the specific positions of big data in policy-making are undeniable. Moreover, the requirement for policy-making institutions to produce a structured model based on big data analytics for now-casting and eventuality of predictive policies is growing rapidly. The literature review demonstrates that a comprehensive model to assist policy-making institutions by providing all components and indicators in now-casting of predictive policies based on big data analytics is not devised yet. The presentation of the model is the main finding of this research. This research aims to provide a comprehensive model of now-casting and eventuality of predictive policies based on big data analytics for policy-making institutions. The research findings indicate that the dimensions of the comprehensive model include: the alignment of now-casting strategies and the big data analytics’ architecture, now-casting ecosystem, now-casting data resources, now-casting analytics, now-casting model and now-casting skill. The results of using the model were analyzed and the recommendations were presented.

In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works.

This paper focuses on the problems of company’s sustainable management in an emergent environment. The authors’ vision of the functioning peculiarities in the formulation of a company’s strategy based on the information support is presented in the article. The world experience of studying the development of investment projects in companies of different industry sectors is investigated and analyzed according to the economic and mathematical modeling. A multivariate model of evaluation and selection of the investment project based on the hierarchy criterion of company's economic objectives with the information technology is developed as an embodiment of the strategic goal of ensuring the company's sustainability. The essence of the hierarchy criterion method is to determine the order and the value of priority of each individual criterion, thus, application of this method allows providing recommendations for making an effective management decision in unpredictable environment. The proposed model based on the hierarchy criterion of company’s economic tasks allows providing sustainability in emergent environment. The analysis and substantiation of the necessity of applying the proposed model with the information technology support is presented in this paper.

In the theory of traffic flows the main characteristics are: intensity, speed, and density.  They make it possible to use hydrodynamic models. In connection with the development of modern highways and road networks, traffic flows behavior is becoming more and more complex and diverse. In particular, the B.Kerner studies have shown that the laminar solution of hydrodynamic models is poorly correlated with experimental data. Our research team is developing tools for intelligent monitoring of traffic flows on fragments of the road network with different geometries.  The paper presents a project of a client-server system, which allows obtaining, in real-time, information regarding the basic characteristics of traffic flows at the junction of any configuration using mobile devices. The automation of obtaining characteristics is based on the application of image recognition algorithms (virtual detection method).

Malware attack is growing day by day in cyberspace. And Wireless Sensor Network (WSN) is also facing a hazardous type of situation due to attack of malware (malicious code, virus, worm etc.). Malwares target sensor nodes easily because, nodes are equipped with limited resources. Hence, security of WSN against malware attack is one of the imperative requisite. Malware spreads in the entire network wirelessly, which initiates from single infectious node and spread in the whole WSN. In this way the complete network comes under the security threat. Therefore, it is mandatory to apply the security technique through which to secure WSN against malware attacks. To secure WSN due to malware attacks a quarantine based model has been proposed. The proposed model consists of various epidemic states namely: Susceptible Carrier - Infectious - Quarantine - Recovered - Susceptible (SCIQRS). The model explained the propagation dynamics of malware in WSN and proposed a technique to prevent its propagation. The technique of quarantine along with recovery is to much effective in prevailing of malware propagation in WSN. For the determination of WSN stability and equilibrium points the expression of basic reproduction number has been obtained. Malware propagation is affected by different network parameters, which has been also discussed. The comparative investigation of proposed model has been carried out with existing model. The proposed model has been substantiated by simulation outcomes

The purpose of research is focused on the insight into the future of Bitcoin on the financial situation, its implications and challenges. The problem of study is to investigate how to deal with a new type of digital currencies (such Bitcoin) that does not have a physical presence and there is no specific body to issue. Thus, this study aims to identify the nature of Bitcoin currency and what are the challenges associated with it as well as exchange rates with some currencies, as the research hypothesized the main hypothesis that Bitcoin will contribute to financial development in the future, the research also used the analytical rooted approach to present concepts and data, using financial technical analysis to display the results The research also came up with a number of conclusions and recommendations. The results of paper significantly contribute to the literature through providing evidence from financial data of Bitcoin.

Focusing on the problems faced by blind people, this paper has come up with the technology solution for the assistance of blind people. The solution is based on the intelligent data transmission to the earphone of a person based on task associated. The solution consists of a jacket to detect the obstacles along with a wearable box with task priority switchs. The system helps in detection of the obstacle and its height, one-touch cab booking and support of relatives, Ambulance services, Police services, etc. in the case of emergency. Either wired and wireless headphones or speakers can be interfaced with the device (box) to get audio notifications. The various tasks are triggered using multiple switches. The system will use a definitive SOC (System on Chip) platform recognized as Rasp-Pi-Pi along with ultrasonic sensor HC-SR04, Neo-6M GPS (Global Positioning System) module, and different switches. The system uses a 20,000 mAh lion battery for the power supply. The voice signals can be provided in more than fifty languages. A fall detection system is also discussed in this paper. This system will be beneficial not only for blind but also for care of old aged people

The production of counterfeit paper currencies has become cheaper because of the advancement in the printing technologies. The circulation of counterfeit currencies down the economy of a country. By leveraging this, there is a mandate to develop an intelligent technique for the detection and classification of counterfeit currencies. The intelligent techniques play a major role in the field of Human Computer Interaction (HCI) too. This paper deals with the detection of counterfeit Indian currencies. The proposed method feature extraction is based on the characteristics of Indian paper currencies. The first order and second order statistical features are extracted initially from the input. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8%. The experimental results are compared with state-of-the methods and produced reliable results.