Wykaz obszarów badawczych związanych z tagiem Machine-learning:
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| 1 |
see https://www.gagolewski.com/
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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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My main research interests include application of Artificial Intelligence and Machine Learning to the development of general-purpose human-like / human-level learning and problem-solving methods. Building such multi-purpose solving systems includes, among others, the concepts of explainability, universality, knowledge abstraction, task sharing, and continuality of the learning process. I’m also interested in the development of hybrid metaheuristic solution methods with application to games, dynamic and bi-level optimization problems, and human-machine cooperation in problem solving. For more information please visit http://www.mini.pw.edu.pl/~mandziuk
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| 5 |
Operations and Maintenance system in 6G Mobile Networks: analysis of Machine Learning impact in the network management as a whole (multi-agent environment)
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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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computer vision (SLAM, visual search), machine learning (deep learning, generative models, continual learning), representation learning (binary descriptors).
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Machine learning for social and sensor data This issue is dedicated to candidates with knowledge of machine learning methods, the ability to acquire and process data and those interested in both development in this area and the application of these methods to social issues. The goal will be to develop methods for modeling transport behaviors using data from various sources and machine learning methods, including methods dedicated to data streams and transfer learning methods. In particular, it is planned to use data streams describing the actual availability of various means of communication and their fusion, e.g. with survey data. This research issue is directly related to the ongoing international CoMobility research grant including research groups focused on social research and air pollution modelling.
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| 9 |
The objective of the PhD student will be to train a neural network based on a family of impedance frequency characteristics measured for magnetic nanocrystalline rings. This network will enable the mapping of the measured frequency characteristics with lumped element electric circuit (LEEC) model. The intention is to replace the modified-Pade method, as published in the article CEM2014 with a new method based on neutral network (the CEM2014 paper: Szewczyk, J. Pawłowski, K. Kutorasiński, S. Burow, S. Tenbohlen, W. Piasecki, "Identification of a rational function in the s-domain describing the frequency characteristics of a magnetic material," Proceedings of the 9th International Conference on Computation in Electromagnetics (CEM), 31st March - 1st April 2014, London, UK). More information, see: https://wutwaw-my.sharepoint.com/:w:/g/personal/marcin_szewczyk_pw_edu_pl/Ea6m_83KcehBo-oi2NuWHb8BlwiEg6mxS307fjKB0lp3GA?e=h45tAF
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