Now showing items 18722-18741 of 34397

    • Machine learning and artificial neural networks for seismic processing 

      Jonge, Thomas de (Doctoral thesis, 2023-05-05)
      Nylig har tilgjengeligheten av kraftige GPUer og "open source"-programvare gjort det mulig for kunstige nevrale nettverk å løse flere praktiske og industrielle problemer. Vi kan bruke nevrale nettverk til viktige seismiske ...
    • Machine learning and electronic health records 

      Stavland, Sivert (Master thesis, 2020-10-02)
      In this work, we investigate the benefits and complications of using machine learning on EHR data. We survey some recent literature and conduct experiments on real data collected from hospital EHR systems.
    • Machine learning applications in proteomics research: How the past can boost the future 

      Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, S; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart (Peer reviewed; Journal article, 2014)
      Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution ...
    • Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data 

      Zhang, Xiaokang; Jonassen, Inge; Goksøyr, Anders (Chapter, 2021)
      Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. ...
    • Machine learning approaches for high-dimensional genome-wide association studies 

      Malik, Muhammad Ammar (Doctoral thesis, 2022-08-24)
      Formålet med Genome-wide association studies (GWAS) er å finne statistiske sammenhenger mellom genetiske varianter og egenskaper av interesser. De genetiske variantene som forklarer mye av variasjonene i genomfattende ...
    • Machine Learning Approaches in Imaging Genetics 

      Tesaker, Karianne (Master thesis, 2021-06-01)
      Established approaches in imaging genetics and genome wide association studies (GWAS) such as univariate, multivariate and voxel-wise approaches, are prone to certain disadvantages such as being computationally expensive, ...
    • Machine learning approaches in microbiome research: challenges and best practices 

      Papoutsoglou, Georgios; Tarazona, Sonia; Lopes, Marta B.; Klammsteiner, Thomas; Ibrahimi, Eliana; Eckenberger, Julia; Novielli, Pierfrancesco; Tonda, Alberto; Simeon, Andrea; Shigdel, Rajesh; Béreux, Stéphane; Vitali, Giacomo; Tangaro, Sabina; Lahti, Leo; Temko, Andriy; Claesson, Marcus J.; Berland, Magali (Journal article; Peer reviewed, 2023)
      Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model ...
    • Machine learning ATR-FTIR spectroscopy data for the screening of collagen for ZooMS analysis and mtDNA in archaeological bone 

      Pal Chowdhury, Manasij; Choudhury, Kaustabh Datta; Bouchard, Geneviève Pothier; Riel-Salvatore, Julien; Negrino, Fabio; Benazzi, Stefano; Slimak, Ludovic; Frasier, Brenna; Szabo, Vicki; Harrison, Ramona; Hambrecht, George; Kitchener, Andrew C.; Wogelius, Roy A.; Buckley, Mike (Journal article; Peer reviewed, 2021)
      Faunal remains from archaeological sites allow for the identification of animal species that enables the better understanding of the relationships between humans and animals, not only from their morphological information, ...
    • Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction 

      Woolley, Rebecca J.; Ceelen, Daan; Ouwerkerk, Wouter; Tromp, Jasper; Figarska, Sylwia M.; Anker, Stefan D.; Dickstein, Kenneth; Filippatos, Gerasimos; Zannad, Faiez; Marco, Metra; Ng, Leong L.; Samani, Nilesh J.; van Veldhuisen, Dirk J; Lang, Chim C.; Lam, Carolyn S.P.; Voors, Adriaan A. (Journal article; Peer reviewed, 2021)
      Aims The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic ...
    • Machine learning in marine ecology: an overview of techniques and applications 

      Rubbens, Peter; Brodie, Stephanie; Cordier, Tristan; Desto Barcellos, Diogo; DeVos, Paul; Fernandes-Salvador, Jose A; Fincham, Jennifer; Gomes, Alessandra; Handegard, Nils Olav; Howell, Kerry L.; Jamet, Cédric; Kartveit, Kyrre Heldal; Moustahfid, Hassan; Parcerisas, Clea; Politikos, Dimitris V.; Sauzède, Raphaëlle; Sokolova, Maria; Uusitalo, Laura; Van den Bulcke, Laure; van Helmond, Aloysius; Watson, Jordan T.; Welch, Heather; Beltran-Perez, Oscar; Chaffron, Samuel; Greenberg, David S.; Kühn, Bernhard; Kiko, Rainer; Lo, Madiop; Lopes, Rubens M.; Möller, Klas Ove; Michaels, William; Pala, Ahmet; Romagnan, Jean-Baptiste; Schuchert, Pia; Seydi, Vahid; Villasante, Sebastian; Malde, Ketil; Irisson, Jean-Olivier (Journal article; Peer reviewed, 2023)
      Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific ...
    • Machine Learning methods for mood disorder decision support 

      Oleksy, Tomasz Artur (Master thesis, 2017-07-11)
    • Machine learning methods for preference aggregation 

      Kujawska, Hanna Maria (Master thesis, 2019-11-14)
      Preference aggregation is the process of combining multiple preferences orders into one global ranking. The top-ranked alternative is called the winner. Many aggregation methods have been considered in the literature. Some ...
    • Machine learning vs logistic regression in credit scoring: A trade-off between accuracy and interpretability? 

      Hovdenakk, Arne Hesjedal (Master thesis, 2021-06-15)
      In this thesis, I compare logistic regression to the machine learning models k-nearest neighbor, decision trees, random forest, and gradient booster by creating different credit models. By using data from an anonymous ...
    • Machine Teaching for Explainable AI: Proof of Concept 

      Håvardstun, Brigt Arve Toppe (Master thesis, 2022-06-21)
      In today’s society, AI and machine learning are becoming more and more relevant. Following this, the field of Explainable AI is becoming of more relevance. The research project ”Machine Teaching for Explainable AI” aims ...
    • Machine Vision: How Algorithms are Changing the Way We See the World 

      Rettberg, Jill Walker (Book, 2023)
      Humans have used technology to expand our limited vision for millennia, from the invention of the stone mirror 8,000 years ago to the latest developments in facial recognition and augmented reality. We imagine that ...
    • Macro and Microstructural Characteristics of North Atlantic Deep-Sea Sponges as Bioinspired Models for Tissue Engineering Scaffolding 

      Martins, Eva; Rapp, Hans Tore; Xavier, Joana R.; Diogo, Gabriela S.; Reis, Rui L.; Silva, Tiago H. (Journal article; Peer reviewed, 2021)
      Sponges occur ubiquitously in the marine realm and in some deep-sea areas they dominate the benthic communities forming complex biogenic habitats – sponge grounds, aggregations, gardens and reefs. However, deep-sea sponges ...
    • Macroecological context predicts species' responses to climate warming 

      Lynn, Joshua Scott; Klanderud, Kari; Telford, Richard James; Goldberg, Deborah E.; Vandvik, Vigdis (Journal article; Peer reviewed, 2021)
      Context-dependencies in species' responses to the same climate change frustrate attempts to generalize and make predictions based on experimental and observational approaches in biodiversity science. Here, we propose ...
    • Macroeconomy and macropartisanship: Economic conditions and party identification 

      Okolikj, Martin; Quinlan, Stephen; Lewis-Beck, Michael S. (Journal article; Peer reviewed, 2022)
      “It's the economy stupid”—is the phrase that captures the ubiquity of economics in determining election outcomes. Nevertheless, while several studies support the premise of economic voting, a constant critique of valence ...