Viktor Medvedev

• PhD
• Senior Researcher at Vilnius University

In today’s cyber environment, threats such as data breaches, cyberattacks, and unauthorized access threaten national security, critical infrastructure, and financial stability. This research addresses the challenging task of protecting critical infrastructure from insider threats because of the high level of trust and access these individuals typic...

This study addresses the problem of detecting pancreatic cancer by classifying computed tomography (CT) images into cancerous and non‐cancerous classes using the proposed deep learning‐based aggregate analysis framework. The application of deep learning, as a branch of machine learning and artificial intelligence, to specific medical challenges can...

As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important...

Deep learning-based approaches are attracting increasing attention in medicine. Applying deep learning models to specific tasks in the medical field is very useful for early disease detection. In this study, the problem of detecting pancreatic cancer by classifying CT images was solved using the provided deep learning-based framework. The choice of...

Cybersecurity is a crucial issue in today’s critical infrastructure to ensure a secure connection between the administrator and the session. Detecting insiders is a difficult task for cybersecurity professionals, as insiders are hard to detect and identify and thus require advanced techniques to prevent their activities. These users may be current...

The current world crisis caused by the COVID-19 pandemic has transformed into an economic crisis, becoming a problem and a challenge not only for individual national economies but also for the world economy as a whole. The first global lockdown, which started in mid-March of 2020 and lasted for three months in Lithuania, affected the movement and b...

Multidimensional scaling (MDS) is a widely used technique for mapping data from a high-dimensional to a lower-dimensional space and for visualizing data. Recently, a new method, known as Geometric MDS, has been developed to minimize the MDS stress function by an iterative procedure, where coordinates of a particular point of the projected space are...

A well-known and widely used technique for mapping data from high-dimensional space to lower-dimensional space is multidimensional scaling (MDS). Although MDS, as a dimensionality reduction method used for data visualization, demonstrates great versatility, it is computationally demanding, especially when the data set is not fixed and its size is c...

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negat...

Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related t...

The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed fo...

The furniture manufacturing sector of the Baltics is facing serious challenges common in all European countries, namely, the growing global competition for customized solutions. New standards to be followed in the industry tend to increase production costs, extend manufacturing time and cause frequent errors in the product quality. To maintain sust...

Visualization is a part of data science, and essential to enable sophisticated analysis of data. The visualization ensures the human participation in most decisions when analyzing data. In this paper, we review methods and software for visualization of multidimensional data. The emphasis is put on the web-based DAMIS solution for data analysis, all...

The growth of marine traffic around the seaports raise the traffic control problems. They increase the workload for traffic service operators. The automated identification system (AIS) of vessel movement generates significant amounts of data that needs to be analysed incrementally to train model as data become available gradually over time. A fast...

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning a...

The conventional technologies and methods are not able to store and analyse recent data that come from different sources: various devices, sensors, networks, transactional applications, the web, and social media. Due to a complexity of data, data mining methods should be implemented using the capabilities of the Cloud technologies. In this paper, a...

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentati...

In this paper, a Cloud computing approach for intelligent visualization of multidimensional data is proposed. Intelligent visualization enables to create visualization models based on the best practices and experience. A new Cloud computing-based data mining system DAMIS is introduced for the intelligent data analysis including data visualization m...

Nowadays, the amount of data being collected and stored has been constantly increasing. Data come from different sources such as various devices, sensors, networks, transactional applications, web and social media. Conventional technologies and methods are not able to store and analyze such amount of data. In this paper, a comparative analysis of t...

Nowadays business information systems are thought of as decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential part of such systems. As datasets tend to be increasingly large, more effective ways are required to display, analyze and interpret information they contain. Most of the cl...

The analysis of medical streaming data is quite difficult when the problem is to estimate health-state situations in real time streaming data in accordance with the previously detected and estimated streaming data of various patients. This paper deals with the multivariate time series analysis seeking to compare the current situation (sample) with...

Business information systems nowadays should be thought of first of all as the decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential constituent of such systems, especially in the age of growing amounts of data to be interpreted and analyzed. As managers are faced with a federated...

In the paper, an overview of methods and technologies used for big data clustering is presented. The clustering is one of the important data mining issue especially for big data analysis, where large volume data should be grouped. Here some clustering methods are described, great attention is paid to the k-means method and its modifications, becaus...

In the paper, we investigate deterministic approaches for solving scheduling and rescheduling problems. Most of them are based on mixed-integer programming. Descriptions of solvers are also presented here. They allow solving complicated optimization problems with many variables and constraints in acceptable time. The deterministic approaches could...

Straipsnis skirtas duomenų tyrybos, pagrįstos saityno paslaugomis, analizei. Apibrėžiamos pagrindinės su saityno paslaugomis susijusios sąvokos. Pristatomos paskirstytosios duomenų tyrybos galimybės bei jų įgyvendinimo priemonės – Grid, Hadoop. Atliekama duomenų tyrybos sistemų, pagrįstų saityno paslaugomis, analitinė apžvalga. Parenkami sistemų pa...

Regarding the complexity of actual software systems, including web portals, it is becoming more and more difficult to develop software systems such that their real usage will satisfy their intended usage. To tackle this problem, we can compare the a priori assumptions about how the system should be used with the actual user behavior in order to dec...

In this paper, we present an approach of the Web application (as a service) for data mining oriented to the multidimensional data visualization. The stress is put on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation includes five visualization methods: MDS SMACOF algor...

In this paper, we present an approach of the web application (as a service) for data mining oriented to the multidimensional data visualization. This paper focuses on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation of such a web application obtains a multidimensional...

The most classical visualization methods, including multidimensional scaling and its particular case – Sammon’s mapping, encounter difficulties when analyzing large data sets. One of possible ways to solve the problem is the application of artificial neural networks. This paper presents the visualization of large data sets using the feed-forward ne...

This paper presents the visualization of large datasets with SAMANN algorithm using clustering methods for initial dataset reduction for the network training. The visualization of multidimensional data is highly important in data mining because recent applications produce large amount of data that need specific means for the knowledge discovery. On...

This paper analyzes the visualization of multidimensional data using feed-forward neural network. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon's nonlinear projection. The SAMANN network offers the generalization ability of projecting new data, which is not...

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the ne...

Sammon’s mapping is a well-known procedure for mapping data from a higher-dimensional space onto a lower-dimensional one. But the original algorithm has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN neural network, that realizes Sammon’s algorithm, provi...

We consider the problem of visual analysis of the multidimensional medical data. A frequent problem in medicine is an assignment of a health state to one of the known classes (for example, healthy or sick persons). A particularity of medical data classification is the fact that the transit from the normal state to diseased one is often not so consp...

In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon’s mapping. This algorithm preserves as well as possible all interpattern distances. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-for...

Sammon’s mapping is a well-known procedure for mapping data from a higher-dimensional space onto a lower-dimensional one. The original algorithm has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. SAMANN neural network, that realizes Sammon’s algorithm, provides a ge...

In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon’s mapping. The algorithm is oriented to minimize the projection error. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural n...

In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon‘s mapping. The paper describes an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon‘s nonlinear proje...

In this paper we discuss the visualization of multidimensional vectors. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon's mapping. This algorithm preserves as well as possible all inter-pattern distances. We investigate an unsupervised back-propagation algorithm to train a multilayer feed...

This paper analyzes the visualization of multidimensional data using feed-forward neural net-work. The dependence of the visualization quality on the configurations of the neuron activation function is presented. We investigate the unsupervised backpropagation algorithm to train a multilayer feed-forward neural network to perform the Sammon's nonli...