| Topic title |
Possible scientific supervisors |
Source of funding |
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Design and analysis of adaptive classification algorithms
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prof. dr. Tomas Ruzgas |
state-funded |
Research Topic Summary.
The goal of this research is to develop and investigate adaptive classification algorithms that are effective under heteroscedastic conditions. Heteroscedasticity, where the dispersion of data varies depending on the characteristics of the objects, is a common challenge in fields such as economics, medicine, and social sciences. Traditional classification methods may be inefficient when data exhibit heterogeneous structures, so it is necessary to create new algorithms capable of accurately classifying such data.
The research will aim to develop a mathematical model describing the distribution of objects and create algorithms based on this model to perform precise data classification. Additionally, methods will be proposed to incorporate supplementary information about the positions and context of objects within the classification algorithms, further improving their accuracy. At the end of the study, we will compare the performance of the developed algorithms with alternative methods using real-world data to evaluate their practical application.
The expected outcomes include the creation of more accurate and efficient classification algorithms tailored to heteroscedastic data. These results will contribute to improving decision-making in various fields, from finance to biomedicine, by ensuring more precise data analysis and prediction.
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Adaptive spline-based data fitting for geometric modeling of cultural heritage objects
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doc. dr. Svajūnas Sajavičius |
state-funded |
Research Topic Summary.
This PhD project is focused on creating accurate geometric models of cultural heritage objects from point cloud data. Using hierarchical splines, particularly truncated hierarchical B-splines (THB-splines), this research will develop advanced data fitting methods that combine local adaptivity with computational efficiency. The project aims to address challenges in modeling complex geometries and high-resolution data, contributing to the digital preservation and reconstruction of cultural heritage. Applications include modeling historical sculptures, architectural details, and archaeological artifacts, blending innovative digital technologies with real-world impact.
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Development of Algorithms for Creating and Real-Time Adjustment of Unmanned Aerial Vehicle Flight Trajectories
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doc. dr. Andrius Kriščiūnas |
state-funded |
Research Topic Summary.
In recent times, the use of unmanned aerial vehicles (UAVs) is becoming increasingly widespread in both civilian activities, such as field games, transportation, mapping, and military applications, including surveillance and tactical field and internal operations. Due to the extensive use of UAVs, the topic of real-time trajectory adjustments, considering both stationary and dynamic obstacles such as birds or other UAVs, becomes more crucial. Autonomous control of UAV trajectories can be applied to various problem-solving scenarios, such as countering the unauthorized use of UAVs in restricted areas like airports or military facilities. Similarly, automatic trajectory adjustments can be applied in entirely different situations, such as avoiding collisions with other UAVs during cargo transport or remote monitoring.
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AI-Powered Differentiation Algorithm for Inclusive Education
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doc. dr. Daina Gudonienė |
state-funded |
Research Topic Summary.
This thesis would involve creating and testing a specific algorithm that help automatically differentiate learning materials and assessments for students with diverse educational needs. AI will be used to:
1. Generate personalized learning pathways for students, including those with disabilities or learning difficulties.
2. Adapt assessments in real-time to ensure they are accessible and fair to students with varying abilities.
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| An artificial intelligence approach for detecting malicious software |
prof. dr. Jevgenijus Toldinas |
state-funded |
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Development of algorithms based on deep neural networks and signal processing technologies for image reconstruction
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prof. dr. Dalia Čalnerytė |
state-funded |
Research Topic Summary.
Image reconstruction (inpainting) covers cases where it is necessary to restore missing parts of an image, anonymize a part of an image, restore damaged parts of an image or similar tasks. Such problems are solved in satellite image analysis, medical image analysis, virtual reality scene creation and other application areas. The object of research is the integration of signal processing methods with deep neural networks for image reconstruction.
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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) algorithms and their 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.
The current development of heuristic algorithms is continued by not only combining different algorithms, but also by exploiting the inner structure of the algorithms. One of the promising directions is the use of so-called hierarchically-structured (hierarchicity-based) (or simply hierarchical) heuristic (HH) algorithms. The key idea behind the HH algorithms is the multiple reuse of the well-known heuristics. 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 the computational methods, 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., iterated/hierarchical local search, master-slave genetic algorithms). 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. The following are the particular stages of the research process:
- analysis of design of the HH algorithms;
- empirical testing of different variants of the HH algorithms;
- applications to real-world problems.
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Metaheuristic approaches for maximally diverse grouping problems.
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doc. 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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| Development of Object Re-Identification Algorithms Integrating Dynamic and Semantic Video Stream Information |
doc. dr. Andrius Kriščiūnas |
state-funded |
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Detection and parameterisation of ophthalmological structures in eye fundus images
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doc. dr. Martynas Patašius |
state-funded |
Research Topic Summary.
The eye fundus is one of the few places on the human body where small blood vessels can be observed non-invasively. They are not only evaluated in the diagnosis of ocular diseases, but also in some systemic diseases (hypertension, diabetes). The fundus images also show other structures (e.g. optic disc, drusen, etc.) that are useful for diagnosis. However, even in this field there are still scientific problems that have not been solved. For example, there is no reliable and fast method for tracing the vascular network, which, in turn, would facilitate the determination of various parameters (tortuosity, angles of branching, etc.). This study would seek to address some problems of such type.
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Simulation and automated design of optimal and reliable systems integrating artificial and ambient intelligence
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prof. dr. Robertas Alzbutas |
state-funded |
Research Topic Summary.
Aim of research - to create the methodology for the smart design of optimal and reliable systems based on ambient and artificial intelligence and demonstrate the application of developed simulation means.
Research tasks:
1. Develop the concepts and techniques for the simulation and automated design of systems;
2. Prepare and update the simulation means and models suitable for automated smart design;
3. Create a concept for system optimization in terms of resource consumption and reliability;
4. Connect the concepts of automated design and optimization and demonstrate this as a unified methodology that enables the automated design of reliable systems.
Activities planned in this research will solve the relevant issues and tasks mentioned above, i.e., ensuring reliable operation of the various systems and optimizing these systems' resource consumption. The developed methodology will be demonstrated based on simulation means (methods and ICT) and various applications and it enable their further development in creating reliable systems. This will also be related to the smart environments and automated design technology to optimize resources used for this technology.
For further information please contact the supervisor of the topic.
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Research and development of brain-computer interface motor imagery methods based on deep neural networks
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prof. dr. Vacius Jusas |
state-funded |
Research Topic Summary.
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 motor imagery signals of brain-computer interface.
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Evaluation of Stress and Fatigue Based on Bio-signals and Movement Tracking Data
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doc. dr. Eglė Butkevičiūtė |
state-funded |
Research Topic Summary.
The aim of this study is to develop an artificial intelligence-based methodology for detecting stress and fatigue states, based on the analysis of bio-signals (electrocardiograms and photoplethysmograms) or motion tracking data. This is relevant in modern society, especially among drivers and other professionals with significant responsibilities, as timely identification of stress or fatigue can help prevent accidents and improve work efficiency and health. The study will involve data collection and analysis, the development and application of AI-based algorithms and models, aiming to create a reliable methodology for practical application and to provide recommendations for further research.
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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 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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