This paper presents a neuro-fuzzy algorithm for high-speed grayscale image segmentation based on a modified defuzzification method using triangular membership functions. The aim of the study is to analyze the effect of simplifying the defuzzification formula on the accuracy and contrast of object selection. The proposed approach includes adaptive learning of the weight coefficient, which allows dynamically adjusting the defuzzification process depending on the target values. The paper compares the basic method of averaging membership values and a modified version taking into account nonlinear weights. Experiments conducted on 1024x720 images demonstrate that the developed algorithm provides high segmentation accuracy and improved object contrast with minimal computational costs. The results confirm the superiority of the proposed method over traditional approaches, emphasizing the prospects for applying artificial intelligence in computer vision problems.

For support of adaptive training and educational analytics in the intellectual learning systems, it is necessary to collect, process data on progress of the student and his various individual characteristics. It can be realized by means of the student model. The analysis of approaches to modeling of the student has shown that application of several types of models is an optimal solution, considering requirements to the learning system. Three approaches were chosen and united into one model: overlay, Bayesian network, error model. Use of overlay model allows to build individual trajectories of student training. Bayesian networks realize competence-based approach in training. The model of mistakes keeps track of wrong knowledge of the student and helps the student to correct them at early stages. The student model uniting in itself these approaches is more suitable for realization of the personalized training, allows to keep track of progress of the student according to various characteristics and also gives the chance to easily submit the card of subjects, knowledge, competence of the student of various areas in the form of the count that is quite convenient and clear representation.