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- 2025
-
Mark
Moving towards cognitive radio access networks : transforming MIMO complexities into opportunities
2025) In 1654-790X(
- Thesis › Doctoral thesis (compilation)
-
Mark
Predicting adverse cardiac events at the emergency department : A deep learning approach
2025) In Lund University, Faculty of Medicine Doctoral Dissertation Series(
- Thesis › Doctoral thesis (compilation)
-
Mark
Bayesian optimization across the spectrum of knowledge : enhancing efficiency through beliefs, information and assumptions
2025)(
- Thesis › Doctoral thesis (compilation)
-
Mark
Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics
2025) In Lund University, Faculty of Medicine Doctoral Dissertation Series(
- Thesis › Doctoral thesis (compilation)
-
Mark
Copy number signatures in cervical samples enable early detection of high-grade serous ovarian carcinoma
2025) SCRM 2025 - the 7th Swedish Cancer Research Meeting(
- Contribution to conference › Poster
- 2024
-
Mark
Searches for New Physics Using Innovative Data Acquisition, Analysis, and Compression Techniques
2024)(
- Thesis › Doctoral thesis (monograph)
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Mark
Spatial Machine Learning for Exploring the Variability in Low Height-For-Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda
(
- Contribution to journal › Article
-
Mark
The predictive value of MUSA features of adenomyosis on live birth is poor, using a machine learning algorithm
2024) ISUOG 34th World Congress on Ultrasound in Obstetrics and Gynecology - Budapest In Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 64(S1). p.88-89(
- Contribution to journal › Published meeting abstract
-
Mark
Exploring Artificial Intelligence in Emergency Medicine for Predicting Disposition, Death, and Diagnosis
2024) In Lund University, Faculty of Medicine Doctoral Dissertation Series(
- Thesis › Doctoral thesis (compilation)
- 2023
-
Mark
Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest : A case study in Rwanda
(
- Contribution to journal › Article