The project achieved significant results in developing, adapting, and applying advanced machine learning and artificial intelligence solutions for data analysis across various fields. The methods created and successfully implemented enabled the efficient processing of both structured and unstructured data in various fields, including energy, biomedicine, digital pathology, crystallography, and text analysis, ensuring the high accuracy, stability, and practical applicability of the models.

The classical and deep learning models developed during this research were applied to real datasets, which included energy measurements, medical signals, images, and multilingual texts. We achieved impressive results: text classification accuracy exceeded 99%, a signal-to-noise ratio greater than 20 dB was achieved in signal restoration tasks, and diagnostic accuracy was significantly improved in medical image segmentation tasks. The solutions developed offer direct practical benefits in energy forecasting, medical diagnostics, and information management systems.

In parallel, research on hybrid quantum-classical methods was conducted using high-performance computing and quantum computing infrastructure. New quantum algorithms and hybrid models have been developed, with some published in prestigious international scientific journals, showcasing the potential of quantum methods to reduce computational complexity and address real-world, applied problems, particularly in biomedicine.

The project emphasised the importance of data infrastructure and open science. An automated platform for the long-term storage and management of scientific data was created, along with standardised metadata schemes to ensure data integrity, compatibility, and reuse. In the fields of life and chemical sciences, large-scale, validated datasets were prepared and made available, integrated into international open databases.

In digital pathology, the development of new spatial biomarkers and federated learning solutions has improved disease prediction and enhanced the potential for personalised medicine. The project also contributed to mobilising the scientific community and enhancing infrastructure by organising international events, strengthening international collaboration, and creating high-performance computing environments.

The results obtained lay a solid technological and methodological foundation for further scientific advancement, practical implementation, and a long-term impact on the progress of science, technology, and society.