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2025

Atmakuru, A. (2025) Enhancing the performance and transparency of Machine Learning (ML) using MRI-derived data: alternative approaches to ML interpretability-explainability. PhD thesis, University of Reading. doi: 10.48683/1926.00127413

2024

Atmakuru, A., Di Fatta, G., Nicosia, G. and Badii, A. (2024) Improved filter-based feature selection using correlation and clustering techniques. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P. M. and Umeton, R. (eds.) Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I. Lecture Notes in Computer Science, 14505. Springer, pp. 379-389. ISBN 9783031539688 doi: 10.1007/978-3-031-53969-5_28

Atmakuru, A., Di Fatta, G., Nicosia, G., Varzandian, A. and Badii, A. (2024) Sensitivity analysis for feature importance in predicting Alzheimer’s Disease. In: Nicosia, G., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, La Malfa, E., Pardalos, P. M. and Umeton, R. (eds.) Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II. Lecture Notes in Computer Science, 14506. Springer, pp. 449-465. ISBN 9783031539657 doi: 10.1007/978-3-031-53966-4_33