Wykaz obszarów badawczych związanych z tagiem AI:
| # | Obszar badawczy | Dziedzina naukowa |
|---|---|---|
| 1 | Integration of knowledge and ICT systems. The development of ICT infrastructure as a key layer of civilization will follow several basic directions. At the technological level, it is the integration of more and more distant functionalities, virtualization, development of structural components, major and minor, such as energy resources, databases and centers computing, clouds, fog, next-generation mobility such as 6G and quantum, networg edge, artificial intelligenceand machine learning, internet of things, digital twins, cybersecurity, and more. At the infrastructural level, it is the merging of ICT with other technical and non-technical layers of civilization material and non-material, such as science and knowledge, medicine and health protection, culture, transport, construction, civil engineering, energy, industry, security, and much more. The research concerns the directions of ICT development and their infrastructure integration. | |
| 2 |
Modeling, control, and simulation of complex systems (ICT, financial engineering, medicine, water resources, etc.), computer decision support systems, recommendation systems, wireless sensor networks, mobile ad hoc networks, optimal resource allocation in data networks and computing centers, parallel and distributed programming, global optimization algorithms, machine learning and Big Data processing, blockchain technologies, cyber security.
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| 3 |
The prevailing solutions used in the practice of the operation of power systems are narrowed to the central balancing mechanisms. The proposed research field is about breaking this paradigm and considering a distributed balancing system involving multiple agents with different, sometimes conflicting goals. The topic is of key importance in the light of the ongoing changes, the increase in the number of distributed energy resources and, consequently, the introduction of rigidity in the system, the emergence of new customers and producers (e.g., electric cars), as well as the processes of democratization of the energy sector. The research topic includes the recognition of existing concepts of distributed balancing mechanisms, critical analysis (including evaluation methodology), and the proposal and research of new balancing mechanisms. The issue is multidisciplinary. It touches on economic elements (maximization of economic benefits) but also social factors (fair distribution of balancing costs) and contains technological potential (e.g., blockchain as a potential solution).
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| 4 |
“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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