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.
Developing an intelligent system for the socialization of individuals is one of the critical tasks of modern social communication. Individuals attempt to optimize all life processes to save time and use it in the most efficient and optimized way. When searching for specific programs, users first choose those that save time, are simple and easy to use, optimize their work, and are automated enough to perform most actions instead of the system user. First of all, it is worth noting the time savings. Every day, people use at least dozens or even hundreds of different software systems, including smartphone applications, computer programs for the desktop, browser-based web applications, and many other programs. Almost every program has several analogues, giving a wide choice for the user and choosing the most convenient program. Understanding complex programs requiring a detailed acquaintance with all the system functions do not appeal to any potential users.
The main goal of our research is to create an intelligent system for the socialization of individuals that uses SEO technologies and machine learning methods. SEO technologies include the fuzzy word search algorithms necessary for the system to function using the Noisy Channel model with algorithms for efficient distribution of text information. Machine learning methods in this situation include a convolutional neural network for identifying system users. Accordingly, a system that uses modern technologies to perform its primary functions will be created. Such an intelligent system is available to almost all users, as it will be implemented as a browser-based web application. The system will allow all its users to register quickly, enter basic information necessary for the system to work and start convenient socialization with other system users.
The research object is the socialization of individuals, which is very important. All modern social networks try to optimize and automate the socialization of various users as much as possible using all popular modern technologies, such as neural networks and algorithms for analyzing user text. For the successful creation of an intelligent system for the socialization of individuals by common interests, the most critical task is to correctly understand and work out the process of socialization of users.
The subject of the research, in this case, is located in the middle of the object of research; it is the user of the intelligent system, whose primary goal is to carry out socialization. Accordingly, the system user is studied, namely, determining the user's authenticity by searching for a human face in a user's photos with the help of neural networks, fuzzy search algorithms, and the Noisy Channel model.
According to the source text, the scientific novelty of the obtained results includes designing a new algorithm to analyze user's information and search for the most suitable users. It is based on existing algorithms such as the Levenshtein algorithm, the sampling algorithm for data collection, the N-Gram algorithm, and the Noisy Channel model. The asynchronous programming template is developed, which makes it possible to create a completely dynamic system. The convolutional neural network, which facilitates an adequate search for human faces in photos and checks the people in the system's database, has been improved. The practical significance of the developed intelligent approach for the socialization of individuals is fundamental. First of all, it is socially meaningful since it performs a vital function of socialization of individuals, which is very necessary for our time. It also allows all users of the system to quickly and conveniently use all its parts.
Moreover, it is an innovative system as there are no similar analogues for the socialization of individuals who would use such algorithms and have an equivalent automation level. The system selects, analyzes, and processes text data and generates the final result efficiently and quickly. The system uses SEO technologies as any system performs the functions of searching and processing data. The neural network allows identifying the user based on their photo effectively. In general, developed algorithms enable the creation of a convenient intelligent system using the necessary algorithms. Optimizing the smart strategy is a complete asynchrony that avoids long waits and complicated queries in processing and analyzing. It makes the system work with different amounts of data, analyze them, process and generate new ones effectively and dynamically. Besides that, a cloud service that distributes data is applied. It will store all the heaviest data in the cloud environment and use a simple software interface to download all the necessary data.
Thus, we may argue that the development of the intelligent system is essential both in social terms and the implementation of all algorithms that provide its necessary functionality.
Machine learning and SEO technologies are currently significant in the process of developing intelligent systems. Almost every software system used by a large number of people possesses specific socialization mechanisms. For the correct and effective implementation of these mechanisms, it is necessary to optimize existing algorithms and design new ones to meet rigid specifications. A significant number of works study the optimization and implementation of machine learning algorithms, socialization, and SEO technologies. Some of them deal with the analysis of user's interaction within the social networks [1]. In the modern world, most people use social networks and spend a considerable part of their free time there, so analyzing their behaviour in certain situations while using social networks is essential and relevant. We study the main aspects of user interaction with each other. It involves analyzing sent messages and replies to them, which allows specifying certain system users' patterns and parameters and further automate the system operation. The implementation of mechanisms for analyzing and checking the current online user experience is highlighted. It is a significant point for dynamic social networks that track the main user actions to optimize the operation of the system mechanisms. Fundamental issues of moderation of internet content within social networks are considered to effectively analyze user texts and respond to specific violations of the tools.
In the article [2], the authors highlight a sample of data from analyzing social networks among different user groups by using paired parameters. The main results are received from the user interaction and studied based on their message communication. The use of a particular framework for the socialization of users in the social network Twitter is considered. The main features of hashtag formation and generated hashtags by users with different input parameters are studied.
We elaborated on the main questions that should be answered and algorithms that should be optimized to analyze possible information about the users more effectively. The concepts of additional algorithms that affect user information analysis are also developed in case of implementation.
The authors reflect important software issues in the article [3]; namely, the principles of building and maintaining efficient server systems that allow to withstand a large number of ongoing users and keep the system in a stable state, regardless of external factors, are considered. This problem is highly relevant since many people use social networks from phones and computers, and the number of users of the popular social networks can reach millions at a time. Another highly relevant issue for our work is to ensure the processing and information output for all users regardless of which systems and devices they have or what the internet speed is. The basic principles of building highly efficient server systems that allow parallel requests and, accordingly, can process requests asynchronously, forming a response to a request in particles, dynamically storing intermediate information are described. Ways to save the information and determine its reliability left by the user are considered; the main ways to avoid loss and distortion of information are highlighted, and ways to optimize the operation of the data storage system are taken into account. The main concepts of using neural networks in user interaction within social networks are studied, and the principal and effective forms of user text analysis are found out.
