Network Tools Research Articles

Degree of acquisition of the English language, attitude, and speaking proficiency of pre-service teachers in a state university in Iloilo, Philippines

English, one of the most important languages that is being spoken globally, serves as a tool for global networks and for local identities. There is a need to achieve higher English proficiency level among students in order to meet the demands of time and to totally gear them up for global competitiveness. This descriptive research was conducted to assess the degree of English language acquisition among the 112 pre-service students, which focused on the variables such as sex, field of specialization, family monthly income, and place of residence. It also looked into the speaking proficiency of the respondents, which formed bases of enhancement programs. A validated questionnaire was used to gather data on the degree of English language acquisition of the pre-service teachers with reliability coefficient of 0.87 while an impromptu speech was done to evaluate their speaking proficiency. Results revealed that “sex” was not a factor in the degree of English language acquisition of the pre-service teachers but “field of specialization,” “family monthly income,” and “place of residence” were factors. In terms of speaking proficiency, “field of specialization” and “place of residence” were factors. “Reading,” among the language areas, was found to be the most employed strategy in English language acquisition of the pre-service teachers but “speaking” was common among those with “high” level of speaking proficiency. Thus, English language acquisition is shaped by personal factors such as attitude, motivation and exposure to the language, and social factors such as socio-economic status and social proximity but is enhanced through reading, watching, listening to expedient materials accompanied by using the language. It is recommended that further studies be conducted considering the inputs the students get from school and outside

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

Nowadays, it is common for applications to require servers to run constantly and aim as close as possible to zero downtime. The slightest failure might cause significant financial losses and sometimes even lives. For this reason, security and management measures against network threats are fundamental and have been researched for years. Software-defined networks (SDN) are an advancement in network management due to their centralization of the control plane, as it facilitates equipment setup and administration over the local network. However, this centralization makes the controller a target to denial of service attacks (DoS). In this study, we aim to develop a network anomaly detection and mitigation system that uses gated recurrent unit (GRU) neural networks combined with fuzzy logic. The neural network is trained to forecast future traffic, and anomalies are detected when the forecasting fails. The system is designed to operate in software-defined networks since they provide network flow information and tools to manage forwarding tables. We also demonstrate how the neural network’s hyperparameters affect the detection module. The system was tested using two datasets: one with emulated traffic generated by the data communication and networking research group called Orion, from computer science department at state university of Londrina, and CICDDoS2019, a well-known dataset by the anomaly detection community. The results show that GRU networks combined with fuzzy logic are a viable option to detect anomalies in SDN and possibly in other anomaly detection applications. We compared our model with other deep learning techniques.

Dynamic and bibliometric analysis of terms identifying the combating financial and cyber fraud system

The main purpose of this study is to conduct a dynamic and bibliometric analysis of the main terms that identify the system for combating financial and fraud to identify trends in the formation of social and scientific thought. The review of the scientific literature indicates an increase in the number of scientific publications over the past ten years. It was revealed that the most cited works cover the problems associated with cyber threats in everyday life, among which are botnets, cyber bullying, as well as financial fraud implemented through cryptocurrencies, smart contracts, and the black market on the Internet. Cloud forensics, technical and intellectual analysis are proposed as countermeasures. The research tools were a dynamic analysis of global network user requests, implemented using Google Trends, and a bibliometric analysis of scientific publications by the world’s leading scientists, performed using the VOSviewer analytical package. The search terms “Fraud”, “Finance Fraud”, “Cyber Fraud”, “Finance Cyber Fraud”, “Money Laundering”, “Anti-Money Laundering” and “Anti-Fraud” for the period from 08/07/2017 to 08/07/2022. For bibliometric analysis, two datasets with a length of 2,000 observations were formed based on queries in the Scopus database regarding the terms “Cyber Crime” and “Anti-money Laundering”. The results of the dynamic analysis revealed a decrease in the level of interest in fraud and financial fraud since the beginning of 2021, while the trend of cyber fraud is increasing. This led to the conclusion that there was an impact of the pandemic, which caused an increase in cybercrime. The results of the analysis of requests for “Fraud” and “Finance Fraud” by geographical distribution showed that they interested users belonging to countries with a significant difference in economic development. That is, representatives of poor countries are potential cyber fraudsters, and developed countries are potential victims of fraud. Conducting a bibliometric analysis made it possible to obtain clusters of promising areas of scientific research in the field of cybercrimes, among which mathematical and network tools for combating them, general concepts, digitalization and digital forensics, cyber protection, data protection, authentication and encryption of data, etc. are highlighted. At the same time, the focus of research is shifting towards methods of countering cybercrimes. Promising directions in the field of Money Laundering are mathematical methods and information technologies, cryptocurrencies and blockchains, corruption, financial terrorism, etc. The greatest potential belongs to money laundering through cryptocurrencies and blockchains. The lessons learned can be useful for improving the strategy of combating financial and cybercrimes and forming an analytical basis for the scientific community and practitioners.

