Information-Extreme Machine Learning of On-Board Vehicle Recognition System

Dovbysh, A. S.; Budnyk, Mykola; Piatachenko, Vladyslav; Myronenko, Mykyta

doi:10.1007/s10559-020-00269-y

Cited by 8 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the problem of forming a learning path, various mathematical tools are used: Petri nets [16], graph theory [17], artificial intelligence technologies [4,5,18,19], decision trees [20] and others. However, the application of these approaches does not make it possible to obtain a predicted value of the number of points in the final testing.…”

Section: Related Work Analysismentioning

confidence: 99%

“…The formation of a learning path as an optimization problem was considered in papers [11][12][13][14][15][16][17][18][19][20][21][22][23]. However, these works did not take into account such individual psychophysiological indicators of a student as cognitive comfort [24] and functional state [25], which significantly affect learning outcomes.…”

Section: Related Work Analysismentioning

confidence: 99%

See 1 more Smart Citation

Mathematical Model for Adaptive Technology in E-learning Systems

Barchenko¹,

Tolbatov²,

Lavryk³

et al. 2022

IJMECS

View full text Add to dashboard Cite

The emergence of a large number of e-learning platforms and courses does not solve the problem of improving the quality of education. This is primarily due to insufficient implementation or lack of mechanisms for adaptation to the individual parameters of the student. The level of adaptation in modern e-learning systems to the individual characteristics of the student makes the organization of human-computer interaction relevant. As the solution of the problem, a mathematical model of the organization of human-computer interaction was proposed in this work. It is based on the principle of two-level adaptation that determines the choice of the most comfortable module for studying at the first level. The formation of an individual learning path is performed at the second level. The problem of choosing an e-module is solved using a fuzzy logic. The problem of forming a learning path is reduced to the problem of linear programming. The input data are the characteristics of the quality of student activity in the education system. Based on the proposed model the computer technology to support student activities in modular e-learning systems is developed. This technology allows increasing the level of student's cognitive comfort and optimizing the learning time. The most important benefit of the proposed approach is to increase the average score and increase student satisfaction with learning.

show abstract

Section: Related Work Analysismentioning

confidence: 99%

Section: Related Work Analysismentioning

confidence: 99%

Mathematical Model for Adaptive Technology in E-learning Systems

Barchenko¹,

Tolbatov²,

Lavryk³

et al. 2022

IJMECS

View full text Add to dashboard Cite

show abstract

“…The use of ideas and methods of the so-called information-extreme intelligent technology (IEIT) of data analysis, which is based on maximizing the system's information capacity in the process of machine learning [14,15], should be considered as a perspective direction. The central paradigm of information-extreme machine learning, as well as in neuro-like structures, is adapting the system's input information description to the maximum reliability of pattern recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Information-Extreme Machine Learning of Wrist Prosthesis Control System Based on the Sparse Training Matrix

Suprunenko

Zborshchyk

Sokołov

2022

JES

View full text Add to dashboard Cite

The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. The task is solved within the framework of information-extreme intelligent data analysis technology, which is based on maximizing the system’s information productivity in machine learning. The idea of information-extreme machine learning of the control system for recognition of electromyographic biosignals, as in artificial neural networks, consists in adapting the input information description to the maximum total probability of making correct classification decisions. However, unlike neuro-like structures, the proposed method was developed within a functional approach to modeling the cognitive processes of the natural intelligence of forming and making classification decisions. As a result, the proposed method acquires the properties of adaptability to the intersection of classes in the space of recognition features and flexibility when retraining the system due to the recognition class alphabet expansion. In addition, the decision rules constructed within the framework of the geometric approach are practically invariant to the multidimensionality of the space of recognition features. The difference between the developed method and the well-known methods of information-extreme machine learning is the use of a sparse training matrix, which allows for reducing the degree of intersection of recognition classes significantly. The optimization parameter of the input information description, the training dataset, is the quantization level of electromyographic biosignals. As an optimization criterion is considered the modified Kullback information measure. The proposed machine learning algorithm results are shown in the example of recognition of six finger movements and wrist.

show abstract

“…One of the promising approaches to the information synthesis of diagnostic DSS is the use ideas and methods of so-called information-extreme intelligent technology (IEIT) data analysis, which is based on maximizing the information capacity of the system in machine learning. 10 , 11 , 12 The idea of IEIT methods, as in CNN, is to adapt the input mathematical description in the machine learning to the maximum possible probability of making the correct diagnostic decisions. But the main advantage of information-extreme machine learning methods is that, unlike neuro-like structures, they are developed as part of a functional approach to modeling cognitive processes inherent in man in the formation and adoption of classification decisions.…”

Section: Introductionmentioning

confidence: 99%