| Topic title |
Possible scientific supervisors |
Source of funding |
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Design and analysis of adaptive classification algorithms
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doc. dr. Tomas RUZGAS |
state-funded |
Research Topic Summary.
The research goal is to create knowledge about classification algorithms that are effective in the case of heterogeneity. In order to achieve this goal, it is necessary to study mathematical methods based on the statistical analysis of the objects distribution. The goal of this study will be achieved by four main tasks: A) to create a probabilistic model for the distribution of objects and to create model identification procedures; B) to design constructive classification algorithms based on this model and its its identification procedures; C) to offer mathematical methods for taking account of additional information related to object positions and the context between them in classification algorithms; D) to study the proposed solutions, to perform comparative analyzes of alternative classification algorithms on the basis of real data.
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Adaptive geometric iterative methods for data analysis
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doc. dr. Svajūnas Sajavičius |
state-funded |
Research Topic Summary.
Many traditional methods for data interpolation and approximation rely on the solution of global linear systems. This feature severely limits the applicability of those methods since even local modifications require a repetitive solution of large-scale linear systems.
In recent years, geometric iterative methods have been used increasingly for data interpolation and approximation. These methods have an intuitive geometric meaning, are easily implemented, and allow easy fulfilment of various geometric requirements. Currently, such methods are used in computer-aided geometric design, image and surface reconstruction tasks, reverse engineering, etc.
To date, only few studies have been dedicated to investigating the possibilities of applying hierarchical spline technologies in geometric iterative methods. The uniqueness of hierarchical splines is the property of local adaptive refinement. In practical applications, local adaptivity allows reducing the amount of the required computational resources and opens up possibilities for solving much more complex tasks.
The main aim of the research will be the development and investigation of geometric iterative methods for data approximation, which will efficiently exploit hierarchical splines technologies. The tasks will include the construction and software implementation of adaptive geometric iterative methods, as well as their theoretical and experimental analysis.
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| Development of algorithms based on physics-informed deep neural network for simulation of dynamical systems |
doc. dr. Dalia Čalnerytė |
state-funded |
| Development of algorithms for the generation of three-dimensional objects based on annotations, using physical behavior models and artificial intelligence methods. |
doc. dr. Andrius KRIŠČIŪNAS |
state-funded |
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Developing data-driven models for investigating Lithuanian geothermal energy potential
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prof. dr. Mayur Pal |
state-funded |
Research Topic Summary.
Artificial intelligence and machine learning based methods have been used to solve a number of complex scientific & engineering problems like surface crack detection, solving partial differential equations, upscaling, & time series forecasting. In this research we aim to use machine learning and artificial intelligence to develop a Multiphysics model to describe geothermal reservoirs. Machine learning is an iterative process, which makes use of data to allow computer to find the underlying patterns. The process of machine learning is highly compatible to solve inverse problem related to geothermal reservoirs modelling. Pressure and temperature changes in geothermal reservoir have significant impact on permeability and porosity of the reservoir system. Understanding these changes is key to understanding the geological complexity of the geothermal reservoir. In this research we aim to use data driven approach to quantify changes induced in reservoir properties over time due to injection and production of hot fluids. To develop these AI and ML based models either measured data from published literature will be used or pseudo models using the numerical modelling approaches of the Multiphysics problem for a Lithuanian geothermal reservoirs will be used for training and testing of the AI and ML models. Additionally AI and ML models will also be used to generate time series forecast of energy production.
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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 ?nance and economics. The reason is that the ?nancial 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, spillovers, 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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Hierarchicity-based (self-similar) heuristic algorithms for combinatorial optimization problems
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prof. dr. Alfonsas MISEVIČIUS |
state-funded |
Research Topic Summary.
Optimization methods and algorithms are an important constitutive part of the computer science and also artificial intelligence. In particular, the heuristic algorithms play a very significant role, just to mention local search (LS), tabu search (TS), genetic algorithms (GA), (evolutionary) population-based algorithms and their numerous combinations (hybrids). The high efficiency of the combined heuristic algorithms by solving various optimization problems was empirically confirmed several decades ago. The further demand of such algorithms was stimulated by the growth of research and development along with the newly arising tasks. This has become even more actual in recent years.
The current development of heuristic algorithms is continued by not only combining different algorithms, but also by exploiting and expanding the inner structure (architecture) of the algorithms. One of the promising directions in this area is the use of so-called hierarchically-structured (hierarchicity-based) (or simply hierarchical) heuristic (HH) algorithms. The central idea behind the HH algorithms is the multiple adoption/utilization (reuse) of the well-known heuristics (like LS, TS, GA). This in connection with what is known as self-similarity - this means that an object (in our case, algorithm) is exactly or approximately similar to constituent parts of itself. The principle of self-similarity is the universal principle; so, we conjecture that for both the computational methods and algorithms, this principle may also be deeply inherent and important. This idea is not very new, and some examples of hierarchical-like algorithms have been already investigated (e.g., hierarchical local search, master-slave GAs). Still, this is an area of active and progressing research. Further computational studies are required for accelerating research and to reveal higher potential of the HH algorithms, as well as the new enhancements (hybrid HH algorithms, population-based HH algorithms, etc.).