The primary information obtained from the neural network analysis of user messages left on Instagram is highlighted. We also analyzed the length of written letters and the factors which affect them. In the article [4], the author considers that specific mechanism designed for interaction between client and server systems highlight the main features of transmitting and storing intermediate information as efficiently as possible, avoiding data loss and forming middle requests from users. Despite the many tasks that solved ineffective social networks, many functions are developed to solve the problems. The main is multithreaded user data processing without losing system efficiency and asynchronous processing of incoming requests. They allow separating the client part from the server and transfer some work to the client to optimize the system. There is also an important task to fully implement the cross-domain data transmission model, which will maximize the formation of a request to the server and respond to the request due to generating and transmitting the current request.
In the article [5], the authors discuss the use of neural networks in social networks. Many people use social networks in the modern world, watch videos, listen to audio, upload and view photos. Neural networks are often used to automate and optimize specific actions. They have a wide range of applications, analysis of audio, video, photo formation, and analysis of text information. All this is hugely relevant in our time and actively studied in the process of creating software systems. Implementing a neural network that can set the basic needs of users by analyzing the list of their online purchases using specific parameters is considered. It shows how an implementation of a neural network can help identify users' moods and emotions out of messages left by them on social networks.
In the article [6], the author highlights how to determine the presence of users in a photo in the social network Instagram and proposes mechanisms for optimizing photo analysis using neural networks. The basic principles of neural networks for identifying various interconnected objects in selected photos on the examples of images with food are considered. The main mechanisms of interaction between users in social networks are defined, and a neural network that forms a sample of messages using the specified parameters is proposed. Neural networks are widely used; despite the demonstrated capabilities in analyzing text information, audio data, and photos, many problems need to be solved. The best solution is to use neural networks, which is a rather challenging task. It involves developing a mathematical model of the system from scratch and implementing its proper and practical testing. It will optimize the work of social networks and use convenient user interaction functions.
Interaction and analysis of users, their requests, and entered information in various socialization systems and online stores are essential issues that the author considers in the article [7]. Nowadays, there is a crucial task for online stores – they should optimize the product offer they want to find. To do this, one needs to analyze users. Their search queries create a particular sample of users for analysis and use algorithms to select the necessary information from a certain amount of text data. The article shows mechanisms for finding users based on specific normalized parameters and the subsequent formatting of the obtained data to automate user interaction. The basic principles of processing media files uploaded by users, namely photos and videos, are considered, and the basic principles of asynchronous storage of user data are described. The main algorithms of linear search of user information for determining the main parameters of interaction of people and factors that affect the data in user profiles are highlighted with the further data analysis. Implementing a neural network that determines users' forecasts and the results of football matches by analyzing pages in social networks of football clubs and user comments are considered.
The mechanism of adequate socialization of users is developed using the functionality of setting user preferences and optimizing the main processes of user interaction as a result of mutual socialization in one of the social networks. Implementing the primary mechanisms for determining and analyzing user needs using convolutional neural networks and algorithms for analyzing text data and determining and comparing the main selected parameters for a model user is highlighted.
In the article [8], the authors consider the main mechanisms of socialization of users who use the same hashtags in the social network Twitter and identify the features of their interaction.
The system features that analyze users' social networks and use fuzzy search algorithms among text data to determine the primary user needs are shown. Despite some solved problems, the problem of analyzing and processing user tools is still urgent. As a result of data separation and extraction, text analysis algorithms are constantly optimized and created using new algorithms.
Analysis and processing of user text information in various software systems and further determining user emotions and prediction of specific actions were brightly highlighted by the authors in the article [9]. Implementing a neural network that determines the emotions and mood of users by analyzing several photos of the user, normalizing information, and forming initial data is considered.
The main mechanisms of user socialization in the social network Facebook and its implementation are highlighted, and algorithms for user interaction are optimized over a certain period.
The features of analyzing user reviews for purchased products, implementing a sampling mechanism, and determining a certain percentage of irrelevant reviews are shown.
The main algorithms for analyzing the user's social networks that determine the user's primary emotions over a particular time are considered, and the fuzzy search algorithm is optimized. A mechanism for displaying targeted ads to the system user has been developed following the user's basic information, which is analyzed using fuzzy search algorithms. A selection of parameters for each user has been generated. Algorithms for analyzing server data and their further storage in the database are highlighted, and algorithms for storing data in the database in case of system load are developed.
In the article [10], the authors develop an algorithm for analyzing social networks of teenagers, design a neural network that determines the main parameters of teenagers, and optimizes their communication. Although the authors solve several problems, text analysis and determining the user's emotions are open. For information analysis, both neural networks searching for specific data in the text according to specified parameters and linear information search algorithms designed to process a large amount of text and analyze it being partially damaged are used.
