Showing papers by "Victoria Vysotska published in 2022"

Two-Stage AES Encryption Method Based on Stochastic Error of a Neural Network

Abstract: This paper proposes a new two-stage encryption method to increase the cryptographic strength of the AES algorithm, which is based on stochastic error of a neural network. The composite encryption key in AES neural network cryptosystem are the weight matrices of synaptic connections between neurons and the metadata about the architecture of the neural network. The stochastic nature of the prediction error of the neural network provides an ever-changing pair key-ciphertext. Different topologies of the neural networks and the use of various activation functions increase the number of variations of the AES neural network decryption algorithm. The ciphertext is created by the forward propagation process. The encryption result is reversed back to plaintext by the reverse neural network functional operator.

Correlation Analysis of Text Author Identification Results Based on N-Grams Frequency Distribution in Ukrainian Scientific and Technical Articles

Abstract: The results of experimental approbation of the proposed content monitoring method used for the determination of the author style in Ukrainian scientific texts of technical profile have been studied. Authorship identification systems typically use plagiarism and rewrite metrics to determine it. There is a necessity to identify whether the work has been borrowed fully or partially. Therefore, the situation when the work has not been published yet is not taken into consideration. Quantitative content analysis of the scientific and technical texts uses the advantages of content monitoring and analysis of text based on NLP, Web-Mining and stylometry methods to identify many authors whose speech styles are similar to the studied passages. It narrows the search for further use in stylometric methods to determine the degree of the analyzed text belonging to a particular author. The method of determining the author has been decomposed on the basis of such speech coefficients analysis as lexical diversity, degree (measure) of syntactic complexity, speech coherence, indices of the text exclusivity and concentration. In parallel, the parameters of the author style, such as the text words, sentences, prepositions, conjunction quantities and the number of words with a frequency of 1, 10 or more have been analyzed.

Sentiment Analysis Technology of English Newspapers Quotes Based on Neural Network as Public Opinion Influences Identification Tool

Abstract: This article dwells upon research concerning human language processing techniques, namely emotion analysis, conducted to identify emotions of author quotations in English newspaper articles. The publication describes general information about human language processing, brief description of analogues based on machine learning and neural network to analyse emotions in text. The main goal is to develop intelligent system for sentiment analysis of English language quotations and keywords identification that influences public opinion. The article describes sequence of program actions and tools that have been used. Then, statistics of tasks performed by neural networks with various functions of activators have been described. In the publication, product demo version has been described, and primary usage scenario has been depicted. In the end, research results have been summarised.

NLP Tool for Extracting Relevant Information from Criminal Reports or Fakes/Propaganda Content

Abstract: The goal of paper is to develop a natural language processing system to extract relevant information from criminal reports or fake/propaganda. A detailed description of the developed system was made. The technical requirements were formed. System development is divided into three points: data preparation, word embedding, and classification. Structure of the product under development have been described. It used libraries and ways of implementing the used methods. The structure of the neural network, initial parameters of the network are described in detail based on knowledge rather than machine learning methods: this means that it is necessary to specify patterns, and not to have a purpose to learn and generalize them. The second aim of this project was to apply the text summarization to case reports; that is, the converting text from long, detailed reports into a coherent, natural-language summary. The article describes the implemented training of the model for identifying propaganda using keywords. That is, propaganda carries a potential fake, and since informative noise is generally dangerous and threatens the sober thinking of a person, we suggest analysing the news for propaganda. In general, there is a lot of propaganda in the world, but many more fakes do not even carry propaganda, which improves the accuracy of model training. In this way, it allows you to filter out both fakes and propaganda.

Emotion Recognition System Project of English Newspapers to Regional E-Business Adaptation

Abstract: Main goal of the work is emotion recognition and sentiment analysis of quotes from English newspapers. Key of this problem is task solution of text emotional analysis, that is, feelings analysis of author who expresses own opinion. Emotional or sentimental text analysis is primarily the classification algorithm aimed at finding certain point and its location and information highlighting particular interest in the process. In this work, the problem of multi-class or binary classification is solved using machine learning. Publication describes general information about human language processing, brief description of analogues, use of machine learning to analyse emotions in text. Article describes the sequence of intellectual system project realization actions and the used tools for emotion recognition. Then, the statistics of tasks performed by neural network with various functions of activators are described. In the publication control example of demo version of the intellectual system project was made, and the main usage scenario was depicted. At the end, the results of the study have been summarized.

