| Topic title |
Possible scientific supervisors |
Source of funding |
| Image analysis using deep learning for seabed coverage evaluation |
prof. dr. Evaldas VAIČIUKYNAS |
state-funded |
| Data business risk analysis and clients segmentation based on big data mining and machine learning |
prof. dr. Robertas ALZBUTAS |
state-funded |
| Hierarchical heuristic algorithms for combinatorial optimization problems |
prof. dr. Alfonsas MISEVIČIUS |
state-funded |
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Authenticated processes data system creation and analysis
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prof. dr. Eligijus SAKALAUSKAS |
state-funded |
Research Topic Summary.
A lot of financial, industrial, social and etc. processes are facing with the challenge of generating, collecting and storing huge amount of real-time data in the cloud known as Big data. The great problem of these data is ensuring their availability and authenticity to wide audience. As an example may serve a data generated to the cloud by internet of things (IoT) systems. The other example is the process of cryptocurrency transactions authenticated by blockchain technology using mining process. But nevertheless, blockchain technology is not adequate to more complex process data for their representation, modelling and authentication when process is branching to many different directions. Research objective is to create a new suitable model for branching processes data representation, modelling and cryptographically secure authentication. The tasks are the following: 1.To create an adequate model for branching processes data representation in real-time. 2.To create a cryptographically secure authentication and validation system for branching processes data authentication. 3.To estimate model effectivity of realization and its cryptographic security against forgery attacks.
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Mathematical models of pension system
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doc. dr. Audrius KABAŠINSKAS |
state-funded |
Research Topic Summary.
The idea is to propose the methods/methodology that provides recommendations for the pension system participant (person) in choosing the efficient pension fund with respect to various criteria including stochastic dominance in time by increasing his possibilities to accumulate optimal amount of money in long term perspective. The main goal is to develop the dynamic risk assessment model based on stochastic dominance principles that serves as a tool for the performance analysis of pension funds. The tasks to be implemented include: 1. To identify a set of risk factors for quantifying the systemic and non-systemic risk that arises during the accumulation for the retirement in long term; 2. To analyse the performance results of 2nd and 3rd pillar pension funds in Lithuania over time using technical analysis; 3. To develop the dynamic multi-criteria pension fund risk and performance assessment model by integrating stochastic dominance principles, data envelopment analysis and other approaches allowing additional gains comparing to static risk assessment techniques; 4. To develop a Markov chain model based on phase type distributions for estimation of expected number of PHs participants depending on funds risk. 5. To propose the methodology for pension funds’ ranking based on dynamic risk and efficiency model developed. The achievement of results will be judged on validation period for various risk type pension funds (PF) working in Lithuania. It is expected that results could be applied in Lithuanian PF market. The ideas implemented in the model will be applicable for other pension systems with few pillars in other countries (e.g. Slovakia, Italy) as well. For more details please contact the supervisor.
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Modelling of Financial Network and Their Topology Using Graph Theory
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doc. dr. Kristina ŠUTIENĖ |
state-funded |
Research Topic Summary.
Recently, graph theory methods are gaining a high interest in finance and economics. The reason is that the financial entities and markets are strongly linked in a globalized world, and graph theory as the framework could be used for describing and modelling the structure of complex financial network. As such, the model to be developed might be used to investigate financial network characteristics such as market stability, network contagion, spillover, risk sharing among network entities, network shock propagation, etc. The solution to these individual questions depends on the topology of the network and its possible evolution over time. Therefore, the purpose of this work is to develop methods for modelling the financial network and its topology, focusing on financial risk assessment by introducing network characteristics and proposing stress-testing methods.
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Research and implementation of methods for brain-computer interface on the base of deep neural networks
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prof. dr. Vacius JUSAS |
state-funded |
Research Topic Summary.
Human-computer interface is the form of communication between a user and computer, when the user issues commands for computer in natural form. Such an interface can be implemented using various forms depending on the purpose and user requirements. Natural and user-friendly forms of the implementation shorten time of the user learning to use the implemented tool. Brain – Computer interface is a system that converts the brain activity into the control signals for a computer or some other electronic device. Such a system has large field of application ranging from the disabled persons to playing games. The clear relationship between the brain activity and the commands to muscles is not disclosed yet. Nevertheless, it is possible using mathematical methods to analyse the brain activity and to form the control signals for the external electronic devices. For this purpose, the method is needed to scan and to save the brain activity. The formation of electroencephalogram (EEG) is one of such methods. This is a non-invasive investigation of bioelectrical brain activity. The measured signals of brain activity are non-stationary and non-linear and they are directly dependent of the particular person. Consequently, it is difficult to distinguish the effective control signals. For this purpose, the feature extraction and classification methods are used. The methods of neural networks are dominant among classification methods. The deep neural networks present very good results for image recognition and classification. The signals of brain activity can be presented in the form of image. The purpose of the investigation is to apply the deep neural networks to recognize and classify the signals of brain-computer interface.