Data analysis and further efficiency of information processing are very important for creating an intelligent system and are considered by the authors in the аrticle [11]. In the modern world, all software systems work with different data. The result of processing is a set of extra data analyzed using specific algorithms or using neural networks. Processing, storage, and use of the received data is a crucial stage in its development. It is necessary to combine constant and dynamic data to optimize the operation of the software product and increase the efficiency of work. A mechanism for determining the primary user's outcome using a mobile device is developed using a convolutional neural network that analyzes the user's work with a mobile device and generates basic information about a given user sample.
The author [12] considers a neural network that allows determining the approximate age of a user of the social network Instagram by searching for the user's face in photos and processing them. A mechanism for analyzing media data, namely movies and TV shows, is invented using a neural network to determine the minimum age of viewers allowed for watching media content. The algorithm for lowquality media files is optimized. The technology of analyzing voice messages of social network users is highlighted, and a neural network is developed that determines the user's emotions by analyzing these voice messages. The mechanism of saving and processing the user history of product searches in the online store is considered. A means for offering the most relevant products to the user is developed.
A mechanism for user socialization in the social network Facebook using the Levenshtein algorithm and a tool of asynchronous data storage has been developed. An optimized algorithm for fuzzy search of certain information when performing data search and text analysis is highlighted, and an optimized mechanism for asynchronous text analysis is developed. Despite the problem's solution, it is still vital to optimize the operation of system components and create new algorithms for further processing of the obtained data, making software systems more flexible and accessible.
The authors consider the use of information search algorithms in text data in the article [13]. It is essential to use the Levenshtein and fuzzy search algorithms. These algorithms are also flexible, enabling creating a separate implementation of the algorithm following the problem. The performance of modern information search mechanisms in social networks is considered, innovative algorithms that require optimization for faster data search and processing are presented, and algorithms for optimized data storage are highlighted. Mechanisms for analyzing user requests in online stores are studied. An algorithm for generating data generated as a result of query analysis is given. The ways of using the generated data to save information about a specific user are created. The implementation of algorithms for asynchronous dynamic calculation and data processing is specified. A method for partial data storage that avoids repeated overloading of the Web page and creates a more dynamic intelligent system is developed. The implementation of user socialization mechanisms using the information immobilization model is considered. It optimizes the data analysis process and performs analysis in several streams. The Levenshtein algorithm for user socialization is also optimized.
In the article [14], the author shows the mechanisms for analyzing user queries in online stores and considers implementing the fuzzy search algorithm among distorted data. It helps to restore the original lost information, select data parameters, and perform text analysis. The article also highlights the implementation of binary algorithms for data search in social networks. It forms the main aspects necessary to optimize data search and ensure the integrity of transmitted data under System load to protect them. As a result, we can say that there is still a problem of optimizing text data processing algorithms since each software system is unique. For each method, it is necessary to create a particular algorithm and optimize it according to the system's features.
The author highlighted the importance of using machine learning in modern Intelligent systems in the article [15]. Nowadays, all systems operate with a large amount of data; it is often necessary to distribute data to optimize and speed up system processes. It is achieved by processing part of the data using machine learning. Features of creating server programs are highlighted, algorithms for the interaction of server programs belonging to different hosts are optimized, an algorithm for optimized packet data transmission in particular objects is created, and algorithms for storing received objects are created. The implementation of text data analysis algorithms in social networks is considered. A neural network is created that compares user photos and text information left by the user and forms a sample based on the analyzed data, determines the type and main parameters of the system user, and optimizes the operation of search algorithms.
In the article [16], the author highlights the main algorithms for clustering data stored on mobile devices, forms the main patterns for saving processing, and developing a new data sample using the current one. It has optimized information search algorithms to avoid irrelevant data and create a helpful piece are presented.
The authors show the use of SEO technologies in software systems that involve user interaction in the article [17]. Nowadays, SEO technologies are essential for optimizing search algorithms and user socialization. An adequately implemented algorithm makes it possible to effectively use all the intelligent system features for searching and interacting with users to optimize the system's main processes. Implementing a linear algorithm for analyzing user text data is considered. An algorithm for constructing information diagrams created using the specified parameters for data processing and fuzzy search algorithms are optimized for a more accurate search of relevant information in a given text. The article highlights the implementation of data extraction mechanisms, creates a neural network that analyzes all social communication initially, and uses input parameters to throw out all irrelevant details saving only the necessary data sample at a particular moment. The implementation of data storage mechanisms from social networks in mobile devices is shown. Data storage on system servers and the device's local database is optimized, and data transmission and efficient storage algorithms are worked out. The authors also propose several new algorithms for analyzing users' social networks, such as neural networks. They analyze photos, and audio recordings of users and algorithms for analyzing user messages that are extremely important in creating global web applications as automating user information processing is one of the most critical problems [18-21].
To sum up, we can say that using SEO technologies is essential since there are many algorithms characterized by the authors in articles [22-26], which need to be optimized for the effective use of intelligent systems of user socialization. We need to create new practical algorithms to improve the operation of modern intelligent systems and carry out effective data processing [27-32]. When creating the selected method, an important task is to study and analyze existing analogues. With the help of this research, you can identify the main advantages and disadvantages of existing intelligent systems and, when creating a design, avoid existing penalties and repeat the main benefits [33-37].
It is worth noting that almost no intelligent systems similar to the intelligent design of socialization of individuals are found during the search for analogues. Among the programs studied, it is worth highlighting three analogues that are most similar in their functionality to the created intelligent system. These are Tinder, Badoo, and Chatous programs. If we consider each of these programs separately, we can highlight the main advantages of using this program and its disadvantages [38-41].
