Wykaz obszarów badawczych związanych z tagiem Artificial-intelligence:
| # | Obszar badawczy | Dziedzina naukowa |
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| 1 |
Use of information technology for proactive detection (identification and exposure) of disinformation and misinformation content - automated methods for flagging disinformation content, tracking propagation channels, detecting original sources of spreading false information. I am primarily interested in projects enabling the combination of engineering sciences with social sciences in preventing and combating disinformation, e.g. by applying methods of Generative Adversarial Networks and Large Language Models, in conjunction with psychosocial analysis of the problem. It is important for me to focus on issues related to the impact of disinformation on crisis situations, amplification of public sentiment, manipulation of public opinion (public health crises, natural disasters, warfare using asymmetric and hybrid methods).
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1)Methods of detection and classification of heart abnormalities using deep learning techniques - concerns techniques and algorithms for processing ECG signals for automatic detection and classification of heart abnormalities using deep machine learning methods. It is planned to use a variety of deep convolutional neural networks for automatic analysis of the structure (waveform) of ECG signals, as well as mechanisms for detecting anomalies (deviations) and searching for information based on autoencoder neural networks. The work will be carried out in cooperation with the Department of Cardiology-Intensive Therapy and Internal Diseases of the Medical University of Karol Marcinkowski in Poznań. 2)Methods of detection and classification of heart abnormalities using heart rate variability (HRV) parameters and machine learning techniques - concerns techniques and algorithms for processing ECG signals for automatic detection and classification of heart abnormalities using machine learning methods. It is planned to use a wide variety of heart rate variability (HRV) parameters as well as new original heart rate asymmetry (HRA) parameters. The work will be carried out in cooperation with the Department of Cardiology-Intensive Therapy and Internal Diseases of the Medical University of Karol Marcinkowski in Poznań. 3)Passive radar for space object detection using signals recorded by antennas of the international network of radio telescopes LOFAR - The research area concerns the techniques and algorithms for processing signals recorded by the antennas of the network of radio telescopes LOFAR (Low-Frequency Array for radio astronomy) in order to use them for passive radiolocation of space objects: satellites in low orbits and so-called space debris. The considered system of passive radiolocation does not require the construction of dedicated transmitters, but uses the existing so-called illuminators of opportunity, e.g. FM, DAB + or TV DVB-T transmitters. After reflecting the signals from the objects, they are received by the antennas of the LOFAR system. The research in this area, carried out in cooperation with the Space Research Center of the Polish Academy of Sciences, is pioneering on a global scale. Three LOFAR stations are located in Poland. A single LOFAR station consists of many antennas creating a large radio telescope that can receive relatively weak signals. 4) Methods and algorithms for signal processing in passive radar for small unmanned aerial vehicles (drones) - concerns techniques and algorithms for signal processing dedicated to the passive radiolocation of small unmanned aerial vehicles (drones). The work will be carried out in cooperation with the Faculty of Power and Aeronautical Engineering of the Warsaw University of Technology at the airport in Sieraków near Przasnysz, recently purchased by the Warsaw University of Technology, where the Area Monitoring Laboratory with four antenna stations was built. Problems to be solved within the research area are related to detection of small flying objects with the use of specific features of signals reflected from the considered objects, estimation of their parameters, tracking, as well as classification of detected objects, in particular, the research is to focus on the possibility of distinguishing small drones from birds. 5) Optimization of methods and algorithms of people identification based on the EEG signal with the use of machine learning techniques - concerns the methods and algorithms of people identification based on the EEG signal with the use of machine learning techniques. It is planned to use the approach based both on the spectral features of the EEG signal in its individual bands, as well as the analysis of the EEG signal itself using the so-called deep learning techniques with convolutional neural networks. The work will include the selection and optimization of EEG signal parameters and classifiers used to identify people, the number of sessions necessary to train classifiers, the minimum number of electrodes used for identification, as well as the development of headbands/caps dedicated to collecting the EEG signal for the application under consideration, and testing the developed solutions under the conditions similar to their practical implementation. The research will be carried out in cooperation with the Nencki Institute of Experimental Biology of the Polish Academy of Sciences.
