Given the inherent diversity in learning styles and rhythms, the current educational landscape demands continuous adaptation toward methodologies that enhance individualized learning. This study addresses the effectiveness of learning personalization using machine learning models to adapt educational content to individual learning styles. Focusing our attention on a cohort of 450 university students, we implemented classification algorithms and neural networks to diagnose learning styles and personalize educational resources accordingly. The results are revealing: the students’ average grades experienced a significant increase, going from 70 to 75 points on a scale of 100 after the personalized intervention. Additionally, increased engagement was recorded, evidenced by more substantial interaction with educational materials tailored to their learning preferences. These findings suggest that personalization of learning is a powerful and effective tool that can improve both academic performance and students’ educational experience. This work confirms the relevance of educational personalization supported by artificial intelligence and provides a practical model for its effective implementation. The implications of this study are particularly pertinent to the evolution of pedagogical practices and curriculum design in the digital age.

Personalization of Learning: Machine Learning Models for Adapting Educational Content to Individual Learning Styles

Nowadays, in production and in everyday life, there is a question of improving the quality and efficiency of grinding systems. These can be both cement and grain factories, as well as coffee shops that use machines for grinding coffee. Effective management of the grinding process is of great importance for increasing the productivity and quality of the final product. Management of the grinding process is a complex task because many factors affect the process. In addition to the factors, the degree of wear of the cutting surfaces, the backlash in mechanical transmissions, which increases the distance between the grinders, and the material from which they are made, must also be taken into account during the analysis. Mathematical models describing the grinding process cannot be completely perfect, because they contain a limited number of factors that affect the process and all of them are defined in different physical quantities. Also, when developing a mathematical model, it is practically impossible to take into account dynamic changes in the object of control, which can be both linear and non-linear. All these features of a complex object of control make it necessary to move to the creation of a situational system with fuzzy logic, which will provide an opportunity to take into account all the features and complexities that arise when creating an «image» of the object of control. For the development of a situational system with fuzzy logic, the method of constructing a fuzzy conclusion of the Mamdani situational type, most suitable for the tasks of metrological support, was chosen. With the help of a graphical user interface, it was possible to build the situational system with fuzzy logic, which made it possible to establish the second level of the information and measurement system, where the technological and metrological «image» of the control object is formed, taking into account the influence of external and internal factors, the necessary technological regimes that will positively affect the uniformity of grinding.

/pdf/development-of-a-situation-system-with-fuzzy-logic-for-the-35nc5h9v.pdf

Development of a situation system with fuzzy logic for the second level of the information and measurement system

Modular exponentiation is an important operation in many applications that requires a large number of operations. Efficient computations of the modular exponentiation are extremely necessary for efficient computations for provide high crypto capability of information data and in many other applications. Modular exponentiation is implemented using of the development of the right-to-left binary exponentiation method for a fixed base with pre-computation of redused set of residuals. To efficient compute the modular exponentiation over big numbers, the property of a periodicity for the sequence of residuals of a fixed base with exponents equal to an integer power of two is used. The multithreading software implementation of modular exponentiation is described. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation. Comparison of the runtimes of four variants of functions for computing the modular exponentiation is performed. In the algorithm with pre-computation of residues for ﬁ xed-base provide faster computation of modular exponentiation compared to the functions of modular exponentiation of the MPIR, OpenSSL and Crypto++ libraries.

Efficient Multithreading Computation of Modular Exponentiation with Pre-computation of Residues for Fixed-base