Tinder of all three listed above is the most popular system for socializing individuals, and it is one of the oldest compared to other programs. Its main advantage is the presence on all platforms; Tinder is used as a desktop program on all operating systems, an application for a smartphone, and the browser version. Another advantage is the photo analysis, the search for analogues of the face in the Tinder system itself, and the face presence in the photo. It guarantees that the program is used only by real people. When you meet or correspond with the user, you are sure that it is not a scammer. The complete absence of advertising, which allows you not to be distracted, adds to the program's benefits.
Among the disadvantages, it is worth highlighting that the program uses only basic user search filters, namely age, gender, and location, which only roughly narrow the search area of users. Still, the program does not have any algorithms for socialization and selection of users by common interests. Another disadvantage is the restriction on the number of users that can be viewed per day. Accordingly, the regulation can be lifted if you buy a paid subscription for 5$.
How the Tinder app works is shown in Fig. 1.
Badoo is a reasonably popular system for socializing individuals, which is the newest program of all these. The program's main advantage is its presence: it is used as a desktop program on all operating systems, as an application for a smartphone, and in the browser version. It is also worth noting that the program is entirely free respectively. It has no restrictions on the number of users that can be viewed per day, has an unlimited number of preferences and messages that can be set to users. Among the disadvantages, it is worth noting a considerable number of ads that cannot be turned off, which does not allow you to use the program fully conveniently. Another disadvantage is that Badoo does not have any algorithms for socialization based on shared interests and can use only basic user search filters. A screenshot of how Badoo works and the main screens of the program are shown in Fig. 2.
The last app is Chatous, the least popular app of all listed. The main advantage of this program is that it is entirely free to use, has an unlimited number of likes, messages, and user views throughout the day. Also, this program completely lacks any advertising. The advantages include analyzing the user's photos and confirming the user's face in the image, ensuring that we communicate with a real user.
Among the disadvantages is that this program is not available on all platforms, but only as a browser program, limiting the number of possible users of the program. Its main drawback is the absence of algorithms for selecting users by interests, which does not allow you to search for users optimally. There are only basic algorithms for filtering the main user parameters, which is inefficient since most users will not meet search expectations, as they will not have common interests.
A screenshot of how Chatous works and the main program screens are shown in Fig. 3.
All advantages and disadvantages of analogue programs are shown in a comparative Table 1.
After analyzing the data obtained in Table 1, we can conclude that an essential point in creating an intelligent system is the absence of advertising or its minimum number. Also, the advantage over Tinder will be an unlimited number of users, likes that can be sent to users, and messages that can be written throughout the day. Also, the advantages of Tinder and Badoo apps are the availability of apps to work on all platforms, which significantly expands the number of potential users. The main result of the analysis is the algorithms that the systems under study provide. It is necessary to implement a photo analysis algorithm that can determine a human face in the photo and search for similar pictures in the system database. Another critical point is the use of basic user search filters available in all three programs. The main advantage of the user socialization intelligent system based on shared interests should be implementing socialization algorithms, which are not available in all three programs. It will optimize the user search process as much as possible by analyzing the user's interests and essential information and selecting the most relevant users of the system. 3. 3.1.
The main goal and aspects of the system's functioning are formulated using the goal tree to describe the intelligent system. In this case, the system is regarded as an «Intelligent system for the socialization of individuals by common interests (G).» It is designed to store and analyze all system users, processes the input information, compares users' interests, and enhance their further socialization [42-49]. It outlines only a general understanding of what the intelligent system should do. A more detailed explanation of the system's operation is necessary to specify its main aspects [50-53]. The first level includes four main elements: 1) data transfer (A1), 2) data аnalysis (A2), 3) data storage (A3), and 4) data generation (A4). In this case, all aspects of the first level of a detailed goal tree are presented, which gives a specific idea of the basic algorithm of systems, but it is still not enough to fully formulate the system's features. The second level includes: structuring (B1), functionality (B2), security (B3), integrity (B4), and scalability (B5).
According to the third level, the criteria that are sub-criteria belong to the second level: structurings consist of patterning (B1.1) and hierarchy (B1.2), functionality consists of operability (B2.1) and representation (B2.2), security consists of recoverability (B3.1) and reserve capacity (B3.2), integrity consists of synchronicity (B4.1) and dependency (B4.2), scalability consists of adaptability (B5.1) and mobility (B5.2). In this case, the fourth level of the hierarchy will be the last. It contains two alternatives: an information-analytical system and an information-reference system, indicated in the diagram as alternative 1 (C1) and alternative 2 (C2), among which it is necessary.
Fig. 4 shows a goal tree of the intelligent system containing four levels of hierarchy. The next step is to prioritize hierarchical elements. Values from 1 to 5 were used in the evaluation. One means a similar deal, 3 represents a weak value, 5 indicates a substantial weight, and 2 or 4 means intermediate values between neighbouring elements. Table 2 shows the constructed matrix of pairwise comparisons. n o
Using the Thomas Saati algorithm, we found the weights of criteria that received the following values: data transfer – 0,357, data analysis – 0,389, data storage – 0,192, data generation - 0,062.
