Procedia Computer Science

This volume presents selected papers of young computational scientists – participants of YSC’2019. Annual Young Scientists Conferences in computational science are traditionally held since 2012 by the University of Amsterdam (the Netherlands) and ...

Trending applications such as AI and data analytics have mandated the use of GPUs in modern datacenters for performance reasons. Current practice dictates to dedicate GPUs to applications, which limits the amount of concurrent users to the ...

Software integration of optimization problems’ solvers leveraging power of heterogeneous computing environments is a great challenge of last decades. Last several years we have been developing coarse-grained parallelization approaches to speed up ...

Time series data as its analysis and applications recently have become increasingly important in different areas and domains. Many fields of science and industry rely on storing and processing large amounts of time series - economics and finance, ...

The development of information technologies entails a nonlinear growth of both volumes of data and the complexity of data processing itself. Scheduling is one of the main components for optimizing the operation of the computing system. Currently, ...

In this contribution we consider modeling of an optical element with freeform surface. As an example, we synthesize a freeform component, which allows to generate a required image for any object on the basis of Snell’s law and the law of energy ...

In this paper, we present results of in silico simulation of dendritic electroless silver colony growth model on a hexagonal cellular automata lattice. A cellular automaton, based on a NetLogo framework, was used to replicate the behavior of a ...

The ability to investigate 3D structure of biomolecules, such as proteins and viruses, is essential in biology and medicine. With the invention of super-bright X-ray free electron lasers (e.g. European XFEL and Linac Coherent Light Source (LCLS)) ...

Molecular dynamics simulations is a field of science that studies interactions of atoms at a very small order of magnitude. The difficulties due to the enormous range of length and time scales, lead us to average out the details of the atomistic ...

Proteins are complex biological macromolecules performing a great variety of functions in the living systems. In order to get insight into the atomic structures and the time evolution of proteins, besides experimental techniques, mathematical and ...

The focused ultrasound (FUS) based on the cavitation effect is a promising technology that has a number of drawbacks, and the main one is the unpredictability of side effects. Numerical simulation of the influence of FUS on a human body can help ...

Chronic diseases are complex systems that can be described by various heteroscedastic data that varies in time. The goal of this work is to determine whether historical data helps to improve machine learning predictive models or is it more ...

This paper represents the research results of echocardiographic study for early prediction of mortality. The classification task is solved by analyzing the echocardiographic data from medical information system. Echocardiographic data of 90000 ...

With the development of remote medicine, more and more programs are being opened for the remote monitoring of chronic patients. The purpose of this research is to provide maximum understanding of the patient’s condition using their vital signs ...

The main idea of this research is the creation of a new approach to the prediction of chronic diabetes course. This approach is based on dividing patients into several clusters. We used Machine Learning methods for it. Next, we created a diagram ...

Healthcare systems should provide technology to store data in different forms: numbers, text, chart or images. These data are growing constantly. As a result, medical databases and environments are increasing year by year. Using information ...

The paper presents a research project which aimed to design and develop an intelligent approach for the integration of heterogeneous data and knowledge sources in personalized healthcare. The integration profile we propose is mainly focused on ...

This article describes the study results in the development of the method of analysis of semi-structured data from electronic health records to improve the quality of data describing patients’ treatment processes. Improving the accuracy of ...

Data extraction from narrative medical texts is a significant task to enable secondary use of medical data. Supervised learning algorithms show good results in natural language processing (NLP) tasks. We have developed a NLP framework based on ...

Many companies want or prefer to use chatbot systems to provide smart assistants for accompanying human specialists especially newbies with automatic consulting. Implementation of a really useful smart assistant for a specific domain requires a ...

Social networks are known to reflect users preferences and traits, for instance, in the form of posts and comments. This data can be used to solve various user profiling tasks that involve classifiers construction. State-of-the-art NLP models, ...

In today’s world, it is crucial to be proactive and be prepared for events which are not happening yet. Thus, there is no surprise that in the field of social media analysis the research agenda has moved from the development of event detection ...

The digitisation process of objects and operations of the real world is quite active, i.e. creating their digital entities or models. People regularly leave enough of their data in social networks services and on various sites, thus forming their ...

The general concept and implementation of a practice-oriented social network data storage and analysis system are discussed in this paper. The need for such a concept comes from the fact that at the moment little attention is paid to the internal ...

This paper introduces the investigation of users interests by topic modeling. This work proposes a methodology for end-to-end unsupervised users topics investigation by textual data. Using additive regularization of topic models with large topic ...

The paper presents the study on identification and systematic description of typical behavioral patterns of drug usage. The dataset is based on the qualitative interviews with 70 drug users and the survey within 80 experts of federal drug control ...

Reliable forecasts of foreign trips are of great interest to banks, tour operators and governments in origin and destination countries. In this study the following goal was set: to develop models that allow predicting the location of the user in ...

Social media data is useful for personalized search engines, recommender systems, and targeted online marketing. Sometimes values of attributes are missing due to security reasons or problematic data collection process. In this case, the ...

It this study, transactions of bank customers are used to analyze their activity profiles. In particular, Merchant Category Codes of transactions are employed to divide customers into three classes: (1) car drivers; (2) public transport users; (3) ...