Search for dissertations about: "machine learning prediction data"

Showing result 1 - 5 of 172 swedish dissertations containing the words machine learning prediction data.

2. 1. Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection

   Author : Mahbub Ul Alam; Rahim Rahmani; Jaakko Hollmén; Sadok Ben Yahia; Stockholms universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Internet of Medical Things; Patient-Centric Healthcare; Clinical Decision Support System; Predictive Modeling in Healthcare; Health Informatics; Healthcare analytics; COVID-19; Sepsis; COVID-19 Detection; Early Sepsis Detection; Lung Segmentation Detection; Medical Data Annotation Scarcity; Medical Data Sparsity; Medical Data Heterogeneity; Medical Data Security Privacy; Practical Usability Enhancement; Low-End Device Adaptability; Medical Significance; Interpretability; Visualization; LIME; SHAP; Grad-CAM; LRP; Electronic Health Records; Thermal Image; Tabular Medical Data; Chest X-ray; Machine Learning; Deep Learning; Federated Learning; Semi-Supervised Machine Learning; Multi-Task Learning; Transfer Learning; Multi-Modality; Natural Language Processing; ClinicalBERT; GAN; data- och systemvetenskap; Computer and Systems Sciences;

   Abstract : This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. READ MORE

4. 2. Learning from Complex Medical Data Sources

   Author : Jonathan Rebane; Panagiotis Papapetrou; Isak Samsten; Myra Spiliopoulou; Stockholms universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Data Science; Healthcare; Complex Data; Explainable AI; Deep Learning; data- och systemvetenskap; Computer and Systems Sciences;

   Abstract : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. READ MORE

5. 3. Applied Machine Learning in Steel Process Engineering : Using Supervised Machine Learning Models to Predict the Electrical Energy Consumption of Electric Arc Furnaces

   Author : Leo Carlsson; Pär Jönsson; Peter Samuelsson; Mikael Vejdemo-Johansson; Henrik Saxen; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Electric Arc Furnace; Electrical Energy Consumption; Statistical Modelling; Machine Learning; Interpretable Machine Learning; Predictive Modelling; Industry 4.0; Ljusbågsugn; Elenergiförbrukning; Statistisk Modellering; Maskininlärning; Tolkningsbar Maskininlärning; Prediktiv Modellering; Industri 4.0; Teknisk materialvetenskap; Materials Science and Engineering; Metallurgical process science; Metallurgisk processvetenskap;

   Abstract : The steel industry is in constant need of improving its production processes. This is partly due to increasing competition and partly due to environmental concerns. One commonly used method for improving these processes is through the act of modeling. READ MORE

6. 4. Data-Centric AI for Software Performance Engineering - Predicting Workload Dependent and Independent Performance of Software Systems Using Machine Learning Based Approaches

   Author : Hazem Samoaa; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Representation Learning; Software Performance Prediction; Machine Learning; Source code Representation; Graph Neural Network; Deep Learning; Data-Centric AI;

   Abstract : Context: Machine learning (ML) approaches are widely employed in various software engineering (SE) tasks. Performance, however, is one of the most critical software quality requirements. Performance prediction is estimating the execution time of a software system prior to execution. READ MORE

7. 5. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning

   Author : Mateusz Garbulowski; Jan Komorowski; Ryan J. Urbanowicz; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; complex diseases; big data; machine learning; transcriptomics; life sciences; rough sets; Bioinformatics; Bioinformatik;

   Abstract : Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. READ MORE