Pattern Recognition Theory Research Articles

DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET

Currently, an increasing amount of research is directed towards solving tasks using computer vision libraries and artificial intelligence tools. Most common are the solutions and approaches utilizing machine and deep learning models of artificial neural networks for recognizing gestures of the Kazakh sign language based on supervised learning methods and deep learning for processing sequential data. The research object is the Kazakh sign language alphabet aimed at facilitating communication for individuals with limited abilities. The research subject comprises machine learning methods and models of artificial neural networks and deep learning for gesture classification and recognition. The research areas encompass Machine Learning, Deep Learning, Neural Networks, and Computer Vision. The main challenge lies in recognizing dynamic hand gestures. In the Kazakh sign language alphabet, there are 42 letters, with 12 of them being dynamic. Processing, capturing, and recognizing gestures in motion, particularly in dynamics, pose a highly complex task. It is imperative to employ modern technologies and unconventional approaches by combining various recognition methods/algorithms to develop and construct a hybrid neural network model for gesture recognition. Gesture recognition is a classification task, which is one of the directions of pattern recognition. The fundamental basis of recognition is the theory of pattern recognition. The paper discusses pattern recognition systems, the environment and application areas of these systems, and the requirements for their development and improvement. It presents tasks such as license plate recognition, facial recognition, and gesture recognition. The field of computer vision in image recognition, specifically hand gestures, is also addressed. The development of software will enable the testing of the trained model's effectiveness and its application for laboratory purposes, allowing for adjustments to improve the model.

Sensitivity Study of Feature Vectors Formed on the Basis of Multiple Scale Transformations of Processed Images

Introduction. Under the conditions of globalization, transportation of oil products by tanker fleets becomes one of the causes of man-made disasters in the water areas of seas and oceans. In this context, environmental monitoring acquires particular significance as a tool for ensuring timely detection of negative consequences of man-made disasters. This task is facilitated by recognition of images obtained from unmanned aerial vehicles with selection of those depicting the traces of man-made accidents or their consequences.Aim. To develop approaches for carrying out automatic selection of input data obtained from unmanned aerial vehicles in the form of photo and video images at the preliminary stage of image recognition.Materials and methods. The theoretical part of the study employed a classification method based on pattern recognition theory. Mathematical processing and calculation were carried out in the MATLAB environment. Simulation was conducted using the MathCAD environment.Results. A series of experiments was conducted to select a basis for discrete wavelet transforms. Modeling was conducted using the study results of the sensitivity of the feature vectors of images formed on the basis of different types of wavelet transforms. A concept of image construction for the purposes of feature vector formation was developed.Conclusion. An approach to the formalization of images for the purposes of feature vector formation is proposed. A metric of their contrast estimation is substantiated. It was established that the sensitivity of a recognition system based on the representation of images in the form of discrete wavelet transform matrices depends not only on the type of the mother wavelet, but also on the value of the scale parameter.

APPLICATION OF STATISTICAL METHODS AND THE THEORY OF PATTERN RECOGNITION IN NON-DESTRUCTIVE TESTING PROBLEMS

A mathematical model is proposed to solve the problem of identifying anomalous areas in composite products using methods of mathematical statistics and pattern recognition theory.

Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model

Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.

On the Work of the Institute of Control Sciences of the Russian Academy of Sciences in the Field of Pattern Recognition Theory and Applications in the 20th Century

On the Work of the Institute of Control Sciences of the Russian Academy of Sciences in the Field of Pattern Recognition Theory and Applications in the 20th Century

