درخت حوزه‌های تخصصی

Blockchain is one of the trending emerging technologies that enables immutable data storage over distributed ledgers. The fundamental features of blockchain technology, such as record and identity traceability and immutability makes it an attractive alternative for the state-of-the-art information systems. Thus, blockchain has found its way into various fields. In Iran, blockchain technology has been employed in different areas including: commercial mining platforms, secure exchange platforms, and innovative trustless projects. One of the main applications of blockchain is secure management of sensitive information, which makes it a suitable infrastructure for providing advanced control over data in police task forces. The motivation behind this research is to provide a conceptual framework for employing blockchain in law enforcement organizations by analysis and comparison of best practices in other countries and academic case-studies. We provide a systematic overview of case-studies by qualitative content analysis. Moreover, the reliability of our statistics is evaluated by Cohen's kappa coefficient roughly 0.64. Finally, the prioritized applications were evaluated by a 14 member experts panel through fuzzy Delphi method with a Kendall’s Coefficient of Concordance of W=0.711, indicating high rates of agreement among members. To this end, “trustless authentication systems”, “distributed data/resource/information management”, and “inter-department integrated information systems” were selected as most valuable applications of blockchain for police task forces and law enforcement agencies in Iran.

SCImago Journal Rank (SJR) is one indicator of a journal's reputation.  The value is calculated based on several published journals, such as scholarly journals' scientific impact, representing the number of quotes sent to a journal and the relevance or reputation of journals from which the quotations originate. A high SJR value means that the corresponding journal has a high reputation. This study aims to approach the SJR classification by implementing a machine learning approach. A simple yet powerful method Naïve Bayes Classifier (NBC), is selected. NBC utilizes probability calculations based on Bayes' theorem. However, NBC has an assumption that the attribute values do not depend on each other. This method is optimized using Particle Swarm Optimization (PSO) to overcome this weakness. This study used SJR data of the computer science domain from 2014 to 2017. Publication without Q rank is filtered for better performance. As a result, the accuracy of the proposed method is higher than the baseline. The use of PSO  significantly improves the NBC performance based on the performed T-test. The PSO-NBC selects four of eight features: H index, Cites/ Doc (2 Years), and Ref. / Doc. Overall results show that using PSO-NBC is closer to SJR rather than using mere NBC.

The purpose of this study is to substantiate the basic principles and prerequisites for the formation of project maturity of public administration for the successful implementation of digital governance projects. The relevance of this study is due to the need to implement the task of digital transformation of Ukraine by solving the problem of modernization of public administration on the basis of constant increase of project maturity of the organizations.The study proved the existence of a direct dependence of prospects for the implementation of digital transformation projects on the level of project maturity of public management. A variant of the project maturity model adapted to the local self-government body is being developed. The model assumes three levels of maturity of local governments. It has established that the majority of the created united territorial communities of Ukraine were characterized by the first two levels of maturity. The implementation of the maturity model makes it possible to assess the current state of the system, choose adequate tools for staff training, create conditions for continuous improvement and realization of the potential of the local community for the implementation of digital transformation projects.

The inefficiency of the national security system is one of the biggest threats to Ukraine's national security. The strategic planning in the field of security and defense has designed to identify the means, including the future structure of forces, that would allow the institutions of the defense and security sector to effectively deal with potential future threats. The purpose of this study is development of theoretical and practical measures to increase the effectiveness of strategic management of national security processes on the basis of goal setting, forecasting and planning in the context of informatization of society. The relevance of this study is due to the needlegal regulation and improvement of the state system of strategic planning of national security of Ukraine. To achieve the goal of the study was used special scientific methods of legal knowledge. The results of the analysis showedwhat in the basis ensuring national security must rely on the principle of systematization, which means that the security and defense sector of Ukraine is considered as a single object of forecasting and planning. This study substantiates the main directions systemic reform of the security and defense sector, which has carried out in Ukraine to ensure the protection of the constitutional order, sovereignty, territorial integrity of the state in the face of armed aggression. The priorities should be implemented through key tasks, such as the establishment of a comprehensive strategic analysis of military threats to Ukraine's national security, coordination of intelligence activities, development of joint intelligence capabilities of the defense forces in order to obtain complete and reliable warning information for timely decision-making guaranteeing the military security of the state.

