Sökning: "deep transfer learning"

Visar resultat 1 - 5 av 33 avhandlingar innehållade orden deep transfer learning.

1. 1. Machine learning for building energy system analysis

   Författare :Fan Zhang; Johan Håkansson; Chris Bales; Stefan Byttner; Högskolan Dalarna; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; district heating; machine learning; deep learning; HVAC; neural networks;

   Sammanfattning : Buildings account for approximately 40% of the global energy, and Heating, Ventilation, and Air Conditioning (HVAC) contributes to a large proportion of building energy consumption. Two main negative characteristics that contribute to performance degradation and energy waste in an HVAC system are inappropriate control strategies and faults. LÄS MER

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

   Författare :Mahbub Ul Alam; Rahim Rahmani; Jaakko Hollmén; Sadok Ben Yahia; Stockholms universitet; []
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

4. 3. Sharing to learn and learning to share : Fitting together metalearning and multi-task learning

   Författare :Richa Upadhyay; Marcus Liwicki; Ronald Phlypo; Rajkumar Saini; Atsuto Maki; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Multi-task learning; Meta learning; transfer learning; knowledge sharing algorithms; Machine Learning; Maskininlärning;

   Sammanfattning : This thesis focuses on integrating learning paradigms that ‘share to learn,’ i.e., Multitask Learning (MTL), and ‘learn (how) to share,’ i.e. LÄS MER

5. 4. Deep Perceptual Loss and Similarity

   Författare :Gustav Grund Pihlgren; Marcus Liwicki; Fredrik Sandin; Hugo Larochelle; Luleå tekniska universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Image Similarity; Perceptual Similarity; Perceptual Loss; Deep Features; Deep Learning; Machine Learning; Maskininlärning;

   Sammanfattning : This thesis investigates deep perceptual loss and (deep perceptual) similarity; methods for computing loss and similarity for images as the distance between the deep features extracted from neural networks. The primary contributions of the thesis consist of (i) aggregating much of the existing research on deep perceptual loss and similarity, and (ii) presenting novel research into understanding and improving the methods. LÄS MER

7. 5. Contributions to deep learning for imaging in radiotherapy

   Författare :Attila Simkó; Joakim Jonsson; Tommy Löfstedt; Anders Garpebring; Tufve Nyholm; Veronika Cheplygina; Umeå universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; deep learning; medical imaging; radiotherapy; artefact correction; bias field correction; contrast transfer; synthetic CT; reproducibility;

   Sammanfattning : Purpose: The increasing importance of medical imaging in cancer treatment, combined with the growing popularity of deep learning gave relevance to the presented contributions to deep learning solutions with applications in medical imaging.Relevance: The projects aim to improve the efficiency of MRI for automated tasks related to radiotherapy, building on recent advancements in the field of deep learning. LÄS MER