Застосування інструментів соціальних мереж для розвитку туристичної індустрії

The article is devoted to the study of social network tools and their application for the development of the tourism industry. The service sector was one of the first to actively and successfully use social networks for commercial purposes in order to increase the number of consumers of services and, accordingly, receive income from the main activity. Content creation and its expansion, brand recognition and authority; Information channels and integration with state digital services have created special conditions for the tourism and hospitality sectors for their development. The rapid development of the digital economy and information society gives impetus to the development of various spheres and branches of the national economy. The spread of various social communication networks has changed the approaches and tools of doing business. It has been confirmed that the advantages of using digital technologies for conducting business, including tourism, are: accessibility, speed, simplicity, productivity, lower cost, etc. Identified trends in the development of tourism in Ukraine due to digitalization in the tourism industry. The dynamics of the volume of digital tourist services during 2015-2020 were studied. It has been established that the most common social networks are Twitter, YouTube, Facebook, Instagram, LinkedIn and others. Each of these social networks has its own set of tools and methods of contact with consumers. It has been studied that the most popular and widespread tools of social networks are: uploading photos, video and text; commenting and labeling of publications; tracing of traces; messenger (direct message, personal messages, etc.); ppyami efipy; "Stories"; "reels". The article highlights the main advantages of social networks for the development of the tourism industry. Thanks to the use of social network tools, there is a prospect of constant and rapid growth of the audience of potential groups

Machine learning techniques applied to the coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021

During the pandemic caused by the Coronavirus (Covid-19), Machine Learning (ML) techniques can be used, among other alternatives, to detect the virus in its early stages, which would aid a fast recovery and help to ease the pressure on healthcare systems. In this study, we present a Systematic Literature Review (SLR) and a Bibliometric Analysis of ML technique applications in the Covid-19 pandemic, from January 2020 to June 2021, identifying possible unexplored gaps. In the SLR, the 117 most cited papers published during the period were analyzed and divided into four categories: 22 articles that analyzed the problem of the disease using ML techniques in an X-Ray (XR) analysis and Computed Tomography (CT) of the lungs of infected patients; 13 articles that studied the problem by addressing social network tools using ML techniques; 44 articles directly used ML techniques in forecasting problems; and 38 articles that applied ML techniques for general issues regarding the disease. The gap identified in the literature had to do with the use of ML techniques when analyzing the relationship between the human genotype and susceptibility to Covid-19 or the severity of the infection, a subject that has begun to be explored in the scientific community.

Protein targets in red complex pathogens for catechin.

The development of antimicrobial drug resistance has encouraged scientists to develop alternate methods to combat infectious pathogens associated with dental diseases. Therefore, it is of interest to predict interactions for catechin (a plant derived compound) with protein targets in the red complex pathogens using computer aided network tools. However, in vitro and in vivo studies are warranted to confirm the antimicrobial effect of catechin (gallocatechin, epicatechin, epigallactocatechin (EGC) and gallolyl catechins) on the dental pathogens.

