Mahbub Ul Alam
Orcid: 0000-0002-1101-3793Affiliations:
- Stockholm University, Department of Computer and System Sciences, Sweden
According to our database1,
Mahbub Ul Alam
authored at least 10 papers
between 2020 and 2024.
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Bibliography
2024
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things: Enhancing COVID-19 & Early Sepsis Detection.
PhD thesis, 2024
2023
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices.
Sensors, January, 2023
SHAMSUL: Simultaneous Heatmap-Analysis to investigate Medical Significance Utilizing Local interpretability methods.
CoRR, 2023
COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning.
Proceedings of the 36th IEEE International Symposium on Computer-Based Medical Systems, 2023
2022
Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography.
Proceedings of the 35th IEEE International Symposium on Computer-Based Medical Systems, 2022
2021
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application.
Sensors, 2021
Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text.
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, 2021
2020
Intelligent context-based healthcare metadata aggregator in internet of medical things platform.
Proceedings of the 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2020) / The 15th International Conference on Future Networks and Communications (FNC-2020) / The 10th International Conference on Sustainable Energy Information Technology, 2020
Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning.
Proceedings of the Biomedical Engineering Systems and Technologies, 2020
Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis.
Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), 2020