Now showing items 5170-5189 of 10352

    • 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 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 for early detection of structure loss in the Czochralski process 

      Eikås, Leander Jacob Nielsen (Master thesis, 2024-06-03)
      This thesis investigates the use of machine learning techniques to predict structural loss in the Czochralski process. The Czochralski process is the industry standard for producing high-quality mono-crystalline silicon ...
    • 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 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 ...
    • 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 ...
    • Macromolecular sheets direct the morphology and orientation of plate-like biogenic guanine crystals 

      Wagner, Avital; Upcher, Alexander; Maria, Raquel; Magnesen, Thorolf; Zelinger, Einat; Raposo, Graca; Palmer, Benjamin (Journal article; Peer reviewed, 2023)
      Animals precisely control the morphology and assembly of guanine crystals to produce diverse optical phenomena in coloration and vision. However, little is known about how organisms regulate crystallization to produce ...
    • Macrophage phenotype transitions in a stochastic gene-regulatory network model 

      Frank, Anna-Simone Josefine; Larripa, Kamila; Ryu, Hwayeon; Röblitz, Susanna (Journal article; Peer reviewed, 2023)
      Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance ...
    • Macroscale mesenchymal condensation to study cytokine-driven cellular and matrix-related changes during cartilage degradation 

      Weber, Marie-Christin; Fischer, Lisa; Damerau, Alexandra; Ponomarev, Igor; Pfeiffenberger, Moritz; Gaber, Timo; Götschel, Sebastian; Lang, Jens; Röblitz, Susanna; Buttgereit, Frank; Ehrig, Rainald; Lang, Annemarie (Journal article; Peer reviewed, 2020)
      Understanding the pathophysiological processes of cartilage degradation requires adequate model systems to develop therapeutic strategies towards osteoarthritis (OA). Although different in vitro or in vivo models have been ...
    • The Madden-Julian Oscillation in a warmer world 

      Chang, Chiung-Wen June; Tseng, Wan-Ling; Huang-Hsiung, Hsu; Keenlyside, Noel; Tsuang, Ben-Jei (Peer reviewed; Journal article, 2015-07)
      Global warming's impact on the Madden-Julian Oscillation (MJO) is assessed using one of the few models capable in reproducing its key features. In a warmer climate predicted for the end of the century, the MJO increases ...
    • MAGIC observations provide compelling evidence of hadronic multi-TeV emission from the putative PeVatron SNR G106.3+2.7 

      Abe, H.; Abe, S.; Acciari, V.A.; Agudo, I.; Aniello, T.; Ansoldi, S.; Antonelli, L.A.; Arbet Engels, Engels; Arcaro, C.; Artero, M.; Asano, K.; Baack, D.; Babić, A.; Baquero, A.; Barres De Almeida, De; Barrio, J.A.; Batković, I.; Baxter, J.; Becerra González, Gonzalez; Bednarek, W.; Bernardini, E.; Bernardos, M.; Berti, A.; Besenrieder, J.; Bhattacharyya, W.; Bigongiari, C.; Biland, A.; Blanch, O.; Bonnoli, G.; Bošnjak, Z.; Burelli, I.; Busetto, G.; Carosi, R.; Carretero-Castrillo, M.; Castro-Tirado, A.J.; Ceribella, G.; Chai, Y.; Chilingarian, A.; Cikota, S.; Colombo, E.; Contreras, J.L.; Cortina, J.; Covino, S.; D'Amico, Giacomo; D'Elia, V.; Da Vela, Vela; Dazzi, F.; De Angelis, Angelis; De Lotto, Lotto; Del Popolo, Popolo; Delfino, M.; Delgado, J.; Delgado Mendez, Mendez; Depaoli, D.; Di Pierro, Pierro; Di Venere, Venere; Do Souto Espiñeira, Souto; Dominis Prester, Prester; Donini, A.; Dorner, D.; Doro, M.; Elsaesser, D.; Emery, G.; Escudero, J.; Fallah Ramazani, Ramazani; Fariña, L.; Fattorini, A.; Font, L.; Fruck, C.; Fukami, S.; Fukazawa, Y.; García López, Lopez; Garczarczyk, M.; Gasparyan, S.; Gaug, M.; Giesbrecht Paiva, Paiva; Giglietto, N.; Giordano, F.; Gliwny, P.; Godinović, N.; Grau, R.; Green, D.; Green, J.G.; Hadasch, D.; Hahn, A.; Hassan, T.; Heckmann, L.; Herrera, J.; Hrupec, D.; Hütten, M.; Imazawa, R.; Inada, T.; Iotov, R.; Ishio, K.; Jiménez Martínez, Martinez; Jormanainen, J.; Kerszberg, D.; Kobayashi, Y.; Kubo, H.; Kushida, J.; Lamastra, A.; Lelas, D.; Leone, F.; Lindfors, E.; Linhoff, L.; Lombardi, S.; Longo, F.; López-Coto, R.; López-Moya, M.; López-Oramas, A.; Loporchio, S.; Lorini, A.; Lyard, E.; MacHado De Oliveira Fraga, De; Majumdar, P.; Makariev, M.; Maneva, G.; Mang, N.; Manganaro, M.; Mangano, S.; Mannheim, K.; Mariotti, M.; Martínez, M.; Mas Aguilar, Aguilar; Mazin, D.; Menchiari, S.; Mender, S.; Mićanović, S.; Miceli, D.; Miener, T.; Miranda, J.M.; Mirzoyan, R.; Molina, E.; Mondal, H.A.; Moralejo, A.; Morcuende, D.; Moreno, V.; Nakamori, T.; Nanci, C.; Nava, L.; Neustroev, V.; Nievas Rosillo, Rosillo; Nigro, C.; Nilsson, K.; Nishijima, K.; Njoh Ekoume, Ekoume; Noda, K.; Nozaki, S.; Ohtani, Y.; Oka, T.; Okumura, A.; Otero-Santos, J.; Paiano, S.; Palatiello, M.; Paneque, D.; Paoletti, R.; Paredes, J.M.; Pavletić, L.; Persic, M.; Pihet, M.; Pirola, G.; Podobnik, F.; Prada Moroni, Moroni; Prandini, E.; Principe, G.; Priyadarshi, C.; Rhode, W.; Ribó, M.; Rico, J.; Righi, C.; Rugliancich, A.; Sahakyan, N.; Saito, T.; Sakurai, S.; Satalecka, K.; Saturni, F.G.; Schleicher, B.; Schmidt, K.; Schmuckermaier, F.; Schubert, J.L.; Schweizer, T.; Sitarek, J.; Sliusar, V.; Sobczynska, D.; Spolon, A.; Stamerra, A.; Strišković, J.; Strom, D.; Strzys, M.; Suda, Y.; Surić, T.; Tajima, H.; Takahashi, M.; Takeishi, R.; Tavecchio, F.; Temnikov, P.; Terauchi, K.; Terzić, T.; Teshima, M.; Tosti, L.; Truzzi, S.; Tutone, A.; Ubach, S.; Van Scherpenberg, Scherpenberg; Vazquez Acosta, Acosta; Ventura, S.; Verguilov, V.; Viale, I.; Vigorito, C.F.; Vitale, V.; Vovk, I.; Walter, R.; Will, M.; Wunderlich, C.; Yamamoto, T.; Zarić, D. (Journal article; Peer reviewed, 2023)
      Context. Certain types of supernova remnants (SNRs) in our Galaxy are assumed to be PeVatrons, capable of accelerating cosmic rays (CRs) to ~ PeV energies. However, conclusive observational evidence for this has not yet ...