Context. The problem of aggregating the decision table attributes values formed out of group expert assessments as the classification problem was solved in the context of structurally rough set notation. The object of study is the process of the mathematical models synthesis for structuring and managing the expert knowledge that are formed and processed under incompleteness and inaccuracy (roughness). Objective. The goal of the work is to develop a set of mathematical models for group expert assessments structuring for classification inaccuracy problem solving. Method. A set of mathematical models for structuring the group expert assessments based on the methods of the theory of evidence has been proposed. This techniques allow to correctly manipulate the initial data formed under vagueness, imperfection, and inconsistency (conflict). The problems of synthesis of group decisions has been examined for two cases: taking into account decision table existing data, only, and involving additional information, i.e. subjective expert assessments, in the process of the aggregation of the experts’ judgments. Results. The outcomes gained can become a foundation for the methodology allowing to classify the groups of expert assessments with using the rough sets theory. This make it possible to form the structures modeling the relationship between the classification attributes of the evaluated objects, the values of which are formed out of the individual expert assessments and their belonging to the certain classes. Conclusions. Models and methods of the synthesis of group decisions in context of structuring decision table data have been further developed. Three main tasks of structuring decision table data gained through the expert survey has been considered: the aggregation of expert judgments of the values of the decision attributes in the context of modeling of the relationship between the universe element and certain class; the aggregation of expert judgments of the values of the condition attributes; the synthesis of a group decision regarding the belonging of an object to a certain class, provided that the values of the condition attributes are also formed through the expert survey. The proposed techniques of structuring group expert assessments are the theoretical foundation for the synthesis of information technologies for the solution of the problems of the statistical and intellectual (classification, clustering, ranking and aggregation) data analysis in order to prepare the information and make the reasonable and effective decisions under incompleteness, uncertainty, inconsistency, inaccuracy and their possible combinations.

Development of mathematical models of group decision synthesis for structuring the rough data and expert knowledge

Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.

The model of decision support system using hybrid method and actual weighting for the study program ranking

Context. The urgent task of increasing the functional efficiency of machine learning of decision support system (DSS) for assessing compliance with content modern requirements of the educational disciplines of the graduation department based on the results of the employer survey has been solved. Objective. Increasing the functional efficiency of machine learning of DSS for assessing compliance with modern requirements of the educational disciplines content of the first (bachelor’s) level specialty educational and professional program based on machine learning and pattern recognition. Method. The method of machine learning of DSS is proposed for adapting the educational content of the graduation department to the labor market requirements. The idea of the method is to maximize the information capacity of the DSS in the machine learning process, which allows in the monitoring mode to guarantee a high full probability of making the correct classification decisions. The method was developed as part of a functional approach to modeling cognitive processes of natural intelligence, which makes it possible to provide DSS with flexibility when retraining the system due to increasing the power of the recognition classes alphabet. The method is based on the principle of maximizing the amount of information in the machine learning process. The modified Kullback information measure, which is a functional of the accuracy characteristics of classification solutions, is considered as a criterion for optimizing machine learning parameters. According to the proposed functional category model, an information-extreme machine learning algorithm was developed based on the hierarchical data structure in the form of a binary decursive tree. The use of such a data structure allows you to automatically divide a large number of recognition classes into pairs of nearest neighbors, for which optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. The geometric parameters of hyperspherical containers of recognition classes were considered as optimization parameters, which were restored in the radial basis of the binary space of Hamming features in the machine learning process. At the same time, the input traning matrix was transformed into a working binary training matrix, which was changed in the machine learning process through admissible transformations in order to adapt the input information description of the DSS to the maximum reliability of classification decisions. Results. The informational, algorithmic, and software of the DSS was developed to assess the educational content quality based on the machine analysis results of respondents’ answers. Within the framework of the geometric approach, based on the information-extreme machine learning results, highly reliable decisive rules, practically invariant to the multidimensionality of the recognition features space, were constructed based on the hierarchical data structure in the form of a binary decursive tree. The influence of machine learning parameters on the functional effectiveness of machine learning of the DSS was studied on the evaluation example of the educational content of the educational and professional bachelor’s program of the specialty 122 Computer Science. Conclusions. The computer modeling results confirm the high functional efficiency of the proposed method of information-extreme hierarchical machine learning and can be recommended for practical use in institutions of higher education to assess compliance with modern requirements of the educational content of graduation departments.