The next step was to find the largest value: λmax = 0,357 * (1 + 0,333 + 0,5 + 0,2) + 0,389 * (2 + 0,333 + 2 + 1) + 0,192 * (1 + 0,25 + 3 + 0,5) + 0,062 * (3 + 5 + 1 + 4) = 2,07 + 0,725 + 0,912 + 0,806 = 4,213. The consistency index was found CI = (4,213 - 4) / 3 = 0,071 and the sequence index CR = 0.071 / 0.90 = 0.078 was also calculated. It is worth noting that 0.90 is a value taken from the random index table, since the current index we need is 4.
The next step is to build a pairwise comparison matrix to analyze the second level of the hierarchy based on the first level, as shown in Tables 3.
3 3 1 2 3
5 3 4 1
3 2 0,5 1 0,333
2 1 0,333 3 1
After performing calculations taken from matrices of pairwise comparisons of system criteria similar to those performed at level 1 of the hierarchy, the following priority values were obtained for level 2 of the hierarchy. For data transfer: structuring – 0,164, functionality – 0,182, security – 0,255, integrity – 0,089, scalability – 0,310. For data analysis: structuring – 0,164, functionality – 0,182, security – 0,255, integrity – 0,089, scalability – 0,310. For data storage: structuring – 0,164, functionality – 0,182, security – 0,255, integrity – 0,089, scalability – 0,310. For data generation: structuring – 0,164, functionality – 0,182, security – 0,255, integrity – 0,089, scalability – 0,310.
Next, global priorities were calculated according to the criteria of the 2nd hierarchy level and their sub-criteria obtained from the 3rd level. The main criteria were selected: structure, functionality, security, integrity, and scalability. A matrix of pairwise comparisons for the third level of the hierarchy relative to the second level concerning all criteria is created and shown in Table 4. After performing all the necessary calculations, the values of global priorities shown in Table 5 were obtained. Then the consistency check was completed, the importance of all λmax, CI, CR were found, and as a result, it was proved that all CR values do not exceed the limit of 10% (Table 5). The last step was to build matrices according to the size of available alternatives, where comparisons are made based on level 3 criteria. The resulting matrices are shown in Table 6.
After analyzing the matrices of pairwise comparisons of the criteria of the last level of the hierarchy relative to the two current existing criteria of the Intelligent System, the weights of the current alternatives to each criterion were obtained, the global weights were calculated as the sum of the products of the data of global values for the second level of the hierarchy, namely: structuring, functionality, security, integrity, and scalability on the values for the current criteria taken from the third level of the hierarchy, namely: patterning, hierarchy, operability, representation, recoverability, reserve capacity, synchronicity, dependency, adaptability, and mobility.
Alternatives have the following values: A1 (intelligent-analytical system) = 0.602, A2 (informationreference system) = 0.398. In conclusion, we can say that the intelligent-analytical system is the best alternative according to all criteria. 3.2.
After constructing the goal tree and related works [21-49], the system functions were further specified, and IDEF0 functional diagrams were selected to detail the system structure (Fig. 5-9).
First of all, to generalize the main functions of the created intelligent system, a contextual diagram was created, which consists of the main input and output data and primary data for displaying mechanisms and controls. Input data includes user login, user password, database, user photos, and user request. Initial aspects include user socialization and an updated database. n I : 6 n E “ h P d d A “ D : 8 e C “ s o p l e S “ s i g e R “ s D : 1 1
Among the main elements of control, it is necessary to highlight access levels and requirements that affect the processes of the intelligent system. The system's primary functions are performed by the system's mechanisms: interaction algorithms, convolutional neural network, fuzzy search algorithms, and data saving algorithms. Fig. 12 shows the decomposition of the Log in block. Fig. 13-15 show the decomposition of the "Add photo,” "Check for a face in the photo," and "Check for a face in the database" blocks. optimize the algorithm's operation since you can avoid using complete data collections for storing text and dynamically feed data in separate elements. A flowchart of the algorithm is shown in Fig. 32. Figure 33: N-Gram algorithm
After dividing the data into two subsamples, the first subsample is taken, and the N-Gram algorithm is used to process it. The algorithm itself is pretty old and has been used for a long time. It is simple to implement, easily modified according to specific requirements, and works relatively quickly. Thus, it is the fastest fuzzy search algorithm among those used in the intelligent system to socialize individuals with shared interests. If you describe the algorithm's operation, it takes the generated data array and compares it element-by-element with the compared data array. The comparison itself is carried out using a straightforward formula. If word one coincides with word two, taking into account some errors, then there is a high chance that they will have a standard string of length N. A flowchart of the algorithm is shown in Fig. 33.
While it is indexed, the desired word is divided into such N-grams that the average probability of similarity of a word match is more than 50% and falls into a separate list of words. Most often, trigrams are used as strings that consist of three letters. The algorithm has certain disadvantages. They may occur when creating all the trigrams of lines. Many words can fall out of the sample, even if they fit the search parameters of the word since there may be a word that does not match the division and will be mistakenly not selected by trigrams before that sample expansion algorithm was used to minimize the possibility of having similar words in the first subsample.
It is believed that most of these words fell into the second sample, so the Noisy Channel model is used with it. It is designed to process all the words that were most likely distorted in a certain way. It treats them as an additional dictionary of possible meanings, which will be another place to store and compare words from the second subsample, so a collection of possible distorted words is developed.