Applying the combination of GIS tools with upgraded structural and morphological methods for studying neotectonics

Structural and geomorphological methods are often applied to the search for small oil-producing structures. Morphometric analysis of digital elevation models has proved to be the most informative one. Morphometric surfaces can be used to evaluate the direction and amplitude of vertical movements, to outline local and regional neotectonic structures and assess their petroleum saturation. This paper shows how to enhance the traditional morphometric analysis with GIS (geographic information systems) tools. A manifold increase in the efficiency of morphometric analysis takes it to a qualitatively new level. Setting specific parameters for some geoprocessing tools (for example, stream network tools) can be very important when studying local structures in small areas. In case of large territories, the output result is almost independent of the calculation errors. The improved technique proposed in this paper was tested on a large territory located in the Volga region. As a result, high-order morphometric surfaces were obtained, which was not possible before. In addition, a statistically significant relationship was discovered between morphometric surfaces and distribution of oil deposits, which can be considered a reliable prospecting indicator in the Volga-Ural petroleum province. Keywords: neotectonics; structural and morphological methods; geoinformation systems; hydrocarbon potential assessment.

The role of process mining tools in STEMI networks: where should we build a new primary PCI centre?

Abstract Background In ST-segment elevation myocardial infarction (STEMI), time delay between symptom onset and treatment is critical to improve outcome. The expected transport delay between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy. The “Centre” region of Portugal has heterogeneity in PCI assess due to geographical reasons. Purpose We aimed to explore time delays between regions using process mining (PM) tools. Methods We retrospectively assessed the Portuguese Registry of Acute Coronary Syndromes for patients with STEMI from October 2010 to September 2019, collecting information on geographical area of symptom onset, reperfusion option, and in-hospital mortality. We used a PM toolkit (PM4H – PMApp Version) to build two models (one national and one regional) that represent the flow of patients in a healthcare system, enhancing time differences between groups. One-way analysis of variance was employed for the global comparison of study variables between groups and post hoc analysis with Bonferroni correction was used for multiple comparisons. Results Overall, 8956 patients (75% male, 48% from 51 to 70 years) were included in the national model (Fig. 1A), in which primary PCI was the treatment of choice (73%), with the median time between admission and primary PCI <120 minutes in every region; “Lisboa” and “Centro” had the longest delays, (orange arrows). Fibrinolysis was performed in 4.5%, with a median time delay <1 hour in every region. In-hospital mortality was 5%, significantly higher for those without reperfusion therapy compared to PCI and fibrinolysis (10% vs. 4% vs. 4%, P<0.001). In the regional model (Fig. 1B) corresponding to the “Centre” region of Portugal divided by districts (n=773, 74% male, 47% from 51 to 70 years), only 61% had primary PCI, with “Guarda” (05:04) and “Castelo Branco” (06:50) showing significant longer delays between diagnosis and reperfusion treatment (orange and red arrows, respectively) than “Coimbra” (01:19) (green arrow); only 15% of patients from “Castelo Branco” had primary PCI. Fibrinolysis was chosen in 10% of patients, mostly in “Castelo Branco” (53%), followed by “Guarda” (30%), with a median time delay of 39 and 48 minutes, respectively. Regarding mortality, PCI and fibrinolysis groups had similar death rates while those patients without reperfusion had higher mortality (5% vs. 3% vs. 13%, P=0.001). Conclusion Process mining tools help to understand referencing networks visually, easily highlighting inefficiencies and potential needs for improvement. The “Centre” region of Portugal has lower rates and longer delay to primary PCI partially due to the geographical reasons, with worse outcomes in remote regions. The implementation of a new PCI centre in one of these districts, is critical to offer timely first-line treatment to their population. Funding Acknowledgement Type of funding sources: None. Figure 1

Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers

Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, the regularity normalization constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrate the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, the unsupervised attention mechanisms is a useful probing tool for neural networks by tracking the dependency and critical learning stages across layers and recurrent time steps of deep networks.

