A model for classifying information objects using neural networks and fuzzy logic

This work is aimed at developing intelligent systems capable of automatically classifying types of educational materials. This will allow students to find the resources they need faster, and it will make it easier for teachers to manage content in educational platforms. The solution of the problem of recognition of information objects using fuzzy output systems and neural networks is considered. This approach combines the advantages of neural machine learning with the flexibility and efficiency of fuzzy logic, making these systems effective tools for solving problems related to fuzzy or uncertain data. An information model of a neural network for classifying information objects in e-learning systems has been developed. Experimental testing of the proposed approach was carried out on a data set, which consists of information objects from real e-learning systems, namely such as manuals, lectures, syllabuses, and textbooks. Design an adaptive mechanism that utilizes fuzzy neural networks to personalize content recommendations for students based on their learning progress and preferences, thereby enhancing the effectiveness of e-learning systems. Improve the efficiency of classification algorithms by fine-tuning neural network parameters and fuzzy logic rules, ensuring high accuracy and computational efficiency when processing large-scale educational datasets. The results of experimental studies have shown that a neural network built based on fuzzy logic is able to classify various information objects efficiently and correctly in e-learning systems. It is shown that the integration of neural networks based on fuzzy logic into e-learning systems to improve the processes of classification of information objects makes it possible to increase the efficiency of educational resources management, ensuring accuracy and flexibility in processing various data.