The Noisy Channel model is prolonged to use due to the constant formation of a middle dictionary during text processing. To optimize it, we created a selection of most likely distorted words not to waste extra resources. A flowchart of the Noisy Channel model is shown in Fig. 34.
The operation of the Noisy Channel model is shown on the example of the word “actress.” The idea is to replace similar words using certain word processing operations. If there are no false letters, the correct term is selected, as shown in Table 8.
The internal algorithm for processing fully formed word samples is the Levenshtein algorithm. The main point of the algorithm is to determine the distance between several sequences of characters. The algorithm at the output determines a particular value, namely the number of necessary substitutions for one word to match the compared word in case of differences. It forms a percentage that shows the chance of word combinations and determines whether two similar words got into the sample. The algorithm is optimized using a finite automaton, so all intermediate data is deleted from memory with a change in iteration. First, there is a formation of two subsamples of words out of the users' data input. Each of the two subsamples is additionally worked out to increase the accuracy of the word search. Then, using the Levenshtein algorithm, there is a direct comparison of word samples of all system users, and each word is compared with the corresponding parameters of the user under study. A flowchart of the algorithm is shown in Fig. 35.
During the comparison, points are generated for each match: three points for matching interests, two points for the search parameter, and one point for general information. Before starting the Levenshtein algorithm, the maximum score of the current user is developed since all unnecessary words have already been thrown out, and we can assume that the score is formed based on keywords. The Levenshtein algorithm work is shown while comparing the words “elephant” and “relevant” in Table 9. When each word matches, users are awarded points, which are summed up with each iteration. The last step is to compare the total score of the current user and the searched one, based on which user similarity is formed. Having received a ready-made sample of users, we can interact with them. The intelligent system for the socialization of individuals with shared interests provides all the necessary functions that are intuitive for the average user. So, the GetUsers method is designed to display the generated list of system users. First, the current user is searched so that his profile is not thrown into the sample, and then his ID is excluded from the available selection.
The directive Response.AddPagination generates the server's response to the request from a sample of users. It passes the current page to display users, the page size, the number of elements, and the total number of pages. The method returns the generated user sample following the original parameter, which was used to create the piece with all the generated page data. It is followed by the GetUser method, which returns a specific user to view the user's profile. The technique takes a unique user ID and returns the found user in the current database or returns a response that an error occurred and the user with this ID does not exist. The UpdateUser method follows it and takes as parameters both the user's ID whose data you want to update and the object which contains all the updated data sent from the application client. First of all, it checks whether the current user is available in the database and logged in. Then the database is accessed, and information about the user is updated asynchronously. If there is a storage error in the database, an exception is thrown. The code and cause of the error are also logged inside the database and stored in the appropriate list. The next step is to apply the LikeUser method, designed to mark the user's preferences. First, it checks the system user authorization, and if the current user is not registered, then an error message is thrown, which does not allow performing the same action. Otherwise, if there are no errors, a new Like object is created, added to the database, and a request is sent to the user registered. The next step is to apply the CreateMessage method for sending messages. It has two main parameters: the sender ID and the object of the message itself. The sender is searched in the database, then if the search is successful, the sender ID is saved, and the recipient of the message is searched using the database ID. If the search is successful, the message itself is generated. All message properties that need to be saved are written to a particular variable that is storing data. First, the message is stored asynchronously in the database. Then an anonymous object is generated and sent to the users using the routing mechanism.
A unique user manual is created to understand how to use the intelligent system to socialise individuals with shared interests.
Goal. The intelligent system of the socialization of individuals is designed to demonstrate the principles of interaction of system users. For this purpose, a neural network is used to identify users and text analysis algorithms to select suitable users.
Abstract. The intelligent system is designed for socializing users. The application task is to register users in the system, enter data, and upload their photos. The aim is to socialize users of the system. The input data is user information and user photos. The output data generated by the system is a selection of users sorted in descending order of the percentage of user similarity. Also, the program's features include the option to like the selected user, have private correspondence within the system, update all personal data, and view read and sent messages.
Designation and name of the program. Full name: Intelligent system for the socialization of individuals with shared interests based on SEO technologies and machine learning methods. Abbreviated name: Intelligent system for the socialization of individuals with shared interests.
Programming languages. C#, JavaScript, TypeScript, ASP .NET Core MVC, Angular.
Program assignment. Socialization of users by forming a percentage of user similarity based on the specified information.
Program features. The intelligent system identifies the user's face in the photo and puts a specific one as the main. It also performs user socialisation, forming a list of suitable users, selecting a user, putting a like, and carrying out private correspondence inside the system.
Our objectives are: registering and authorizing users inside the system, saving user information, saving user photos, analyzing user photos and searching for faces in photos, changing the primary image, forming a list of suitable users and forming a percentage ratio, saving data about selected users, keeping private messages, saving pictures in a cloud service, dynamically loading data.
Methods for solving problems. Identity tools and JWT tokens are used for registration and authorization, and information is saved inside the database using the Entity Framework Core Orm functionality. The Cloudary service is used to save photos, convolutional and Siamese neural networks are used to analyze images, internal functions of the system are used to change the cover photo, and a list of suitable users and forming a percentage ratio is performed using algorithms such as the Levenshtein algorithm, the sample expansion algorithm, the N-gram algorithm and the Noisy Channel model. Session retention methods, methods for storing private messages, and storing information of each message separately are used for user interaction. The task of dynamically loading data is performed using asynchronous methods and “reactive” programming methods.
