Institut für Wirtschaftsinformatik
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/66
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96 Ergebnisse
Ergebnisse nach Hochschule und Institut
Publikation Optimized Computational Diabetes Prediction with Feature Selection Algorithms(2023) Li, Xi; Curiger, Michèle; Dornberger, Rolf; Hanne, Thomas04B - Beitrag KonferenzschriftPublikation Computational Intelligence in Logistik und Supply Chain Management(Springer Gabler, 2023) Hanne, Thomas; Dornberger, RolfPräsentiert 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 - MonographiePublikation Invasive weed optimization for solving index tracking problems(Springer, 2015) Affolter, Konstantin; Hanne, Thomas; Schweizer, Daniel; Dornberger, Rolf01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Echtzeit Ressourcendisposition von Personal und Rollmaterial in der Eisenbahnbranche(Innosuisse, 2023) Ehrenthal, Joachim; Hanne, Thomas; Telesko, Rainer; Gachnang, PhillipZu 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 ArbeitsberichtPublikation Sentiment analysis for a swiss gig platform company(2019) Pustulka, Elzbieta; Hanne, ThomasWe work with a Swiss Gig Platform Company to identify innovative solutions which could strengthen its position as a market leader in Switzerland and Europe. The company mediates between employers and employees in short term work contracts via a platform system. We first looked at the business processes and saw that some process parts were not being controlled by the company, which is now being remedied. Second, we analyzed the job reviews which the employers and employees write, and implemented a prototype which can detect negative statements automatically, even if the review is positive overall. We worked with a dataset of 963 job reviews from employers and employees, in German, French and English. The reviews have a star rating (1 to 4 stars), with some discrepancies between the star rating and the text. We scored the reviews manually as negative or other, as negative reviews are important for business improvement. We tested several machine learning methods and a hybrid method from Lexalytics.06 - PräsentationPublikation FLIE with rules(2021) Pustulka, Elzbieta; Hanne, Thomas; de Espona, LucíaFLIE (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 - PräsentationPublikation Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities(2019) Lehner, Joël Enrico; Dornberger, Rolf; Simic, Radovan; Hanne, ThomasThis paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.04B - Beitrag KonferenzschriftPublikation Optimization of a robotic manipulation path by an evolution strategy and particle swarm optimization(2020) Murillo, Francis; Neuenschwander, Tobias; Dornberger, Rolf; Hanne, Thomas04B - Beitrag KonferenzschriftPublikation 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., Jagadeesh04B - Beitrag KonferenzschriftPublikation An experiment with an optimization game(2019) Pustulka, Elzbieta; Hanne, Thomas; Adriaensen, Benjamin; Eggenschwiler, Stefan; Kaba, Egemen; Wetzel, Richard; Blashki, Katherine; Xiao, YingcaiWe aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI).04B - Beitrag Konferenzschrift