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The use of artificial intelligence methods for detection, tracking and target recognition in radar. Nowadays, artificial intelligence methods are used in more and more different fields. One of them is radar. The classic approach to radar uses methods and algorithms for the detection, tracking and recognition of targets that have been known for decades. Usually, under certain conditions, these solutions provide optimality in a certain sense. In reality, these conditions are often not met, leading to undesired results. The use of artificial intelligence methods may, in certain situations, lead to results that are difficult to obtain with classical methods. As part of this research area, the PhD student will deal with the application of artificial intelligence methods where up to date classic approaches used in radar have been used.
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software engineering, bioinformtics, artificial intelligence, data fusion
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Research in the area of supply chain management (SC): flexibility and resilience of SC to internal phenomena and global disruption (natural disasters, wars, diseases, sanctions), management and shaping of SC, supply, distribution, risk management, development and/or the use of decision support tools in SC based on the machine learning, artificial intelligence, simulation.
Research in the field of city logistics: planning a cargo distribution system, developing innovative solutions for the last mile logistics, infrastructure designing for environmentally-friendly vehicles, including methods of locating charging stations for electric vehicles, developing and/or using decision support tools in urban logistics based on the machine learning, artificial intelligence, simulation.
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Globalization of threats to homeland security, such as international terrorism, smuggling of weapons or drugs became one of the main challenges for security forces in the 21st century. In effect, new, scientifically grounded methods for fighting organized crime have been proposed in recent years. One of rapidly developing approaches are Security Games (SGs), which consist in modeling tactical security issues as games between security forces (secret service, police, etc.) and organized attackers (terrorists, military groups, etc.). Over the last 10 years, as part of my research team's activities and based on international collaboration, we have proposed several methods effectively approximating SG solutions using optimization metaheuristics and random sampling methods. The aim of the thesis is to extend some of these methods to the case of multi-objective SGs in which the Attacker (Follower) and the Defender (Leader) have more than one criterion for optimizing their strategies.
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| 7 |
“Machine learning-based diagnosis of spine injuries using computed tomography” Computed tomography (CT) is a crucial imaging technique in medical diagnosis and is the preferred modality for assessing spinal trauma. However, the large volume of image data generated during tomographic examinations presents significant challenges for image analysis and diagnosis. Artificial intelligence (AI) offers the potential to enhance the speed and accuracy of diagnosis in such cases. This research aims to explore the application of machine learning (ML) methods and deep neural networks (DNNs) for the automated detection of traumatic vertebral body injuries. The study will focus on classifying vertebral fractures, distinguishing between traumatic and non-traumatic cases. The learning dataset will be constructed using trauma examination records from a clinical hospital in Warsaw. These records will consist of X-ray spine tomography studies in DICOM format, annotated by experienced radiologists. The AO Spine Classification system for thoracolumbar injuries will serve as the framework for categorizing spinal fractures. To address the computational challenges posed by the high dimensionality of tomographic data (i.e., the large number of voxels per examination), methods for reducing data size will be employed. Various deep neural network architectures will be evaluated to determine their efficacy and performance in fracture classification. Furthermore, the interpretability and explainability of the developed ML-based approach will be analyzed using tools and techniques from Explainable AI (XAI). Efforts will be made to validate the reliability of the recommendations generated by the ML models, ensuring they align with clinical expertise and established diagnostic standards.
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“Deep neural networks for image reconstruction in electrical capacitance tomography” Electrical capacitance tomography (ECT) enables the visualization of the spatial distribution of an object’s electrical permittivity. Image reconstruction in ECT presents a significant challenge as it is an ill-posed and ill-conditioned inverse problem. Advanced nonlinear algorithms, such as the Levenberg-Marquardt method, are iterative and computationally intensive, primarily due to the repeated calculation of the Jacobian matrix. Deep neural networks (DNNs) have emerged as a promising alternative for image reconstruction. This work will explore deep network architectures that not only match but significantly outperform classical methods in reconstruction quality. Synthetic data will be employed for supervised learning, and the performance of DNNs will be tested against real measurements. Application-specific training datasets will be analyzed, and their size will be expanded using data augmentation techniques. A comparative evaluation of various training datasets will also be conducted. To optimize the network, different loss functions and solvers will be utilized. Reconstructed images will be generated using DNNs and compared against a baseline provided by the Levenberg-Marquardt algorithm. Results will be assessed using selected image quality metrics. It is anticipated that deep networks will enhance the spatial resolution of ECT scanners. The insights gained from selecting deep networks, analyzing their architectures, and refining training strategies can potentially be applied to solve inverse problems in other fields.
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