Elemental, Thermal and Physicochemical Investigation of Novel Biodiesel from Wodyetia Bifurcata and Its Properties Optimization using Artificial Neural Network (ANN)

In this study, an unexplored oil from the wodyetia bifurcata fruit was used for biodiesel production. The transesterification process was implemented to convert the raw oil into wodyetia bifurcata methyl ester (WBME) and the influence of process variables on WBME yield was examined with the response surface method (RSM) assisted Box-Behnken optimization. The results of RSM show that a maximum biodiesel yield of 94.67% was achieved and reaction time was identified as an influencing process variable. The fatty acid composition (FAC) from chromatography reveals the presence of highly unsaturated in WBME and the significant fuel properties of thermal and molecular meet the required fuel standards (ASTM). The obtained fuel properties of WBME are compared with other popularly used biodiesels and observed low kinematic viscosity (3.87mm2/sec) and moderated cetane number (53) for WBME. Furthermore, artificial neural network (ANN) tools are used for the prediction of WBME yield and show an improvement of 0.4% than RSM and low mean square error and a high coefficient of correlation was observed for ANN.

Adsorptive removal of dairy industrial pollutants by chitosan zinc –oxide nanoadsorbent- A comparitive study of artificial neural network and quadratic Box-Behnken design

Aim: To investigate the effect of operational parameters on the adsorption of biological oxygen demand (BOD) and chemical oxygen demand (COD) on to Chitosan zinc oxide (CZnO) nanoadsorbent using cost-effective and eco-friendly nanoadsorbent based effluent treatment processes. Methodology: CZnO nanoadsorbent particle was synthesized using chemical precipitation method. The nano size <100 nm was achieved using high-speed cryo all mill, followed by the characterization using high-end instruments such as scanning electron microscope with elemental detection sensor (SEM-EDS), atomic force microscope (AFM), X-ray diffractometer (XRD) and Fourier transform inform infrared spectroscopy (FT-IR). Modeling and optimization of operational parameters were done with the artificial neural network (ANN) and Box-BehnkenDesign (BBD) statistical tools. Results: Optimized treatment combination for adsorption of BOD and COD were found at initial BOD and COD concentration of 100 and 200 mg l−1, pH of 7.0 and 2.0, adsorbent dosage of 1.25 mg l−1, contact time of 100 and 60 min. In these conditionsthe desirability values of 0.988 and 0.950 were found for BOD and COD adsorption. The maximum per cent reduction of BOD and COD by using CZnO nanoadsorbent was found to be 96.71 and 87.56. Two models such as Quadratic Box-Behnken and ANN were compared in term of sum of square errors (SSE), root mean square error (RMSE) and correlation coefficient (R2) values. Interpretation: The results obtained revel the well trained ANN model found to be more accurate in prediction of BOD and COD adsorption process parameters compared to BBD model.

Context-aware security for the Intra-Electric Vehicle Network under energy constraints

Smart Vehicle applications are generally designed to ensure the driver’s and passenger’s safety as well as to improve the traffic management and provide convenience services. On the other hand, the researchers and industrialists consider Electric Vehicle as a key technology contributing to the reduction of CO2 emissions. Many attacks, such as vehicle remote control and engine management software reboot, can be performed on the Smart Electric Vehicle Network and affect the driver’s safety. To protect the Smart Vehicle traffic, it is necessary to protect all the involved systems (cloud services, applications, automotive components, Intra-Vehicle Network, maintenance and diagnostic tools, etc.). The main objective of this work is to secure the communication of the in-vehicle sensors by proposing a context-aware security solution based on the dynamic driving context. This solution applies a set of security strategies according to the State Of Charge (SOC), the distance to the nearest available charging station, the type of sensors, the sensor resources (memory and processing) and the traffic conditions. Simulation experiments were conducted to evaluate the proposed approach in terms of the trade-off between security and energy.