Machine Learning Model for Paraphrases Detection Based on Text Content Pair Binary Classification

Abstract: This article dwells process of ML-model development for detecting paraphrasing by binary classification of texts pair. For this study, the following semantic similarity metrics or indicators have been selected as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover Distance, distances according to WordNet dictionaries, prediction of two ML-models: Siamese neural network based on recurrent and Transformer type - RoBERTa. Developed software uses principle of model stacking and feature engineering. Additional features indicate semantic affiliation of sentences or normalized number of common N-grams. Created model shows excellent classification results on PAWS test data: weighted accuracy (precision) – 93%, weighted completeness (recall) – 92%, F-measure (F1-score) – 92%, accuracy (accuracy) – 92%. Results of study have shown that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without need for additional feature generation. Fine-tuned NN RoBERTa (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This model specificity may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both overall classification accuracy and model sensitivity to pairs of sentences that are not paraphrases of each other.

Compensatory Method for Measuring Phase Shift Using Signals Bisemiperiodic Conversion in Diagnostic Intelligence Systems

Abstract: This article dwells upon phase shift determination method using total signal obtained as a result of summing up two harmonic signals after carrying out bisemiperiodic transformation, which can be attributed to measurement compensation method. Value of phase shift has been determined by comparing vector obtained after amplitude time analog-digital conversion of total signal with a set of reference function vectors. Maximum value of correlation coefficient has been used as criterion for specified vectors coincidence. Algorithm for finding the maximum correlation coefficient using golden section method has been developed. Main errors sources for proposed method of phase shift measurement have been determined. Application of proposed method in artificial intelligence system for diagnostic and determination of modern weapons and military equipment state will allow to reduce requirements for measuring equipment without reducing accuracy of measurements

Review of: "CURATIVE RATES OF MEDICATION-RELATED OSTEONECROSIS OF THE JAW FOLLOWING APPLICATION OF THE SICMF-SIPMO SURGICAL TREATMENT ALGORITHM"

Abstract: Quite an interesting and relevant research. The article has not only a scientific component but a practical part in the form of experiments, results and discussion. This is a very important aspect of such a relevant area of research.

Intelligent system of passenger flows dynamic 2D-visualization for public transport routes

Abstract: In order to increase the attractiveness of public transport for urban residents, a software product has been created for transport companies that, by visualizing passenger traffic, helps to improve the quality of public transport services provided within the city. The paper analyses existing and current scientific developments and literature sources, which show the advantages and disadvantages of a large number of different algorithms and methods, approaches, and methods for solving problems of 2D- visualization of passenger flows on public routes. As a result of the research, stable connections have been established between the factors and criteria involved in assessing the quality of passenger transport services. The system analysis of the designed system is executed, and examples of the structure of an intelligent system of 2D visualization of passenger flows are created. The connections of the system with the essential elements of the external world are analysed. For a visual representation, diagrams of usage variants, classes, sequences, states, and activities are created according to UML notation. Our own unique algorithms have been created for displaying visualizations in two different modes: schematic and “on the map”. In the “on the map” mode, a method of calculating data on the movement of transport units on the route was successfully applied for 2D visualization on the screen, taking into account the absolute values of geographical coordinates in the world. This avoids unnecessary errors and inaccuracies in the calculations. An artificial neural network has been developed that operates using the RMSprop learning algorithm. The artificial neural network predicts how the values of passenger traffic will change when adjusting the schedule of the transport unit on the route. The obtained results make it possible to form and substantiate the expediency of changing the schedule of the vehicle running on the route in order to make more efficient use of races during peak times.

Decision-making support system to support of social networks users based on similar common interests and preferences

Abstract: Today, the socialisation of individuals with common interests is an extremely important process in the isolation of people due to the prolongation of the global pandemic. At the same time, most people are always trying to simplify and automate all the basic life processes that usually take up a lot of free time. The same applies to the individual socialisation process. Machine learning and SEO technologies are extremely important in IS development and Big data analysis. Virtually every IP popular among many people uses appropriate socialisation mechanisms. The primary function of the IS of socialisation of individuals by common interests is to find relevant users, so the main task is to write an optimised algorithm that automates the process of users’ socialisation. In this case, a special algorithm was created based on Levenstein’s algorithm, sample extension, N-grams and the Noisy Channel model.