INTRUSION DETECTION SYSTEMS AND METHODS: CURRENT SITUATION AND DIRECTIONS FOR IMPROVEMENT

The structure of modern intrusion detection systems (IDS) is reviewed. The main directions of recognition of security violations of protected systems are characterized in modern IDS. The analysis of the used methods and models of the structure of IDS was carried out according to the defined main groups. The main disadvantages of the current IDS are given and directions for their improvement are justified. Intrusion detection systems (IDS) are systems that collect information from various points of a protected computer system (computer network) and analyze this information to identify both attempts to breach and real security breaches (intrusions). It has been shown that in modern detection systems, the following main elements are logically distinguished: the information collection subsystem, the analysis subsystem, and the data presentation module. Each intrusion detection method has been reviewed separately and the disadvantages of the methods have been highlighted. The current situation regarding intrusion detection systems and methods is described and directions for improvement are indicated. Among the methods used in the analysis subsystem of modern IDS, two directions can be distinguished: one is aimed at detecting anomalies in the protected system, and the other is aimed at finding abuses. Each of these directions has its advantages and disadvantages, therefore, in most of the existing IDSs, combined solutions are used based on the synthesis of the corresponding methods. It was noted that there are two groups of methods: with controlled training (“training with an instructor”), and with unsupervised training (“training without an instructor”). The main difference between them is that supervised training methods use a fixed set of evaluation parameters and some a priori knowledge about the values of the evaluation parameters. The main methods of detecting anomalies are explained in detail and detailed in the Tables. It has been pointed out that the methods currently applied in IDS are based on the general concepts of pattern recognition theory. Several main methods of creating an “image” in modern IDS are discussed and the topic of choosing the optimal set of functions for evaluating the protected system is highlighted. Information about Bayesian statistics, Covariance matrices, Confidence networks (Bayesian networks) is reflected. Pros and cons of descriptive statistics, Neural networks, Pattern Generation, abuse detection methods are also discussed. It has been explained that the methods currently implemented in IDS are based on the general concepts of pattern recognition theory. In accordance with them, in order to detect an anomaly, an image of the normal functioning of the information system is formed on the basis of an expert assessment. This image acts as a set of evaluation parameter values. The data presentation subsystem is necessary to inform interested parties about the state of the protected system. Some systems assume the existence of groups of users, each of which controls certain subsystems of the protected system. Therefore, in such IDSs access control, group policies, permissions, and etc. is applied. In the end, the disadvantages of the existing detection systems were noted and recommendations were given for improving the IDS. It has been noted that due to the presence of a significant number of factors of various nature, the functioning of the information system and IDS has a probabilistic nature. Therefore, it is relevant to substantiate the type of probabilistic laws of specific parameters of functioning. Of particular note is the problem of substantiating the loss function of an information system, which is set in accordance with its objective function and on the area of the parameters of the system functioning. At the same time, the objective function should be determined not only at the expert level, but also in accordance with the totality of the parameters of the functioning of the entire information system and the tasks assigned to it. Then the IDS quality indicator will be defined as one of the parameters that affect the objective function, and its admissible values will be determined by the admissible values of the loss function. Based on the foregoing, it can be concluded that considerable experience has been accumulated in practical activities in solving intrusion detection problems. The applied IDS are largely based on empirical schemes of the intrusion detection process, further improvement of IDS is associated with the specification of methods for the synthesis and analysis of complex systems, the theory of pattern recognition as applied to IDS. Keywords: IDS, optimal function, Covariance matrix, Bayes statistics, detection systems.

Assessing Environmental Management System: The Case of Dukem Town, Oromia Special Zone Surrounding Finfinne, Ethiopia

The paper highlights an approach to solving problems of medical diagnosis. The problems are formulated in terms of pattern recognition theory. An original algorithm is proposed. The combined information in the form of objects and rules is used for its training. Indirect justification of the algorithm with the use of the resolution method is presented. Keywords : pattern recognition, algorithms, resolution method, Diagnosis problems, decision support systems. DOI : 10.7176/CEIS/10-1-03

A Pattern Recognition Method for Offshore Drilling Gas Kick and Overflow Diagnosis

In offshore drilling, accidents such as gas invasion, overflow, and kicks are unavoidable, and they can escalate into blowouts and other catastrophic events, resulting in casualties and significant economic losses. Therefore, ensuring drilling safety requires precise monitoring of gas invasion and overflow. Currently, most overflow monitoring methods used at drilling sites are based on threshold criteria. However, the monitoring parameters obtained during actual drilling operations often contain noise signals, which makes it challenging for threshold-based methods to achieve a balance between improving accuracy and minimizing false positives. This paper proposes a novel method called Pattern-Recognition-based Kick Detection (PRKD) for diagnosing overflow in offshore drilling. The PRKD method utilizes the overflow evolution process by integrating multiphase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. By analyzing the shape and wave characteristics of the curves, PRKD effectively detects and monitors gas intrusion and overflow based on single parameters. Through case analysis, it is demonstrated that the proposed method achieves high precision in monitoring drilling overflow while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations. The case analysis shows that by integrating the probabilistic information of pre-drilling kicks and various characteristic parameters, when the noise amplitude is less than 8 L/s, the PRKD model exhibits superior detection performance. Moreover, when the noise amplitude is 16 L/s, the PRKD model detects the continuous overflow approximately 200 s after the actual overflow occurs and predicts a 96.4% probability of overflow occurrence at the specified location, meeting the on-site requirements. The gas invasion monitoring method proposed in this paper provides accurate diagnostic results and a low false positive rate, offering valuable guidance for gas invasion monitoring in drilling operations.