P4Flow: Monitoring Traffic Flows With Programmable Networks

Cloud providers perform flow monitoring to get insights from the network traffic flows, often using coarse-grained packet counters or packet probing. These approaches give partial information from ongoing flows or introduce significant overhead if the probe packets cross multiple hops with diverse delay and bandwidth to reach the traffic collector. Recently, In-band Network Telemetry (INT) offered by programmable networks, e.g., P4, has gained attention by providing fine-grained network telemetry. Current attempts on INT are inflexible in collecting telemetry for customs flows according to the desired interval. This letter proposes P4FLOW, a flow monitoring tool for cloud provider networks implemented on programmable data planes. P4FLOW allows the providers to monitor a set of desired flows according to their needs. It reduces at least 1.6× the overhead of telemetry packets compared with the existing approaches.

The Network of Pro-Inflammatory Factors CD147, DcR3, and IL33 in the Development of Kawasaki Disease.

IntroductionKawasaki disease (KD) is an acute febrile systemic vasculitis, but the etiology remains unknown. We studied serum levels of CD147, DcR3, and IL33 in different stages of KD to explore the value of CD147, DcR3, and IL33 in the pathophysiology of KD.MethodsWe measured serum levels of CD147, DcR3, and IL33 by enzyme-linked immunosorbent assay (ELISA) at different stages with 71 KD patients and 66 healthy control children. We apply for network tools GeneMANIA and Cytoscape APP to analyze the functions of these pro-inflammatory factors at the gene and protein level.ResultsSerum levels of CD147, DcR3, and IL33 were significantly increased in KD patients before IVIG treatment. Serum levels of CD147, DcR3, and IL33 gradually decreased over time after the treatment of IVIG. Eight cases were IVIG non-responders, while nine KD patients got CALs, but they did not overlap. And there were no statistical differences between group IVIG responders and IVIG non-responders or between groups without CALs and with CALs. We explored the functions of CD147, DcR3, and IL33 from GeneMANIA and Cytoscape APP and found these third pro-inflammatory factors were coexpressed, physical interactions, genetic interactions with other KD-related factors.ConclusionCD147, DcR3, and IL33 are involved in the pathophysiology of KD, which provides novel evidence for diagnosing and treating KD with their inhibitors.

A Comparative Study of Social Network Tools for Analysing Chinese Elites

A Comparative Study of Social Network Tools for Analysing Chinese Elites

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.

Pollinator-Mediated Indirect Effects on Plant Fecundity Revealed by Network Indices.

AbstractIndirect effects arise when one species influences how another species interacts with a third. Pollinator-mediated indirect effects are widespread in many plant communities and are often not restricted to plant species pairs. An analytical framework does not exist yet that allows for the evaluation of indirect effects through shared pollinators in a community context as well as their consequences for plant fitness. We used network indices describing pollinator sharing to assess the extent to which plant species affect and are affected by others in a pollination network from a species-rich dune community. For 23 plant species, we explore how these indices relate to plant fecundity (seeds/flower) over two years. We further linked plant traits and indices to uncover functional aspects of pollinator-mediated indirect interactions. Species frequently visited by shared pollinators showed higher fecundity and exhibited traits that increase pollinator attraction and generalization. Conversely, species whose shared pollinators frequently visited other plants had lower fecundity and more specialized traits. Thus, pollinator sharing benefited some species while others suffered reproductive disadvantages, consistent with competition. The framework developed here uses network tools to advance our understanding of how pollinator-mediated indirect interactions influence a species' relative reproductive success at the community level.

Describing properties of littoral habitats from NW Mediterranean rocky shores through co-occurrence network analysis