Analysis of Geo-Economic Distribution of Scientific Publications Citation and Self-Citation Standardized Indices Based on Machine Learning

Abstract: The article dwells upon the logical order of processing, transformation and synthesis of data windows, their visualization and analysis for geo - economic distribution research of articles authorship numerical characteristics, their citation, estimation, lack of linear and nonlinear relationships between individual parameters of author and percentage of self-citation. The work demonstrates the possibility of using new and classical methods of data visualization to study patterns, relationships between numerical and nominal data as well as methods of using conventional multilayer perceptrons to search for nonlinear relationships between multiple parameters. Open source software designed to build the necessary representations of data and models is the important part of the investigation.

Information Support for Personalities Socialization Processes Based on Common Interests

Abstract: The main objective of this article is to create an information system project for socialization by personal interests on the basis of SEO-technologies and methods of machine learning. The main purpose of this information system is to identify the user within the system using neural networks and to select similar users by analysing the user's current information. An information system was created that, through Identity and JWT tokens, provides optimized and secure authorization, logging, and support functions for the current system user session. Finding a face in a user's photo and checking the presence of a similar user in the database are implemented using convolutional and Siamese neural networks. The analysis and formation of similar user beeps were implemented using fuzzy search algorithms, the Levenshtein algorithm and the Noisy Channel model, which made it possible to maximize the automation of the user selection process and to optimize the time spent in this process. Tools have also been created to view other users’ profiles, preferences and private correspondence. All private correspondence and information about it are stored in the current database. Each user of the system can view all information about sent and received messages. The created information system implements the process of user identification, analysis, selection and further socialization of system users.

Visual simulation technology for passenger flows in the public transport field at smart сity

Abstract: Context. Today, the problem of visual simulation of passenger flow in public transport is essential in creating information systems for the development of modern Smart City. In Industry 4.0, it is crucial to develop technologies, means, and tools for implementing a single self-regulatory intelligent data exchange system in the provision of appropriate passenger transportation services in public transport. Today the following is essential: to visually display problem areas on routes in Smart City; to form and identify the main stops in time sections with the largest passenger exchange; to create proposals on the need to modernise routes taking into account the increase in public transport congestion in certain areas of Smart City, and to obtain results of passenger flow forecasting when making appropriate changes based on machine learning methods. Objective of the study is to develop a technology for visual simulation of passenger traffic in the field of public transport to improve the quality of passenger services in Smart City. Method. They have improved the simulation model for calculating passenger flow when changing the number of rolling stock on the route, in contrast to the known, added forecasting based on the developed neural network. The mechanism of visual simulation of passenger flows using GoogleMaps maps and dynamic movement on them with control of simulation display speed has been improved. A neural network with fully connected layers utilising an optimisation algorithm with an adaptive level of learning Adam to predict the flow of passengers between stops for a certain period of the day is proposed. Criteria for detailing passenger flows on urban routes are defined, including general indicators of the ratio of passenger traffic at a specific stop to the current period of the day. When designing the intelligent system, changing the capacity of public transport rolling stock in Smart City was further developed. Unlike the known ones, the available vehicles limit the change of power. The method of calculating a set of indicators of passenger traffic at stops and races, taking into account different local schedules and the specifics of transport on individual routes, has undergone further development. Results. An intelligent system of visual modelling of passenger traffic based on a neural network and machine learning has been developed, allowing optimising passenger traffic by public transport in Smart City. This data presentation makes it possible to assess the profitability of adding a new vehicle to the route or adjusting the schedule of other cars to cover the loaded areas during peak hours better. The well-known standard of public transport data presentation – GTFS is used for the operation of the software. It allows you to adapt the developed software product to the universal, rather than specific to a particular city or country. It was provided with a comparison of the obtained results on a data set of trolleybus routes (about 2000 records, collected based on experimental marketing research) in Lviv (Ukraine) to form a forecast of changes in passenger flow on certain sections at different times. Conclusions. It was found that the passenger flows predicted by the neural network in comparison with the actual ones lead to their growth by an average of 28% in critical races at rush hour. These results allow us to justify adding a schedule of a new vehicle for better coverage of loaded areas during peak hours. A comparison of changes in passenger traffic distributed by races during the day from 19:00 to 20:00, according to actual data and after the operation of the neural network indicates an increase in their average 70% of races that were predicted, which will allow a reasonable decision to launch additional transport on appropriate routes.