Functions. The following procedures are used: Register – for user registration, Login –for user authorization, GetUsers – for generating a list of users and displaying the generated list, GetUser – for displaying complete information about the selected user, UpdateUser – for updating user information, LikeUser – for marking a preference for the selected user, GetPhoto – for choosing the main photo, AddPhotoForUser –for searching for a face and adding a photo, SetMainPhoto – for setting the main photo of the user, DeletePhoto – user photo deletion function, GetMessage – for selecting a single message from the list, GetMessagesForUser – for displaying sent, received, or unread messages, GetMessageThread – for showing all messages from the dialogue between users, CreateMessage –for creating and saving the message, DeleteMessage – for deleting the message, MarkMessageAsRead – for marking the message as read.
Time. The program optimizes all tasks performed since there are no page reloads and all requests are asynchronous. Photo analysis by neural networks occurs asynchronously using “reactive” programming. Text analysis algorithms are improved for processing large amounts of data. The system itself advances user socialization, as it generates the percentage of user’s similarities and allows choosing the best one.
Operating mode. The system is created as a web application. Accordingly, it works around the clock and starts working when the first user requests inside the system. The JWT token is used for the first request and registration/authorization of the user. A session is created for 24 hours, all intermediate data uploaded or selected are saved and cleared at the end of the session.
Monitoring tools. There are two types of errors in the system: data loading errors and page loading errors. Almost all system functions return either an error message or emergency message in the event of an error without damaging the entire system. If the page loads incorrectly, the user is redirected to a particular web page with an error message.
Program limitations. The main rules of the program are: it cannot guarantee the reality of the system user based on the photo, accurately generate the percentage of users, be used by an unauthorized user, process data offline.
Conditions required for the program performance. The main limitations are: a registered and authorized user can only use the program to create a profile. The system user must have their photo set as the main one with all the data filled in. The user should have access to the Internet.
Technical and software tools. The intelligent system is created in the form of a web application so, the user of the system must have a computer or phone with internet access and a browser.
Parameters of peripheral devices. Peripherals should include a keyboard and mouse for entering data and a monitor for displaying data.
Software requirements. Among the software tools, you only need a browser, preferably the latest version with Internet access and the function to use cookies. The user should install any working operating system and drivers for using the keyboard, mouse, and monitor.
Organizational, technical, and technological requirements and conditions. For the system to work correctly, you must have constant access to the internet, cookies should be enabled in the browser, and data input and output devices should function properly.
Under the standard regulations, a user manual is written. It is divided into two sections: an introduction and the primary information about the program. The introduction section indicates general information for the user, and the central part presents information about the program, its purpose, functions, and programming languages in it is developed.
The classes of tasks solved are described. They include the program's main functions, how exactly the program performs them, and the tools to solve all the necessary procedures are indicated. The main characteristics and features of the program are demonstrated: the time characteristics, operating modes, restrictions within the program, and the means of monitoring and self-restoring.
Information on the functional limitations of the program application is provided. It covers the optimal conditions for the correct execution of the program, the software and hardware requirements, and a list of peripherals and application software tools. In addition, the leading software requirements and the conditions under which the program can fully perform all the necessary functions are described.
The control example shows the main functions and work of the created intelligent system. Fig. 36 shows the main screen of the program. Fig. 37 shows the user registration form. Fig. 38 shows the user registration form. Fig. 39 shows the user's authorization, login, and password entry, and Figure 4.15 shows a message upon successful authorization.
Fig. 44 shows uploaded photos of the user. Users can delete all images except the cover photo, and neural networks also process all images. The image with no face on it is not available for displaying the user's cover photo. Fig. 45 shows the generated list of users using text processing algorithms and sorted in descending order by the percentage of user similarity.
Fig. 50-52 shows the selected user's basic profile information, a tab with the user's interests, and a tab with all the user's photos.
Fig. 53 shows a tab of private correspondence with the user. It offers user nicknames, photos, time of sending and reading messages. Fig. 54-55 demonstrate a page with information about all messages unread, received, and sent. You can manage notifications, namely, view the selected message by tracing a dialogue with the user, or delete the selected message for everyone or only for yourself.
Fig. 56-57 show logging in from another user's profile selected by the first user of the system and viewing the list of users who put a like mark. It allows you to start a private correspondence. Fig. 58 shows a personal correspondence between the registered user and the one selected by the system.
After receiving the implemented intelligent system, a statistical analysis of two system parameters was carried out, namely, comparing the speed of user sample formation and the accuracy of the obtained percentage ratio. The combination of Levenshtein algorithms, N-gram, sample extension, Noisy Channel model, and the standard Levenshtein algorithm is most often used in similar systems for the socialization of individuals, being implemented they are respectively compared.
First, we analyzed the efficiency of forming a sample of users and selected twelve system users. Then we created a selection for each one using the Levenshtein algorithm and a combination of algorithms. The diagram shown in Fig. 59 demonstrates that the combination of algorithms is more efficient and accurate by about 25-30% comparing with the usual Levenshtein one. Next, the speed of forming a sample of users was analyzed. We again selected twelve users of the system and developed an example using a combination of algorithms and the standard Levenshtein algorithm. The resulting diagram is shown in Fig. 60. It can be concluded that the combination of algorithms implemented in the system makes the sample about ten times faster than the standard Levenshtein algorithm.