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.

The reconstruction of 3D images is always a difficult task for the researchers. The 3D reconstruction of the image is a core technique of various fields such as Computer graphics, computer vision, CAD systems, medical science, computer application, etc. Reconstruction of the 3D image allows us to gather the quantitative features of the objects such as the shape, size, and volume of the objects. The existing computer algorithms need spatial dimension information to make the distinguished inference from the given 3D image which is not always possible. This paper simplifies the 3D reconstruction of the image. This research paper introduced a novel algorithm for the representation of the Three Dimensional images into a textual form.  The syntactic approach is used for the extraction of the features of the image and these are called knowledge vectors. The knowledge vector consists of the direction information and length information. This a new approach in the field of image processing where images can be represented as a knowledge vector and it could be a great contribution in the field where security is a major concern. Further, the knowledge vector is used for the reconstruction of the 3D image. The performance of the algorithm is evaluated on the PASCAL 3D + and example-based Synthesis of the 3D Object Arrangements dataset. According to the obtained results, the proposed methodology is having better accuracy, and the processing time of reconstruction of the original 3D image is 1.02 Seconds. Single-pass is sufficient for reconstructing the original image

Nowadays with the ever-increasing growth in social media platforms and the creation of different forms of online activism, the word known as “Campaign” has become a familiar and useful term in people’s everyday lives. Campaigns with all kinds of social aims especially using Hashtags are run on social media platforms by individuals, charities, NGOs, governments, municipalities and brand companies and with peoples' participation and value co-creation, appropriate results are achieved and the desired changes are made in people’s attitudes and the behavior of individuals, businesses, or governments. Therefore, there seems to be a new form of a virtual public sphere where there is action, dialog and social mobilization, which leads to the formation of public opinion and social change in the digital era. In this paper, using a critical review method, we are going to investigate a public sphere's literature and study its core features (1. access, 2. dialog, 3. action, and 4. public opinion) in digital campaigns and online platforms. Then, in the last part, we are going to map those in a real case study in order to further clarify the arguments and show how social campaigns on online platforms contribute to the formation of a public sphere in the digital era.

This study aims to test the relationship between knowledge generation and organizational creativity. The second method was surveyed (analytical) using exploratory research through the survey as a data collection tool. The validity coefficient was measured as a reasonable degree of stability was obtained for a sample of (46) individuals. The data was analyzed using statistical package (SPSS V25). Descriptive Statistic Among the findings is that CASNOS has a medium level of the knowledge generation process, and there is a statistically significant effect of knowledge generation on the organizational innovation of CASNOS (El-Oued), according to the dimensions adopted in the study model.

This research deals with integrating RE and CBC in the product design process. RE improves the product value based on evaluate the competition products, whereas CBC aims to use a process approach to define activities and prelates those activities to products or customers using the product’s Characteristics. Integrating RE and CBC together leads to the improvement of product value, an increase of customer satisfaction, and support of competitive advantage. In this study first, a conceptual model of integration of these two approaches is provided and then the implementation procedures in product design cycle are explained, and finally, the results got from implementing it in an Electronic Industries company are discussed.

The aim of the study is to develop a set of organizational and pedagogical conditions for integrated training of working teachers and students of pedagogical institutions of higher education to perform labor functions, highlighting the role of teachers in the professionalization of the educational process in a Smart Environment. The article considers the main directions of transformation of the role position of future teachers in the professionalization of the educational process. The article has presented structurally functional model development of role positions of teachers in the formation of professional competencies of students. The development of the role position of the teacher has considered in relation to his professional and personal development and self-awareness as a subject of the educational process under the influence of a Smart Environment. The mechanism of realization of such model has offered by the integrative program which provides integration of formal and informal education and formation on such basis of new competences and development of components of a role position of the future teacher.

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.