Littoral habitats are commonly defined by environmental properties and the presence of certain dominant species. This conception relies on species’ frequencies and/or abundances, but often omits co-occurrence relationships. In an effort to incorporate this information, we here used presence-absence data from visual censuses of 16 mediolittoral habitats from North-western Mediterranean Sea rocky shores to build co-occurrence networks. These were compared to networks built using species frequency data. Network analyses were done for the entire mediolittoral zone as a unit, and for each habitat individually. For each habitat, network topology revealed central hubs of highly co-occurring species that scored high in degree and eigenvector centrality, essentially corresponding to the most frequent species, both in the mediolittoral zone as a whole and within each of the habitats. Many species, however, co-occurred disproportionately to their frequencies, which highlights the relevance of co-occurrence estimations for habitat descriptions. Some habitats had clear modules as separate habitat subunits. Values of Shannon-Wiener diversity and evenness were well associated with most network metrics, but not with graph density, which helped uncover inhomogeneous species relationships within a same habitat throughout the sampling sites. The habitat features observed mostly supported current phytosociological subdivisions. This stresses the importance of network tools for depicting and describing habitat types, as they provide complementary information to conventional descriptions and may help identify habitat sub-groups, or to define unclassified habitats.

The role of process mining tools in STEMI networks: where should we build a new primary PCI centre?

Abstract Background In ST-segment elevation myocardial infarction (STEMI), time delay between symptom onset and treatment is critical to improve outcome. The expected transport delay between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy. The “Centre” region of Portugal has heterogeneity in PCI assess due to geographical reasons. Purpose We aimed to explore time delays between regions using process mining (PM) tools. Methods We retrospectively assessed the Portuguese Registry of Acute Coronary Syndromes for patients with STEMI from October 2010 to September 2019, collecting information on geographical area of symptom onset, reperfusion option, and in-hospital mortality. We used a PM toolkit (PM4H – PMApp Version) to build two models (one national and one regional) that represent the flow of patients in a healthcare system, enhancing time differences between groups. One-way analysis of variance was employed for the global comparison of study variables between groups and post hoc analysis with Bonferroni correction was used for multiple comparisons. Results Overall, 8956 patients (75% male, 48% from 51 to 70 years) were included in the national model (Fig. 1A), in which primary PCI was the treatment of choice (73%), with the median time between admission and primary PCI <120 minutes in every region; “Lisboa” and “Centro” had the longest delays, (orange arrows). Fibrinolysis was performed in 4.5%, with a median time delay <1 hour in every region. In-hospital mortality was 5%, significantly higher for those without reperfusion therapy compared to PCI and fibrinolysis (10% vs. 4% vs. 4%, P<0.001). In the regional model (Fig. 1B) corresponding to the “Centre” region of Portugal divided by districts (n=773, 74% male, 47% from 51 to 70 years), only 61% had primary PCI, with “Guarda” (05:04) and “Castelo Branco” (06:50) showing significant longer delays between diagnosis and reperfusion treatment (orange and red arrows, respectively) than “Coimbra” (01:19) (green arrow); only 15% of patients from “Castelo Branco” had primary PCI. Fibrinolysis was chosen in 10% of patients, mostly in “Castelo Branco” (53%), followed by “Guarda” (30%), with a median time delay of 39 and 48 minutes, respectively. Regarding mortality, PCI and fibrinolysis groups had similar death rates while those patients without reperfusion had higher mortality (5% vs. 3% vs. 13%, P=0.001). Conclusion Process mining tools help to understand referencing networks visually, easily highlighting inefficiencies and potential needs for improvement. The “Centre” region of Portugal has lower rates and longer delay to primary PCI partially due to the geographical reasons, with worse outcomes in remote regions. The implementation of a new PCI centre in one of these districts, is critical to offer timely first-line treatment to their population. Funding Acknowledgement Type of funding sources: None. Figure 1

The use of virtual reality and fuzzy neural network tools to identify the focus on achieving project results

In the process of making a decision on the inclusion of an applicant in the project team, it is proposed to take into account his project results targeting (PRT). The article describes the conceptual basis for identifying people’s focus on achieving the results of significant projects based on the use of virtual reality tools and the capabilities of fuzzy inference systems. At the same time, to configure the parameters of the membership functions of the fuzzy inference system and the values of the individual inferences of the fuzzy rules, the use of a neural-fuzzy network is proposed. To get a training sample, the participants of the teams of implemented projects will need to pass a series of tests using virtual reality tools. According to the proposed concept, persons with high PRT will be primarily recommended for inclusion in the team of project performers.