During the practical implementation of the user socialization system with shared interests, the created software tool, all algorithms, and system functions were described. All the features and procedures for performing the user socialization process were highlighted. A user manual was also created to provide basic information about the designed intelligent system. We also analyzed a control sample in which the main steps of performing the main algorithms and functions of the created intelligent system were demonstrated. The main goal of the work is to develop a smart strategy for the socialization of individuals with shared interests based on SEO technologies and machine learning methods. It is developed using fuzzy word search algorithms, the Noisy Channel model with algorithms for efficient distribution of text information, and a convolutional neural network to identify users of the system. No analogue system exists to analyze the specified information about the user and form a list of the most relevant users. Creating an intelligent system for the socialization of individuals is an urgent task since, in the modern world, people try to optimize all life processes to save time. When searching for specific programs, users first choose those that save time, optimize their work, and are automated enough to perform most actions instead of the system user. Our intelligent system combines two critical tasks: socialize users, maximize and automate the socialization process itself.
The design of a budget assessment will show that the development of this software product is profitable. The created intelligent system offers unique user search and analysis opportunities, and large software development companies will be interested in buying this software product. Marketing assessment reveals that the system meets the users' needs as it includes functionality that is not available in similar software products. It is easy and convenient to use and offers an entirely new approach to searching, analyzing, and socializing users within the system.
Currently, the software market is developing rapidly, and new software products are constantly emerging that affect users' lives using the created intelligent systems. The most popular are browserbased web applications, applications for smartphones implemented using a client-server data transfer model, ongoing working online programs frequently used by different users. It embraces perspectives for further development of various software products. Similar products on the market include all existing systems designed for finding and socializing users, which are usually free to use but have paid subscriptions. Larger companies purchase the intelligent systems themselves to expand their software products. The primary users are the people whose main goal is to find new friends with similar interests. Also, this search should be fast and easy to use, automated to search for people, and effectively save time. There are many similar programs, but there are several competing systems that have identical functionality. Among the programs found, it is worth highlighting three analogues similar to the intelligent system being created. They are Tinder, Badoo, and Chatous programs. If we consider each of these programs separately, we can identify the main advantages and disadvantages that need to be considered when creating a system. In analyzing all the available information, we can assume an excellent opportunity to create a new software product aimed at the mass user since everyone will search for people with shared interests. Also, the designed product offers options that analogues do not possess, which makes it competitive and unique among similar programs in the market.
In our time, the socialization of individuals according to shared interests is an essential process since most people try to simplify and automate the main life processes, which usually take up a lot of free time. The same applies to socialization, and the developed intelligent system plays an essential role in this regard. In performing the work, the top scientific results related to this topic and play a significant role in forming an understanding of the main implementation processes of the intelligent system of socialization according to shared interests were analyzed. We investigated the main algorithms that play an essential role in social networks and various user interaction systems. Algorithms that are best suited for the current intelligent system were identified. The use of modern neural networks and their importance in automating the operation of an intelligent system in data processing issues were investigated. While studying the system analysis, a decision tree was built to outline the essence of the intelligent system and determine the primary purpose of the system's functioning. The intelligent system structure was also specified using IDEF0 and IDEF3 diagrams, and the hierarchy of system processes was constructed using a tree structure.
Choosing the right software solutions for writing an intelligent system is essential since most of its features depend. We worked with the programming languages C# and JavaScript. Their main frameworks ASP .NET Core and Angular, together with the corresponding software code development environments such as Visual Studio and Visual Studio Code, made it possible to write a multi-level system. It is divided into server and client parts, dynamically and completely asynchronously interact with each other during the system work. The practical implementation of the intelligent system of the socialization of individuals with shared interests is the most crucial step. Reliable and straightforward functionality of registration and authorization using identity and JWT tokens methods were developed. It helped securely store user passwords in the database, optimize the creation of a session, and provide all the necessary functionality while working in the system. Next, two neural networks were implemented: convolutional and Siamese, which allowed us to search for a human face in the photos that the user uploads and compare the found face with the available faces in the database. It helped us effectively identify the user's authenticity and guarantee that this user is currently not in the database, so he is real. An algorithm for analyzing and comparing user information was created using fuzzy search algorithms, the Levenshtein algorithm, and the Noisy Channel model. It generates a list of available users of the system, sorted in descending order of the percentage of user similarity. It indicates how much the interests of other users coincide with the interests of the current user. The algorithm of sampling of users of similar interests implemented in the IS is 25-30% more efficient and accurate than usual Levenshtein algorithm. At the same time, the implemented algorithm performs sampling 10 times faster than Levenshtein algorithm. We achieved our goals and objectives in work, but there are still many things that can be improved inside the created intelligent system. Still, the functionality available inside the intelligent system corresponds to the goal of its creation. All system users can register, including personal data, upload photos with their faces and then carry out adequate socialization. It includes viewing users from the generated list, checking the user's preferences, and conducting private correspondence within the system with the selected user. Finally, a discussion of the results follows, highlighting the most important findings and their interpretation, the data limitations, and the perspective for further research. Our findings put forward a theory that gives a strong perspective for further research and development of new inexpensive catalysts with superior CO tolerance and durability. 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