Machine learning decision support systems for adaptation of educational content to the labor market requirements

Context. Modular exponentiation is an important operation in many applications that requires a large number of calculations Fast computations of the modular exponentiation are extremely necessary for efficient computations in theoretical-numerical transforms, for provide high crypto capability of information data and in many other applications. Objective – the runtime analysis of software functions for computation of modular exponentiation of the developed program that uses the precomputation of redused set of residuals for ﬁ xed-base. Method. Modular exponentiation is implemented using of the development of the right-to-left binary exponentiation method for a fixed basis with precomputation of redused set of residuals. To efficient compute the modular exponentiation over big numbers, the property of a periodicity for the sequence of residuals of a fixed base with exponents equal to an integer power of two is used. Results. Comparison of the runtimes of five variants of functions for computing the modular exponentiation is performed. In the algorithm with precomputation of redused set of residuals for ﬁ xed-base provide faster computation of modular exponentiation for values larger than 1K binary digits compared to the functions of modular exponentiation of the MPIR and Crypto++ libraries. The MPIR library with an integer data type with the number of binary digits from 256 to 2048 bits is used to develop an algorithm for computing the modular exponentiation. In the work has been considered and analysed the developed software implementation of the computation of modular exponentiation on universal computer systems. One of the ways to implement the speedup of computing modular exponentiation is developing algorithms that can use the precomputation of redused set of residuals for ﬁ xed-base. The software implementation of modular exponentiation with increasing from 1K the number of binary digit of exponent shows an improvement of computation time with comparison with the functions of modular exponentiation of the MPIR and Crypto++ libraries.

The modular exponentiation with precomputation of redused set of residues for fixed-base

The article aims at evaluating the effectiveness of distance learning supported with the use of modern technologies and web tools for practical classes in learning the discipline “The Latin language and medical terminology” at medical universities, working with foreign students. Modern technologies and web tools for organizing distance learning have been analyzed at every stage of the practical class: the preparatory stage, the main stage and the final stage of practical class. Their didactic purpose has been determined: at the preparatory stage: – to transfer and present theoretical material; – to give useful educational tips; – to check the initial level of knowledge; – to motivate students for taking the active role in studying; at the main stage: – to present educational information in the interactive way; – to do practical training in a real-time interaction, asking and answering questions; and at the final stage: – to sum up the topic of the lesson; – to control and evaluate students’ knowledge. The comparison of distribution of students’ achievements of the discipline “The Latin language and medical terminology” during traditional (in 2018-2019 studying year) and distance learning (in 2020-2021) has been conducted. Research has shown that the use of modern technologies and web-tools for organizing distance learning provides the same academic performance of the foreign students as they are obtained during traditional learning. The disadvantages of distance learning can be seen in the bad quality of internet connection, the technical features of computers and mobile devices can be insufficient, gadgets can be broken or not functioning properly. of modern technologies and web tools for practical classes overweight the disadvantages, furthermore the distance learning helps foreign students get knowledge at the qualified level during the Covid-2019 pandemic.

The use of modern technologies and web tools for organizing distance learning at medical universities

Context. The analysis of the risk terminal flows in technogenic systems is carried out, which arise in the process of the impact of informational and cognitive threats in the automated document management system as part of the hierarchical production system. The object of the research is the process of functioning of complex systems with a hierarchical structure, in which automated document management systems with a high level of data flow protection for decision-making are used to provide the information quality control of technological processes

Analysis of risk terminal flows in technogenic systems arising in the process of threat impact

Context. Because assessing information security risks is a complex and complete uncertainty process, and uncer-tainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to non-calculated data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of information security professionals. Therefore, it will be advisable to use models that can adequately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of information security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Mo-dels/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized. Objective. Development of the structure of a fuzzy model of quality of information security risk assessment and protection of ERP systems through the use of fuzzy neural models. Method. build a model for calculating the risk assessment of information security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized. Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters is substantiated and the structure of the fuzzy product model of risk assessment and the basis of the rules of fuzzy logical conclusion is developed. The use of fuzzy models for solving problems of information security risk assessment, as well as the concept and construction of ERP systems and analyzed problems of their security and vulnerabilities are considered. Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of information security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of linguistic variables of the model is defined. The structure of the database of fuzzy product rules – MISO-structure is chosen. The structure of the fuzzy model was built. Fuzzy variable models have been identified.

Developing a fuzzy risk assessment model for erp-systems

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