Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education

The main goal of this research is to improve the personification of learning in higher education. The proposed flexible model for organizing blended and distance learning in higher education involves the creation of an individual learning path through testing students before the start of training. Based on the learning outcomes, the student is credited to the learning path. The training path consists of mandatory and additional modules for training; additional modules can be skipped after successfully passing the test, without studying these modules. The paper examines the composition of intelligent learning systems: student model, learning model and interface model. A student model is described, which contains the level of their knowledge, skills and abilities, the ability to learn, the ability to complete tasks (whether they know how to use the information received or not), personal characteristics (type, orientation) and other factors. The student's model is described by a mathematical formula. Thus, being described using logical rules, which have formed the basis for the software implementation of mixed and distance learning rules for lifelong learning courses. There is an interface model presented in the paper, and the results of the course of the proposed flexible model for the organization of mixed and distance learning "Digital Skills of a Modern Teacher in the Context of Distance Learning", as well as the face-to-face course "Digital Learning for Everyone" before the start of the pandemic which is close in its content to the course under study. Based on the results of the analysis, we introduced criteria for the effectiveness of the training course, proposed the weighting coefficients for evaluating the training course, carried out the assessment and drew conclusions.

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

BACKGROUND

Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new drugs with desired properties. The interdisciplinary of research creates additional difficulties in creating new drugs. Currently, there are no universal algorithms and software for predicting the "structure-property" dependence of drug compounds that can take into account the needs of specialists in this field. In this regard, the development of a modern Smart-system based on the promising bio-inspired approach of artificial immune systems for predicting the structure-property dependence of drug compounds is relevant. The aim of this work is to develop a multi-agent Smart-system for predicting the "structure-property" dependence of drug compounds using the ontological approach and modified algorithms of artificial immune systems using the example of drug compounds of the sulfonamide group. The proposed system makes it possible to increase the accuracy of prediction models of the "structure-property" dependence, to reduce the time and financial costs for obtaining candidate drug compounds.

METHODS

During the creation of a Smart-system, there are used multi-agent and ontological approaches, which allow to structure input and output data, optimally to distribute computing resources and to coordinate the work of the system. As a promising approach for processing a large amount of chemical information, extracting informative descriptors and for the creation of an optimal data set, as well as further predicting the properties of medicinal compounds, there are considered modified algorithms of artificial immune systems and various algorithms of artificial intelligence.

RESULTS

There was developed an ontological model of a multi-agent Smart-system. There are presented the results of the «structure-property» dependence simulation based on a modified grey wolf optimization algorithm and artificial immune systems. During the simulation, there was used information from the Mol-Instincts sulfonamide descriptor database.

CONCLUSION

The developed multi-agent Smart-system using ontological models allows visually to present the structure and interrelationships of agents functioning, which greatly facilitates the development of software and reduces time and financial costs during the development of new drugs.

Ontological modeling of electronic health information exchange

INTRODUCTION

Investments of resources to purposively improve the movement of information between health system providers are currently made with imperfect information. No inventories of system-level electronic health information flows currently exist, nor do measures of inter-organizational electronic information exchange.

METHODS

Using Protégé 4, an open-source OWL Web ontology language editor and knowledge-based framework, we formalized a model that decomposes inter-organizational electronic health information flow into derivative concepts such as diversity, breadth, volume, structure, standardization and connectivity.

RESULTS

The ontology was populated with data from a regional health system and the flows were measured. Individual instance's properties were inferred from their class associations as determined by their data and object property rules. It was also possible to visualize interoperability activity for regional analysis and planning purposes. A property called Impact was created from the total number of patients or clients that a health entity in the region served in a year, and the total number of health service providers or organizations with whom it exchanged information in support of clinical decision-making, diagnosis or treatment. Identifying providers with a high Impact but low Interoperability score could assist planners and policy-makers to optimize technology investments intended to electronically share patient information across the continuum of care. Finally, we demonstrated how linked ontologies were used to identify logical inconsistencies in self-reported data for the study.