Information Processing with Stability Point Modeling in Cohen–Grossberg Neural Networks

The aim of this article is to develop efficient methods of expressing multilevel structured information from various modalities (images, speech, and text) in order to naturally duplicate the structure as it occurs in the human brain. A number of theoretical and practical issues, including the creation of a mathematical model with a stability point, an algorithm, and software implementation for the processing of offline information; the representation of neural networks; and long-term synchronization of the various modalities, must be resolved in order to achieve the goal. An artificial neural network (ANN) of the Cohen–Grossberg type was used to accomplish the objectives. The research techniques reported herein are based on the theory of pattern recognition, as well as speech, text, and image processing algorithms.

Remote monitoring and control of the status of transformers in distribution electrical networks

RELEVANCE. One of the most important expected effects of digitalization of objects of the power grid complex of the Russian Federation is to increase the level of reliability of its functioning. In this regard, research and development in this direction is undoubtedly relevant. THE PURPOSE. To propose a mathematical model for monitoring dangerous developing defects in power oil-filled transformers, this would meet the properties of predictability and adaptability. Based on the model, to develop a decision-making algorithm for long-term reliable operation oftransformers. METHODS: Methods of statistical pattern recognition theory, correlation analysis and Bayesian classification will be used to solve the problems to ensure high reliability of diagnostic assessments, validity and effectiveness of operational solutions. RESULTS. A predictive model was obtained and verified in the form of a correlation function of a sign of a faulty state of a transformer from the values of its electrical load. An event tree has been formed that restores the causal relationship between the result of monitoring the transformer, the defect sign and the operational decision being made. Based on the event tree and diagnostic evaluation criteria, a control algorithm is implemented, with the help of which calculations are performed confirming the effectiveness of the proposed approach. CONCLUSION. The possibility of effective application of the developed defect recognition model and operational control algorithm as tools of industrial technology of the Internet of Things is illustrated, in particular, when organizing remote diagnostic monitoring of oil-filled transformer equipment at substations of the distribution grid area.

A multi-state target recognition method based on multi-classifier fusion

A multi-classifier target recognition algorithm based on fuzzy pattern recognition and fuzzy comprehensive evaluation theory is proposed, where the base classifiers are deployed as fuzzy pattern recognition classifiers, and the secondary classifiers are based on fuzzy comprehensive evaluation theory. On the basis of time-frequency domain analysis of target signals, fuzzy theories such as fuzzy pattern recognition, decision-making and comprehensive evaluation are synthetically applied to carry out first-level evaluation, where the extracted feature parameters are used as the input of target base classifiers. With the evaluation results as the input of second-level evaluation, target state recognition is performed on the multi-state human targets behind the walls in the ultra-wideband radar sensor network based on fuzzy evaluation. The results show that the recognition can efficiently discern the Multi-status human being behind the wall.

Development of a verified osteoarthritis risk scale based on a cross-sectional study of clinical and anamnestic parameters and pharmacological anamnesis of patients

Objective: the development and verification of a scale for identifying patients at high risk of osteoarthritis (OA).Material and methods. The results of the analysis of a sample of patients aged 35–90 years from the database of the Institute of Trace Elements (n=3440), which included information on clinical and anamnestic, diagnostic parameters and pharmacotherapy of patients, including patients with OA (n=107), are presented. To analyze information about patients, modern methods of data analysis proposed within the topological theory of pattern recognition were used.Results. Based on the analysis of the sample, a 100-point scale of 27 points was developed which allows to identify patients at high risk of OA (sensitivity 88%, specificity 100%). For patients with high scores on the scale, a kind of “preemptive” prescription of symptomatic slowacting drugs for osteoarthritis (SySADOA) based on highly purified substances of chondroitin sulfate (CS) and glucosamine sulfate (GS) is promising. The evidence for CS/GS is extensive and has been reviewed previously.Conclusion. Early adoption of preventive measures (including the usage of SySADOA – CS/GS) in patients with a high score on the developed scale may reduce the risk of OA.

A short history of innate immunity

Innate immunity refers to the mechanisms responsible for the first line of defense against pathogens, cancer cells and toxins. The innate immune system is also responsible for the initial activation of the body's specific immune response (adaptive immunity). Innate immunity was studied and further developed in parallel with adaptive immunity beginning in the first half of the 19th century and has been gaining increasing importance to our understanding of health and disease. In the present overview, we describe the main findings and ideas that contributed to the development of innate immunity as a continually expanding branch of modern immunology. We start with the toxicological studies by Von Haller and Magendie, in the late 18th and early 19th centuries, and continue with the discoveries in invertebrate immunity that supported the discovery and characterization of lipopolysaccharide (LPS) and pattern recognition receptors that led to the development of the pattern recognition and danger theory.

