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Кулик, Сергей Дмитриевич

Организационные подразделения

Организационная единица

Институт интеллектуальных кибернетических систем

Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.

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Сергей Дмитриевич

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Теперь показываю 1 - 10 из 23

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Intelligent Information System for Telemedicine
(2020) Kondakov, A.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.The problem of telemedicine systems was very important and relevant in Russian Federation for many years. Communications and technologies in most regions allow to introduce such systems, but it became possible only in 2018 when federal law (No. 242-FZ On amendments to certain legislative acts of the Russian Federation to clarify the procedure of personal data processing in information and telecommunication networks [1]) about telemedicine came into effect. Since this moment many IT companies begin developing remote systems for healthcare. There are many different types of telemedicine systems and services, but all of them have the same concept. It is fast and easy way to consult patient, make diagnosis and prescribe treatment. It especially important for patients who live far enough from hospitals, people with disabilities or for people who live in regions where hospitals have no modern and quality equipment. Also some regions have problems with qualified personnel. This article presents a new intelligent information system for the treatment of psoriasis. This system was developed for Russian clinic which has a big experience in psoriasis treatment and now it became possible to make a big network of clinics that can located in different parts of the country (also in most remote) and still have all the benefits of central clinic.
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Investor Bot for Business Process
(2024) Kulik, S.; Sofronov, I.; Кулик, Сергей Дмитриевич; Софронов, Иван Евгеньевич
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Using Electronic Nose in Forensic Odor Analysis
(2024) Shtanko, A.; Kulik, S.; Кулик, Сергей Дмитриевич
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Topological Analysis of Protein Surfaces and Its Role in the Development of New Medicines
(2024) Bystrov, O. V.; Kulik, S. D.; Быстров, Олег Владимирович; Кулик, Сергей Дмитриевич
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Computational model for forensic biological hair analysis
(2020) Nikonets, D.; Suchkova, E.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.Human hair is one of the subjects, which is dealt with in researches of the forensic biological examination. To increase the efficiency of evaluating the results of a forensic microscopical analysis of hair from a person's head, in the course of this work, a mathematical model was developed for obtaining a probabilistic-statistical evaluation of the set of matching features, characterizing human hair (the random match probability). Such probability has been estimated for all hair samples from control dataset. As a result of the work, we can conclude that in most cases the estimation of the probability of appearance of the hair features set doesn't give us the precise results which allow to make an unequivocal positive conclusion in the identification of a person by hair. This made it possible to increase the efficiency of forensic biological experts.
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Educational Intelligent System Using Genetic Algorithm
(2020) Protopopova, J.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.This paper presents an Intelligent Information System for education. The system was created for teaching students to use genetic algorithm in application to optimization tasks. The system allows to quickly encode a solution of the problem and pick up most suitable configuration of genetic algorithm. The paper also demonstrates a specific example of usage of educational system to solve an optimization task. The paper contains a description of the educational system and its features, a description of its capabilities of working with genetic algorithm and its graphical interface.
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Effective scientific personnel training in the field of modern computer technologies for the implementation of advanced research projects of the Megascience class
(2020) Shtanko, A. N.; Kulik, S. D.; Kondakov, A. A.; Кулик, Сергей Дмитриевич
© Published under licence by IOP Publishing Ltd.Successful research projects of the Megascience class usually require a well-trained team of scientists from various fields of knowledge. These scientists must be high-skilled experts. Each member of a team like that must have the necessary, specialized cross-industry skills, for example, in such areas as artificial intelligence, convolutional neural networks, specialized intelligent search engines, and full-text analysis. One of the key aspects of effective personnel training for successful implementation of Megascience projects into reality is the acquisition by students professional skills, abilities, and knowledge to use tools of modern scientific technologies, containing, for example, libraries of programs (functions). In particular, convolutional neural networks and intelligent search systems can be applied in various research projects in the field of physics, chemistry, biology, and medicine, for example, in telemedicine, for effective decision-making in diagnosing a patient. Therefore, understanding the principles of neural networks and intelligent search systems is a necessary competence of researchers working in the framework of Megascience projects. Classic search engines are based on indexing the textual information of the database that is being searched. Intelligent search engines can improve the search experience through intelligent data processing, including using convolutional neural networks. This report examines practical examples and areas of the successful application of convolutional neural networks and information systems in practice.
