Hochschule für Wirtschaft FHNW

Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/60

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Bereich: Suchergebnisse

Gerade angezeigt 1 - 10 von 50
  • Publikation
    Optimized Computational Diabetes Prediction with Feature Selection Algorithms
    (2023) Li, Xi; Curiger, Michèle; Dornberger, Rolf; Hanne, Thomas
    04B - Beitrag Konferenzschrift
  • Publikation
    Computational Intelligence in Logistik und Supply Chain Management
    (Springer Gabler, 2023) Hanne, Thomas; Dornberger, Rolf
    Präsentiert den aktuellen Stand der Technik beim Einsatz von Computational Intelligence in der Lieferkette. Behandelt Probleme in den Bereichen Bestands- und Produktionsplanung, Scheduling, Transportplanung. Überprüft die verfügbare Software und Informationssysteme für jeden der behandelten Problembereiche.
    02 - Monographie
  • Publikation
    Echtzeit Ressourcendisposition von Personal und Rollmaterial in der Eisenbahnbranche
    (Innosuisse, 2023) Ehrenthal, Joachim; Hanne, Thomas; Telesko, Rainer; Gachnang, Phillip
    Zu wenig Personal und Rollmaterial, kurzfristig angesagte Arbeiten an der Infrastruktur mit den entsprechenden betrieblichen Behinderungen und Einschränkungen sowie kurzfristig auftretende Störungen prägen zurzeit die Berichterstattung über die Entwicklungen im öffentlichen Verkehr der Schweiz. Es ist absehbar, dass sich diese unbefriedigende Situation über eine längere Zeitspanne kaum massgeblich verbessern wird. Umso wichtiger ist es, vorhandene Ressourcen optimal einzusetzen und den zukünftigen Bedarf an Mitarbeitenden und Rollmaterial in den Griff zu kriegen. Die Fachhochschulen der Ostschweiz (OST) und der Nordwestschweiz FHNW entwickelten mit der Südostbahn (SOB), den luxemburgischen Eisenbahnen (CFL) und der Eisenbahn-Softwareherstellerin Qnamic eine zukunftsweisende Software zur Unterstützung der Eisenbahn-Disposition, um in Echtzeit über situationsspezifische Massnahmenpakete zur Störungsbehebung zu verfügen.
    05 - Forschungs- oder Arbeitsbericht
  • Publikation
    FLIE with rules
    (2021) Pustulka, Elzbieta; Hanne, Thomas; de Espona, Lucía
    FLIE (Form Labelling for Information Extraction) allows us to extract information from Swiss insurance policies. Insurance policies are forms which are weakly aligned and do not lend themselves to automated data extraction without preprocessing. Our preprocessing annotates data with geometry and combined with manual training data generation gives the extraction accuracy of over 80% for a subset of attributes which have been seen 8 times or more. In this paper we extend FLIE with rules. The aim is to compare machine learning used in FLIE to the standard industry approach of using rules to extract data. We hand crafted rules (regular expressions in Python) for the KTG insurance (27 rules), UVG insurance (29 rules), and UVG-Z (23 rules), for each insurance type covering around 20 attributes. We also generated rules for building insurance policies which we were new to (16 rules encoded in SpaCy). In all cases we saw that using rules alone gives us a similar accuracy in data extraction to machine learning (around 80%). In the case of building insurance the accuracy is higher, above 96%, with precision and recall around 89-92%. To support annotation and experimental evaluation, we created an annotation GUI and a GUI which automates the ML experiment. Planned work includes a comparison of rule based and ML approaches and extension to further policy types.
    06 - Präsentation
  • Publikation
    Optimization of a robotic manipulation path by an evolution strategy and particle swarm optimization
    (2020) Murillo, Francis; Neuenschwander, Tobias; Dornberger, Rolf; Hanne, Thomas
    04B - Beitrag Konferenzschrift
  • Publikation
    Improved long-short term memory U-Net for image segmentation
    (Springer, 2021) Oller, Heide; Dornberger, Rolf; Hanne, Thomas; Thampi, Sabu M.; Krishnan, Sri; Hegde, Rajesh M.; Ciuonzo, Domenico; Hanne, Thomas; Kannan R., Jagadeesh
    04B - Beitrag Konferenzschrift
  • Publikation
    Parameter selection for ant colony optimization for solving the travelling salesman problem based on the problem size
    (2021) Kempter, Philipp; Schmitz, Martin Peter; Hanne, Thomas; Dornberger, Rolf; Abraham, Ajith; Hanne, Thomas; Castillo, Oscar; Gandhi, Niketa; Nogueira Rios, Tatiane; Hong, Tzung-Pei
    04B - Beitrag Konferenzschrift
  • Publikation
    Comparison of swarm and graph algorithms for solving travelling salesman problems
    (2020) Eggenschwiler, Stefan; Spahic, Maja; Hanne, Thomas; Dornberger, Rolf
    04B - Beitrag Konferenzschrift
  • Publikation
    FLIE: form labeling for information extraction
    (2021) Pustulka, Elzbieta; Hanne, Thomas; Gachnang, Phillip; Biafora, Pasquale; Arai, Kohei; Kapoor, Supriya; Bhatia, Rahul
    Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84-89%. It is currently being extended to other policy types and languages.
    04B - Beitrag Konferenzschrift
  • Publikation
    04B - Beitrag Konferenzschrift