Sökning: "Machine learning ML"

Visar resultat 1 - 5 av 95 avhandlingar innehållade orden Machine learning ML.

  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. Visual Analytics for Explainable and Trustworthy Machine Learning

    Författare :Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

    Sammanfattning : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. LÄS MER

  4. 3. 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

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

    Författare :Leo Carlsson; Pär Jönsson; Peter Samuelsson; Mikael Vejdemo-Johansson; Henrik Saxen; KTH; []
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER

  7. 5. Voice for Decision Support in Healthcare Applied to Chronic Obstructive Pulmonary Disease Classification : A Machine Learning Approach

    Författare :Alper Idrisoglu; Johan Sanmartin Berglund; Blekinge Tekniska Högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Automated decision-support; Classification; Machine Learning; Voice-affecting disorders; Voice dataset; Voice Features; Chronic Obstructive pulmonary disease COPD ; Tillämpad hälsoteknik; Applied Health Technology;

    Sammanfattning : Background: Advancements in machine learning (ML) techniques and voice technology offer the potential to harness voice as a new tool for developing decision-support tools in healthcare for the benefit of both healthcare providers and patients. Motivated by technological breakthroughs and the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, numerous studies aim to investigate the diagnostic potential of ML algorithms in the context of voice-affecting disorders. LÄS MER