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Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for Intelligent Robotics
(2020) Kulik, S. D.; Shtanko, A. N.; Кулик, Сергей Дмитриевич
© 2020, Springer Nature Switzerland AG.In this paper we’ve conducted multiple experiments with modern object detection system YOLO. Object detection systems are fundamental to many robotics tasks. Recognition algorithms involving object detection are often part of various intelligence systems for robots. Training object detection systems usually requires waste amounts of training data which can be expensive and time-consuming. In this paper we’ve conducted several experiments with YOLO on small training datasets investigating YOLO’s capacity to train on small number of examples. We measured accuracy metrics for object detector depending on the size of training dataset, compared training process of full and smaller versions of YOLO and their speed. Gathered information will be used for creating visual factographic intelligence system for robots. YOLO (You Only Look Once) is a special intelligent technology for computer vision techniques. Our results are useful for industry professionals and students from a broad range of disciplines related to robotics, intelligent technologies and other fields.
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The Problem of Efficiency of Microscopic Human Hair Analysis in the Forensic Biological Examination
(2020) Suchkova, E. V.; Nikonets, D. A.; Kulik, S. D.; Кулик, Сергей Дмитриевич
The main aim of this research work is to study new approaches to assessing the results of forensic microscopic analysis of the investigated person's hair, which will improve the effectiveness of forensic biological examination of human hair. The forensic examination of human hair includes an analysis of their morphological characteristics. If all individualising features coincide, a conclusion can be drawn that this very hair probably belongs to the investigated person. However, a "probabilistic" conclusion cannot be the basis for a court decision. A mathematical model for assessing the probabilistic-statistical estimate of the set of matching features that characterise human hair (random match probability) has been developed to improve the evaluation of the forensic microscopic hair analysis. The identification significance (rarity) has been estimated for all morphological features characterising human hair. The aggregated identification significance of the set of matching features (morphological hair profile) has been estimated, and the probability and the frequency of morphological hair profiles have been evaluated for a control dataset of hair samples. As a result of the work, it can be concluded that, in most cases, the probabilistic-statistical estimate of appearance of morphological profiles of human hair does not give us precise data to make an unequivocal positive conclusion in the identification of a person by hair. However, the microscopic hair analysis is scientifically justified, and its results have a good correlation with the results of the DNA analysis, especially if the analysis made an unequivocal negative conclusion. The most accurate method of forensic hair analysis is the DNA analysis. However, despite the fact that the nuclear DNA analysis is more suitable for an identification of a person by hair than the microscopic hair analysis, it should be noted that, in two-thirds of cases, the probability of appearance of a genetic profile is in the same range of values as the probability of appearance of a morphological profile of human hair. Only in one-third of cases, this probability has values that are difficult to achieve in the microscopic hair analysis. Moreover, there are situations when it is impossible to perform the DNA analysis, but the microscopic analysis of human hair can be done without any restriction. As a result, it is concluded that, to increase the expert biologist's efficiency, it makes sense to use an integrated approach to the forensic examination of human hair. The integrated approach combines a consistent application of the method of the microscopic analysis of human hair and the method of the DNA hair analysis, which allows obtaining the most compete forensic information on human hair.
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Intelligence Information System for Forensic Microscopical Hair Analysis
(2020) Suchkova, E. V.; Nikonets, D. A.; Kulik, S. D.; Кулик, Сергей Дмитриевич
© 2020, Springer Nature Switzerland AG.The problem of identification by human hair has been considered in the paper. The main aim of this paper is to present a new intelligence information system for forensic microscopical hair analysis. In our research we used micromorphological characteristics of the human hair: cuticle scale pattern, cortical layer background colour, pigment colour, pigment granule size, pigment aggregate size and pigment distribution. The micromorphological characteristics of the hair specimens have been investigated with the special microscope, such as Leica DM 1000 microscope. The result of the work is very important for the development of a mathematical model for the evaluating of the probability of a set of the matching features in the investigated hair object and comparative hair samples. Pattern recognition and decision making is special intelligent technology for forensic examination of human hair. Our results are useful for forensic experts and students from a broad range of disciplines related to intelligent technologies, for forensic microscopical hair analysis and other fields. Gathered information will be used for creating effective intelligence information system for forensic microscopical hair analysis.