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Computer vision algorithms for solving object shape recognition problems
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doc. dr. Armantas OSTREIKA |
state-funded |
Research Topic Summary.
Computer vision (CV) is an interdisciplinary field that deals with algorithms that enable decision-making by analyzing data from digital images. The goal here is to automate tasks that under normal conditions could only be performed by a human. CV systems are used in many fields, including manufacturing, medical, traffic monitoring, security systems, and more. CV methods allow for automatic analysis of important information when processing large amounts of data. Artificial intelligence algorithms have recently become an integral part of solving computer vision problems.
The proposed topic will aim to improve existing general methodologies focusing on specific production and real-world processes, in order to obtain greater efficiency and reliability, by adapting and improving machine learning methods including Decision Trees, K-Nearest Neighbors, Na?ve Bayes, Support Vector Classifiers, Convolutional Neural Networks. and YOLO algorithm modifications.
Here, CV tasks will include methods of acquisition, processing, analysis and interpretation of digital images; real-world data mining to obtain numerical or symbolic information in the form of decisions. Data interpretation in this context refers to the transformation of digital images into descriptors that can be linked to other action processes and suggest further appropriate solutions.
Focusing on real examples of provided services or production processes, the aim will be to apply and test the obtained algorithms in practice. Examples of applications include CV systems measuring and counting complex shaped products or workpieces, estimating their weight, shape or volume, and inspecting objects at high speed against their predetermined characteristics.
This research is expected to improve existing methodologies and introduce new research ideas based on artificial intelligence and new heuristics methodologies.
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Confidential and verifiable transactions system in blockchain.
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prof. dr. Eligijus SAKALAUSKAS |
state-funded |
Research Topic Summary.
Confidential and verifiable transactions system in blockchain. Confidentiality allows to realize confidential amount of money transfer in transaction in order to hide this information from competitors and other business subjects. But at the same time it is necessary to ensure the honesty of transactions by specially created verification methods.
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Machine learning and information integration research for disease prediction and health care
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prof. dr. Robertas ALZBUTAS |
state-funded |
Research Topic Summary.
The objective of the research is the creation of methodology and test calculations based on machine learning and information integration for the most accurate disease prediction and health care.
Tasks:
1. Review and compare possibilities of information integration methods and algorithms as well as the application of related software and machine learning methods intended for health care.
2. Define information integration and smart systems accuracy criteria and their evaluation procedures in the context of data relevant to health care.
3. Develop and demonstrate the information integration tools whose usage increases the accuracy and/or reduces the risk of incorrect decision-making.
4. Perform the pilot studies of smart systems intended for health care and create a methodology for the effective application of these systems.
For further information please contact the supervisor of the topic.
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Metaheuristic approaches for maximally diverse grouping problems.
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prof. dr. Gintaras PALUBECKIS |
state-funded |
Research Topic Summary.
Maximally diverse grouping problems (MDGPs) consist of finding a partition of a set of elements into a given number of mutually disjoint groups, while respecting the requirements of group size constraints and diversity. The MDGP models a variety of real-world situations. It is possible to formulate MDGPs with different objective functions and constraints. The purpose is to introduce MDGPs with the Max-Min objective function. Such a variation of the MDGP has several important applications. There is a clear need to develop metaheuristic-based algorithms for solving the Max-Min MDGPs.
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| Explainable artificial intelligence based on segmentation methods and similarity metrics for cardiac computed tomography images |
prof. dr. Agnė PAULAUSKAITĖ-TARASEVIČIENĖ |
state-funded |
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Research and implementation blockchain-based reputation model of student
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prof. dr. Vacius JUSAS |
state-funded |
Research Topic Summary.
Blockchain technology enables recording of business transactions online in a secure, transparent, efficient and auditable way. This technology makes it possible to create truly autonomous smart devices that can exchange data without the help of a centralized intermediary.
Blockchain technology allows the creation of chains of value, i.e. a complete sequence of business processes that enable an enterprise to bring a particular service or product to market and receive remuneration in a safe, efficient, and undeniable manner. An essential element of such chains of value is Smart Contracts, i.e. autonomous agents (programs) operating in blockchains and ensuring that business rules are followed in business transactions. Smart contracts can scan data, perform the necessary calculations according to a specified algorithm and protocol, and pass the results further down to smart contracts in blockchains without human intervention, only in accordance with the specified business rules. The practical implementation of blockchain technologies would allow for an innovative transformation of the public services and management sector. One of the small particles of the public sector is university students. After graduation, students obtain a diploma. However, the diploma does not reflect the course of studies and does not provide the employer with enough information about the prospective employee. Therefore, the employer cannot properly evaluate the prospective employee. In the course of the study process, it would be possible to gather information about students' behavior and achievements. Students could supervise the students. The blockchain technology is perfect one for this purpose.
The aim of the research is to apply the blockchain technology to the development of a student’s reputation model.
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Complex synchronisation effects and their identification in chaotic systems.
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prof. habil. dr. Minvydas Kazys RAGULSKIS |
state-funded |
Research Topic Summary.
Synchronisation of chaotic systems plays an important role in the dynamics of complex chaotic systems. New synchronisation detection algorithms will be developed and applied for the detection of long-term relationships between parameters of human electrocardiograms and earth magnetic field data.
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