Статистические характеристики сигнала на выходе оптимальной радиолокационной системы распознавания подвижных воздушных объектов

This work can be considered as a continuation of the article, or as separate materials devoted to the recognition of moving air objects based on the theory of testing statistical hypotheses. In the future, when presenting the materials of this article, the terminology proposed in [1] will be used. The relevance of this article is due to the events taking place on the European continent, indicating the need for further development of radar systems for various purposes and the transition from radar to radio vision, which will significantly improve the effectiveness of air defense and airspace control in general. The construction of a radar portrait and further automatic recognition of moving air objects, as an element of artificial intelligence, will eliminate human error, as well as significantly reduce the time it takes to make a decision on the necessary measures to influence the detected aircraft. Studying the freely available scientific works of various scientists devoted to such a direction of research and development of artificial intelligence as the statistical theory of pattern recognition, one can note the absence of such an important element as the function of the dependence of the probability of correct recognition on the quality and noise of the image. Within the framework of this article, a variant of solving this problem in the form of a mathematical model will be proposed. Since in [1] a recognition method is considered that uses the correlation coefficient between the current image and the reference one, and the probability of correct recognition is estimated using the Neumann-Pearson criterion, it is proposed to plot the dependence of the probability of correct recognition on the ratio of the image energy EI to the spectral noise density (R = 2EI/N0R), for a given false recognition probability . In the future, in the presented work, this dependence will be called recognition characteristics. Graphs will also be considered, in this paper referred to as the distribution density of the recognition probability.

Intellectualization of Moving Transport Objects Recognition

The purpose of the article is to research, analyze, and consider current problems and prospects for the development of software for the recognition of transport objects based on the use of pattern recognition theory, methods, and tools of artificial intelligence, and different types of neural networks.
 The research methodology is basic methods and algorithms of pattern recognition, methods and means of artificial intelligence, and different types of neural networks. The article considers the main problems of intellectualization of processes occurring in transport. The main attention is paid to the intellectualization of the processes of transport objects’ recognition. The article analyzes the most common recognition methods. A study of these methods and approaches to the recognition of moving vehicles is conducted. It is determined which methods have high and which have low computational complexity. Among the considered methods are those that recognize static transport objects (primarily transport infrastructure objects) using intelligent technologies, statistical, probabilistic, and other methods. The main attention is paid to methods that recognize dynamic transport objects. The basis of intellectualization of the processes of recognition of this group of objects is the use of neural networks, in particular convolutional, recurrent, neural networks with long short-term memory (LSTM), etc.
 The scientific novelty of the research is the analysis of modern methods of moving transport objects’ recognition, the results of which can be used in the development of their software product. The article emphasizes that the proposed modern approach to the recognition of transport objects (moving vehicles and transport infrastructure) involves solving a wide range of problems based on the use of intelligent technologies, in particular neural networks.
 Conclusions. The most common methods for solving current problems of recognition of transport objects (vehicles and transport infrastructure) have been investigated and analyzed. Based on the analysis of methods for recognizing moving vehicles, it has been determined which methods have high and low computational complexity. The basis of the intellectualization of the recognition processes for this group of transport objects is the use of various neural networks.

Cluster approach to criteria evaluation of the quality of a student’s educational outcome

Problem and goal . The issues of criteria-based evaluation of the student's educational results remain relevant for the modern theory and practice of education. As a rule, measures to monitor educational results and resources in educational institutions are carried out by expert, manual, non-automated methods. In accordance with the directions of digital transformation of education, it is necessary to create a technological assessment system that meets the requirements of modern society, subject to automation and intellectualization. The purpose of the work is to substantiate a new model of criteria-based assessment of the quality of the educational result, based on the mathematical methods of the theory of clustering and pattern recognition and allowing to automate the procedures for assessing the quality of educational objects, resources, educational and personal achievements of students. Methodology. The quality of an educational result or resource is determined by criteria indicators, which can be represented as features of the evaluated object using the information vector. By clustering the set of acceptable objects into three classes - with low, medium and high quality - it is possible to evaluate an object by its belonging to one of these classes. Clustering is carried out on the basis of a mining algorithm, the metric of city blocks is taken as a measure of the similarity of objects. Results. A program has been developed that consists of a source data module, a clustering module, and a recognition and training module. The model results of the program correlate with traditional rating assessments, in which the quality of the object is determined by a point scale. The obtained test results confirm the validity of the recognition algorithm and the correctness of the software product. Conclusion. Thus, the proposed model based on clustering and the recognition method showed the possibility of automated assessment of the quality of educational results of trainees and educational resources.