Mobile Information System for Human Nutrition Control

Abstract: It is acknowledged that each person's life, group of people and nation is formed depending on geographical, economic, political, cultural and religious conditions. Lifestyle is formed as a result of daily repetition and consists of the following factors: nutrition, exercise, the presence of bad habits, moral and spiritual development, and so on. In recent decades, lifestyle has been considered an integral part of well-being, leading to increased research. According to the scientist's study, more than half of health problems are related to diet. Millions of people eat incorrectly and are not even aware of it. The actuality of the theme: there are many approaches to solving the problem of diet control, but it should be understood that different analogues offer different opportunities that are not always clear and convenient. It is because there are several ways to achieve the same goal. The need for research on healthy eating in modern conditions is one of the priority tasks to improve the physical condition of different age groups. The aim is to create a system that will be aimed at helping the end-user to follow a healthy diet by determining the composition and caloric content of the product and the formation of recommendations based on the appropriate rhythm of life. The system is designed to solve specific tasks: to recognize products, correlate the product and its caloric content, form a food diary, remind the user about missed meals and keep statistics.

Information system for converting audio in Ukrainian language into its textual representation using nlp methods and machine learning

Abstract: Speech recognition involves various models, methods and algorithms for analysing and processing the user’s recorded voice. This allows people to control different systems that support one type of speech recognition. A speech-to-text conversion system is a type of speech recognition that uses spoken data for further processing. It also provides several stages for processing an audio file, which uses electroacoustic means, filtering algorithms in the audio file to isolate relevant sounds, electronic data arrays for the selected language, as well as mathematical models that make up the most likely words from phonemes. Thanks to the conversion of speech to text, people whose professions are closely related to typing a large amount of text on the keyboard, significantly speed up and facilitate the work process, as well as reduce the amount of stress. In addition, such systems help businesses, because the concept of remote work is becoming more and more popular, and therefore companies need tools to record and systematize meetings in the form of written text. The object of the research is the process of converting the Ukrainian-language text into a written one based on NLP and machine learning methods. The subject of the research is file processing algorithms for extracting relevant sounds and recognizing phonemes, as well as mathematical models for recognizing an array of phonemes as specific words. The purpose of the work is to design and develop an information system for converting audio Ukrainian-language text into written text based on the Ukrainian Speech-to-text Web application, which is a technology for accurate and easy analysis of Ukrainian-language audio files and their subsequent transcription into text. The application supports downloading files from the file system and recording using the microphone, as well as saving the analysed data. The article also describes the stages of design and the general typical architecture of the corresponding system for converting audio Ukrainian-language text into written text. According to the results of the experimental testing of the developed system, it was found that the number of words does not affect the accuracy of the conversion algorithm, and the decrease in percentage is not large and occurred due to the complexity of the words and the low quality of the microphone, and therefore the recorded file.

Analytical Method for Social Network User Profile Textual Content Monitoring Based on the Key Performance Indicators of the Web Page and Posts Analysis

Abstract: The article considers the development of methods and software for processing web pages and user messages in social networks, blogs, or forums. A method of textual content support based on analysing information about the comments from web pages of social network users is proposed. This method is built on the principles of web analytics and uses analytical data to evaluate web resources. The analytical method of text content support uses the analysis of key performance indicators to form many keywords to increase the potential audience of the blog or e-commerce sites. Software tools for technical support of textual content have been developed. A method of designing and implementing systems for monitoring textual content of Internet blogs and Internet forums, which reflect theoretical research results was proposed. From the standpoint of a systemic approach, propose the application of the principles of web resources processing for the implementation of the life cycle of textual content, which allowed to develop of a method of content support. The main problems of functional services for managing a web page or profile of users of social networks, blogs, and forums for their further promotion in search engines and attracting a potential/permanent audience are analyzed.

