درخت حوزه‌های تخصصی

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.

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.

Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving objects. Its shows less absolute error and less object tracing error while evaluating the proposed work.

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.

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.

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.

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.

LoRaWAN is one of the new and updated standards for IoT applications. However, the expected high density of peripheral devices for each gateway, and the absence of an operative synchronization mechanism between the gateway and peripherals, all of which challenges the networks scalability. In this paper, we propose to normalize the communication of LoRaWAN networks using a Reservation-ALOHA (R-ALOHA) instead of the standard ALOHA approach used by LoRa. The implementation is a library package placed on top of the standard LoRaWAN; thus, no modification in pre-existing LoRaWAN structure and libraries is required. Our proposed approach is based on a distributed synchronization service that is suitable for low-cost IoT end-nodes. R-ALOHA LoRaWAN gives better performance in comparison with the previous models; Pure-ALOHA LoRaWAN, Slotted-ALOHA LoRaWAN, and TDMA LoRaWAN. It significantly improves the performance of network regarding the probability of collision, the maximum throughput, and the maximum duty cycle.

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 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

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.

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

VANET (Vehicular Ad-hoc Network) is a developing technology, which is a combination of cellular technology, ad-hoc network & wireless LAN to improve the safety of vehicle as well as driver. VANET communication can be of two types, first one is broadcast and second one is unicast. Either communication may be broadcast or unicast both are sensitive to different types ofassaults, for example message forgery, (DOS) denial of service, Sybil assault, Greyhole, Blackhole & Wormhole assault. In this paper machine learning method is used to detect the wormhole assault in VANET’s multi-hop communication. We have created a scenario of VANET by using AODV routing protocol on NS-3.24.1 simulator, which utilizes the overall mobility traces generated by the simulator SUMO-0.32.0 to model the wormhole assault. The simulation is performed by using NS-3.24.1 simulator, and the statistics created by flow monitor are collected. The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. The novelty of this research work is that with the help of proposed detection & prevention technique, vehicular ad-hoc network can be made free from wormhole assault by using ML approach. The performance of proposed machine learning models is compared with existing work. In this way it is clear that our proposed approach by using ML is powerful tool by which the wormhole assaults can be detected in VANETs. A scheme based on packet lease and cryptographic techniques is used to prevent the wormhole attack in VANET

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.