Question and criterion method for assessing the quality of the organization’s digital educational environment

It is of interest to create a technological, accessible and convenient method for evaluating multi-component and multi-dimensional educational systems, with the maximum share of automation and intellectualization of all related events. The purpose of the study is to substantiate the question and criterion method for assessing the quality of the digital educational environment of an organization based on mathematical methods of the theory of clustering and pattern recognition (using the example of monitoring the development of the digital educational environment of additional education practices for children). Methodology. The quality of educational systems or resources can be assessed using their inherent criteria, presented in the form of an information vector. The current values of these indicators determine the rating of a given system in a set of similar systems, and the dynamics of their changes over time shows the degree of development of each criterion sign. Monitoring sheets describing each system with an information vector represent a plurality of objects that can be clustered into certain classes. From a mathematical point of view, it is convenient to divide such systems into classes using a mining algorithm, and to take the metric of city blocks as a measure of the similarity of objects. Results. Monitoring of the organization's digital educational environment is carried out according to the functional characteristics of the environment based on the assessment of the organization's official website. According to the pyramid method, a question tree was developed on the functional components of the digital educational environment of additional education practices for children, according to which an information vector of the environment was formed, the values of which were determined by experts. Assessment of sites is carried out according to expert estimates in the automated system of competitive procedures “ASCO.” Conclusion. The proposed method allows monitoring the digital educational environment of an organization using mathematical methods of clustering theory and pattern recognition.

Поиск зон экстремальности на основе алгоритмов дискретного математического анализа для выявления рисков при бурении по геофизическим данным

Despite the impressive list of examples of the integration of pattern recognition theory into various activities in the development of oil and gas fields, the authors propose a fundamentally new approach to the use of artificial intelligence. The paper considers in detail the algorithm for searching for extremity zones, based on discrete mathematical analysis (DMA), as applied to the problem of identifying geological hazards. The application of the method is shown on models of the physical properties of rocks reconstructed from seismic data. Potentially, it can also be applied directly to the wave seismic field to identify objects.

EXTERNAL CRITERION OF STABILITY OF INTRA-MULTIPLE DISTANCES IN TASKS OF DIAGNOSIS OF STATES OF TECHNICAL OBJECTS

The purpose of the research is adaptation of biclasticization criteria, which have the properties of external complement to the problems of classification of states of complex technical objects in the field of computer engineering. The principles of inductive modelling of complex systems, in particular, the principle of external complementarity, as well as the methodology of the theory of pattern recognition, methods of inductive cluster analysis, mathematical statistics are applied. The construction of the criterion of stability of intra-multiple distances is based on the schemes of known criteria of self-organization of models, in particular the criterion of model inconsistencies, which are widely used in multi-step image recognition algorithms with intelligent choice of optimal results. The paper proposes the application of the criterion of stability of intra-multiple distances in the problems of diagnosing the state of technical objects, in particular, in the field of computer engineering. Since the application of such a criterion requires the presence of a target feature, one of the options for splitting the original experimental database into two subsets has been adapted: a subset of target features and a subset of input parameters. The concept of intra-multiple distances is extended to the application of algorithms of optimal complexity in the criteria of self-organization algorithms. Intelligent algorithms for self-organization of models of optimal complexity can be used to increase the reliability of computer systems.

Entropy-Argumentative Concept of Computational Phonetic Analysis of Speech Taking into Account Dialect and Individuality of Phonation.

In this article, the concept (i.e., the mathematical model and methods) of computational phonetic analysis of speech with an analytical description of the phenomenon of phonetic fusion is proposed. In this concept, in contrast to the existing methods, the problem of multicriteria of the process of cognitive perception of speech by a person is strictly formally presented using the theoretical and analytical apparatus of information (entropy) theory, pattern recognition theory and acoustic theory of speech formation. The obtained concept allows for determining reliably the individual phonetic alphabet inherent in a person, taking into account their inherent dialect of speech and individual features of phonation, as well as detecting and correcting errors in the recognition of language units. The experiments prove the superiority of the proposed scientific result over such common Bayesian concepts of decision making using the Euclidean-type mismatch metric as a method of maximum likelihood and a method of an ideal observer. The analysis of the speech signal carried out in the metric based on the proposed concept allows, in particular, for establishing reliably the phonetic saturation of speech, which objectively characterizes the environment of speech signal propagation and its source.