Modelling game task of assigning staff to perform it-projects based on ontolog ies

Abstract: Context. This article describes how to solve the game problem of assigning staff to work on projects based on an ontological approach. The essence of the problem is this. There is a need to create teams to carry out several projects. Each project is defined by a set of necessary ontological knowledge. To implement projects, managers invite qualified specialists (agents), whose abilities are also defined by sets of ontologies. The composition of the teams should be such that the combined ontologies of their agents cover the set of ontologies of the respective projects. Each agent with a certain probability can take part in the implementation of several projects. Simultaneous work of the agent on different projects is not allowed. It is necessary to determine the order of project implementation and the corresponding order of personnel appointment. Objective of the study is to develop a mathematical model of stochastic game, recurrent Markov methods for its solution, algorithmic and software, computer experiment, analysis of results and development of recommendations for their practical application. Method. A stochastic game algorithm for coloring an undirected random graph was used to plan project execution. To do this, the number of vertices of the graph is taken equal to the number of projects. The edges of the project graph for which the same agent is invited are connected by edges. Due to the recovery failures of agents, the connections between the vertices of the graph change dynamically. It is necessary to achieve the correct coloring of the random graph. Then projects with the same colored vertices of the graph can be executed in parallel, and projects with different colors of vertices – in series. Results. The article builds a mathematical model of a stochastic game and a self-learning Markov method for its solution. Each vertex of the graph is controlled by the player. The player’s pure strategies are the elements of the color palette. After selecting the color of their own top, each player calculates the current loss as a relative number of identical colors in the local set of neighboring players. The goal of the players is to minimize the functions of average losses. The Markov recurrent method provides an adaptive choice of colors for the vertices of a random graph based on dynamic vectors of mixed strategies, the values of which depend on the current losses of players. The result of a stochastic game is an asymptotically correctly colored random graph, when each edge of the initial deterministic graph will correspond on average to different colors of vertices. Conclusions. A computer experiment was performed, which confirmed the convergence of the stochastic game for the problem of coloring a random graph. This made it possible to determine the procedure for appointing staff to implement projects.

Intelligent System for Socialization of Individual's with Shared Interests based on NLP, Machine Learning and SEO Technologies

Abstract: The main objective of this work is to create an intelligent system for socialization by personal interests based on SEO technologies and methods of machine learning. The primary purpose of this intelligent system is to identify the user within the system using neural networks and to select similar users by analyzing the user's current information. An intelligent system is created that, through Identity and JWT tokens, provides optimized and secure authorization, logging, and support functions for the current system user session. Finding a face in a user's photo and checking the presence of a similar user in the database are implemented using convolutional and Siamese neural networks. The analysis and formation of similar user beeps are implemented using fuzzy search algorithms, the Levenshtein algorithm, and the Noisy Channel model, which made it possible to maximize the user selection process's automation and optimize the time spent in this process. Tools have also been created to view other user’s profiles, preferences and private correspondence. All personal mail and information about it are stored in the current database. Each user of the system can view all the information about sent and received messages. The created intelligent system meets the original goal, as it fully implements user identification, analysis, selection and further socialization of users of the system. The algorithm of sampling of users of similar interests implemented in the IS is 2530% more efficient and accurate than usual Levenshtein algorithm. At the same time, the implemented algorithm performs sampling 10 times faster than Levenshtein algorithm.

The virtual reality information system for the ISN LPNU department tours with elements of full immersion as a platform for an open day

Abstract: Virtual reality is an important information technology that allows to achieve significant progress in underserved areas. Immersive multimedia, or virtual reality, is a software-simulated environment that simulates physical presence in the real or imagined world. Innovative applications such as high-tech intelligent systems that correlate with the information technologies of display, modelling, building and maintaining networks, artificial touch and computer graphics have made virtual reality a breakthrough in the computing world. Excursions and distance learning in virtual reality are one of the ways to simulate the presence in a city in which a person cannot be physically present at the moment. When viewing virtual tours or attending online classes using videos/photos, the user (applicant/student/learner/teacher) only sees a flat image and cannot interact with it. In this way, the effect that the user is present in that place is lost. Virtual reality with the effect of full immersion allows to eliminate these disadvantages almost completely, and to provide the opportunity to interact with objects located on the virtual stage with the help of real body movements. In addition, in a short period of time, with the help of virtual reality, the user can visit many places, literally without leaving home. This is impossible to do in real life, as certain places are located at a great distance from the user. The object of the study is the process of conducting an interactive excursion and distance learning on the basis of the Department of Information Systems and Networks of the Lviv Polytechnic National University in virtual reality. The subject of the study comprises means, methods of designing and developing the virtual reality information system of excursions and distance learning using virtual reality information technologies. The practical significance of the obtained results is the implemented information system for conducting interactive excursions and distance learning on the basis of the university department. The scientific novelty of the obtained results is an information system based on the use of virtual reality, which is intended for online visits to the premises of the university department with elements of full immersion, as a platform for career guidance of students or distance learning of students.