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Design and analysis of long series dependence estimators
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doc. dr. Tomas RUZGAS |
state-funded |
Research Topic Summary.
The research goal is to create knowledge about autocorrelation methods of long series that are effective in the case of non-stacionarity. In order to achieve this goal, it is necessary to study mathematical estimators based on the non-parametric analysis. The goal of this study will be achieved by four main tasks: A) to create a probabilistic model for the statistical relationship of series and to create model identification procedures; B) to design constructive autocorrelation methods based on this model and its its identification procedures; C) to offer mathematical estimators for taking account of additional information related to series context in autocorrelation methods; D) to study the proposed solutions, to perform comparative analyzes of alternative statistical relationship estimators on the basis of real data.
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| Machine learning and information integration research for disease prediction and health care |
prof. dr. Robertas ALZBUTAS |
state-funded |
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Research of methods for development of explainable artificial intelligence algorithms
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doc. dr. Agnė PAULAUSKAITĖ-TARASEVIČIENĖ |
state-funded |
Research Topic Summary.
Artificial Intelligence (AI) has achieved a wide range of capabilities from image recognition to language processing and data analysis. But, the automatic decision made by “black box”- based algorithm is hard to explain and even their designers cannot explain why the algorithm arrived at a specific decision. Quit often, this kind of algorithm for generalizing of training data creates rules that may not fit to another sample of data, i.e., it not generalizing properly or partly generalizing a given data. Therefore, the widespread application of artificial intelligence techniques is limited by the lack of trust of people in algorithmic decisions. Especially when there are no clear and objective criteria and rules for explaining one or the other decision made by the algorithm (e.g. Convolutional Neural Network – a deep learning algorithm). The absence of clear connection between accuracy and interpretability creates a barrier to a human consistently predict the model results.... For further information please contact the supervisor of the topic.
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Multicriteria decision making in finance using explainable artificial intelligence
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doc. dr. Audrius KABAŠINSKAS |
state-funded |
Research Topic Summary.
Artificial intelligence is one of the hot topics of the early 21st century in the physical and technological sciences. Large-scale investors such as pension funds, banks and investment companies have been using artificial intelligence principles for some time, however, most of techniques they use are limited to machine learning or artificial neural networks. The resulting solutions are difficult to explain and interpret. Moreover, the worst thing is that it is often it is nearly impossible to verify if the resulting solution is exactly what we aspired and whether it is optimal. Because investment decisions usually depend on many factors, such as expected return, risk, ethics, investor's risk profile and personal characteristics, all of these must be taken into account when developing an autonomous system or robo-advisor. Quite often such problems are quite easily solved by conventional methods, however, as the amount of information and number of criteria increases, the complexity of the task can become unbeatable even for supercomputers. It is therefore necessary to develop new methods based on artificial intelligence that are understandable and explainable and that their results can be validated. Therefore, the main task is: to create artificial intelligence based methods that are explainable and interpretable for most of the investment decision makers.
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| Deep learning based algorithm for generating musical melodies for more than one instrument songs |
prof. dr. Rytis MASKELIŪNAS |
state-funded |
| Artificial intelligence based model for predicting early symptoms of Alzheimer ‘s |
prof. dr. Rytis MASKELIŪNAS |
state-funded |
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Algebraic transformations for chaos and nonlinear dynamical systems
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prof. habil. dr. Minvydas Kazys RAGULSKIS |
state-funded |
Research Topic Summary.
Importance – dynamics of nonlinear systems and chaos is an important research in a variety of scientific disciplines. A typical example is a clinically validated model of cyclical chemotherapy where nonlinear processes play a pivotal role in the evolution of this complex competitive system. The ability to identify solitary solutions, the system of separatrices, to perform the analysis of bifurcations are very important objectives which can help to understand the mechanisms governing the evolution of the disease. The scientific problem – algebraic transformations for nonlinear systems will enable to develop a new approach for the understanding the dynamics of those systems. Algebraic multiplicative operators will be used to construct closed form solitary solutions to fractional order nonlinear differential equations. Algebraic Hankel-type transforms will be exploited for the investigation of the convergence of these solutions. Objectives and aims – to construct and to apply algebraic multiplicative operators and Hankel-type transforms for the investigation of nonlinear dynamical systems and chaotic processes. Expected results – at least 4 papers published at high level International journals. At least two presentations at International conferences. The doctoral student will be a member of the team at one research project at least.
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