Experimental and Exploratory Analysis of Programming Languages Popularity According to the PYPL Index

Abstract: This article dwells upon procedure for primary and exploratory analysis of data concerning programming languages popularity according to the PYPL index. Classical, but flexible methods of cluster and correlation analysis, and mathematical statistics have been used in data analysis. Examples of various researches in the field of popularity and development of programming languages have been provided, and based on open questions in this area, set of graphic results has been constructed by data analysis, which logically has resulted in conclusions regarding the selection of models and methods for further data forecasting. Integral part of the report information content consists of executable code examples in the R programming language, which can be used to conduct similar studies.

Relationships Knowledge Graphs Construction Between Evidence Based on Crime Reports

Abstract: In this paper, we proposed a structure based on a graph of knowledge for the chronology of homicide investigations. The knowledge graph construction needs the relationship analysis between the statistics of their crime reports for the following disclosure of criminal offences, particularly the disclosure of murders. Modelling the topic of crime demonstrates a more nuanced model of criminal incidents, which entails a massive loss of information obtained from unified reporting about the related criminal offences. We have proposed two performance indicators to assess crime theme models and demonstrated that choosing a thematic model has important implications for identifying crime hotspots. We have shown that more coherent topics that are simultaneously concentrated on a higher specific subject area can be achieved, allowing for more targeted police intervention given limited resources.

Система підтримки прийняття рішень для підтримки користувачів соціальних мереж на основі подібних спільних інтересів та вподобань

Abstract: На сьогодення соціалізація особистостей за спільними інтересами є надзвичайно важливим процесом під час ізоляції людей із-за подовженості світової пандемії. Паралельно більшість людей завжди намагаються спростити та автоматизувати всі основні життєві процеси, які зазвичай займають багато вільного часу. Це ж стосується і процесу соціалізації особистості. Машинне навчання та SEO-технології на даний момент є надзвичайно важливими в контексті розроблення ІС опрацювання та аналізу великих даних . Практично кожна популярна серед великої кількості людей ІС використовує відповідні механізми соціалізації. Головною функцією ІС соціалізації особистостей за спільними інтересами є пошук релевантних користувачів, тому основним завданням є написати оптимізований алгоритм, який максимально автоматизує процес соціалізації користувачів. В даному випадку створений спеціальний алгоритм на основі таких алгоритмів, як алгоритм Левенштейна, розширення вибірки, N-грам та моделі Noisy Channel. До наукової новизни одержаних результатів варто віднести розроблення нового алгоритму аналізу користувацької інформації та пошуку найбільш релевантних користувачів ІС відповідно до проаналізованого тексту повідомлень профілю на основі вже існуючих алгоритмів Левенштейна, розширення вибірки, N-грам та моделі Noisy Channel. Для створення динамічної ІС соціалізації використано шаблон асинхронного програмування. Удосконалено згорткову нейронну мережу, що дозволило ефективно здійснювати пошук людських обличь на фото та перевіряти наявність вже існуючих людей в БД ІС. Система дозволить ефективно та швидко здійснювати підбір, аналіз, опрацювання текстових даних та формування кінцевого результату. В системі використовуються SEO-технології для ефективного та якісного інтелектуального пошуку та опрацювання відповідних даних за потребою конкретного користувача. Нейронна мережа дозволяє ефективно здійснювати ідентифікацію користувача по його фото. Загалом використовувані алгоритми дозволяють створити зручну ІС соціалізації з використанням необхідних для цього алгоритмів. Варто зазначити важливість оптимізації наявної в ІС, в першу чергу це повна асинхронність системи, що дозволить уникнути всіх довгих очікувань та важких в плані опрацювання та аналізу запитів, система дозволяє ефективно та динамічно працювати з різними обсягами великих даних, здійснювати їх аналіз, опрацювання та формування нових даних необхідних користувачам ІС. Також використовується хмарний сервіс, який дозволить здійснити розподіл даних, відповідно можна буде зберігати всі найбільш важкі дані в хмарному середовищі і з використанням простого програмного інтерфейсу ІС за допомогою запитів здійснювати завантаження всіх необхідних даних. Таким чином, можна стверджувати, що створення даної ІС є важливим як і в соціальному плані, так і в плані реалізації всіх алгоритмів, які забезпечують необхідний функціонал ІС.