{"id":112,"date":"2018-01-17T14:06:36","date_gmt":"2018-01-17T13:06:36","guid":{"rendered":"https:\/\/www.hsu-hh.de\/ai\/?page_id=112"},"modified":"2025-11-16T19:22:32","modified_gmt":"2025-11-16T18:22:32","slug":"forschung","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/ai\/forschung\/","title":{"rendered":"Forschung &amp; Innovationen"},"content":{"rendered":"\n<p>Der Lehrstuhl deckt ein breites Methoden-, Dom\u00e4nen- und Datenformatspektrum ab und verf\u00fcgt \u00fcber umfangreiche Expertise bei der Entwicklung innovativer maschineller Lernverfahren, insbesondere moderner Deep-Learning-Architekturen sowie deren Anwendung in Wirtschaft, Verwaltung und Verteidigung:<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Autonome Roboter\/Drohnen:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Europe&#039;s Next Industrial Revolution Has Arrived\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/Sj-nSKZCcyU?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"650\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151-1024x650.jpg\" data-credit=\"https:\/\/pixabay.com\/\" alt=\"\" class=\"wp-image-412 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151-1024x650.jpg 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151-300x191.jpg 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151-768x488.jpg 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151-1100x699.jpg 1100w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/drone-e1758650004151.jpg 1280w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Im Bereich autonomer Roboter\/Drohnen verf\u00fcgt der Lehrstuhl \u00fcber Expertise zur <strong>Koordination autonomer Roboterschw\u00e4rme<\/strong> (B\u00fcttner 2010), zur <strong>Orts- und Lageerkennung<\/strong> (Baumgartl &amp; B\u00fcttner 2020) sowie deren <strong>Angriffsszenarien <\/strong>(Bertram\/Eisentraut\/B\u00fcttner 2025), zur <strong>Erkennung forensisch relevanter Signale<\/strong> (Gohe et al. 2024) und <strong>wichtiger Zeichen in Rettungsszenarien<\/strong> (B\u00fcttner &amp; Baumgartl 2019), zur <strong>Energieoptimierung in Drohnen<\/strong> (Gatscher\/Breitenbach\/B\u00fcttner 2023) und zur <strong>drohnenbasierten Detektion von Objekten<\/strong>, bspw. von <strong>Landminen <\/strong>(Heuschmid et al. 2025). Wir entwickeln und verbessern Systeme f\u00fcr jede Art von Objekterkennung f\u00fcr milit\u00e4rische, aber auch f\u00fcr zivile Anwendungen, bspw. im Pflegeumfeld (Auweiler et al. 2025).<\/p>\n<\/div><\/div>\n\n\n\n<p>Heuschmid\/Wacker\/Zimmermann\/Penava\/Buettner (2025): Advancements in Landmine Detection: Deep Learning-Based Analysis with Thermal Drones. IEEE Access 13:91777-91794. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3572196\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3572196<\/a>.<\/p>\n\n\n\n<p>Bertram\/Eisentraut\/Buettner (2025): A Systematic Literature Review of Current Machine Learning Approaches for Detecting GNSS Spoofing Attacks. IEEE Access 13:108898-108917. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3582435\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3582435<\/a>.<\/p>\n\n\n\n<p>Auweiler\/Mueller\/Puhla\/Penava\/Buettner (2025): A Novel High Performance Object Identification Approach in Care Homes Using Gaussian Preprocessing. IEEE Access 13:79705-79717. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3566223\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3566223<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Autonome Roboter\/Drohnen zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>Gohe\/Kottek\/Buettner\/Penava (2024): Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. PLOS ONE 19(12):e0314533. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0314533\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1371\/journal.pone.0314533<\/a>.<\/p>\n\n\n\n<p>Gatscher\/Breitenbach\/Buettner (2023): Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments. HICSS-56 Proceedings, pp. 6924-6933. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2023.839\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2023.839<\/a>.<\/p>\n\n\n\n<p>Baumgartl\/Buettner (2020): Development of a highly precise place recognition module for effective human-robot interactions in changing lighting and viewpoint conditions. HICSS-53 Proceedings, pp. 563-572. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2020.069\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2020.069<\/a>.<\/p>\n\n\n\n<p>Buettner\/Baumgartl (2019): A highly effective deep learning based escape route recognition module for autonomous robots in crisis and emergency situations. HICSS-52 Proceedings, pp. 659-666. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2019.081\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2019.081<\/a>.<\/p>\n\n\n\n<p>B\u00fcttner (2010): Automatisierte Verhandlungen in Multi-Agenten-Systemen: Entwurf eines argumentationsbasierten Mechanismus f\u00fcr nur imperfekt beschreibbare Verhandlungsgegenst\u00e4nde, Zugl.: Diss. Univ. Hohenheim 2009, Gabler-Verlag, 2010, 287 Seiten,<br><abbr title=\"International Standard Book Number\">ISBN<\/abbr> 978-3-8349-2131-4. <a href=\"https:\/\/doi.org\/10.1007\/978-3-8349-6500-4\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/978-3-8349-6500-4<\/a>.<\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong><strong>Behavioral Analytics<\/strong><\/strong> f\u00fcr pr\u00e4zise Lagebilder:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Palantir Redacted\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/6qCoUg0miOs?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"519\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804-1024x519.jpg\" data-credit=\"Bild von freepik\" alt=\"\" class=\"wp-image-249 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804-1024x519.jpg 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804-300x152.jpg 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804-768x389.jpg 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804-1100x557.jpg 1100w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/portraet-des-hackers-mit-maske_23-2148165891_free-e1725798309804.jpg 1380w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Im Bereich Behavioral Analytics sind wir spezialisiert auf <strong>Social-Media-Daten<\/strong> (B\u00fcttner 2014, 2017d), <strong>digitale Fu\u00dfabdr\u00fccke<\/strong> (B\u00fcttner 2019), <strong>Sprachsignale<\/strong> (B\u00fcttner et al. 2022), <strong>Eye-Tracking und Videodaten<\/strong> zur <strong>Prognose von Nutzerperformanz<\/strong> (B\u00fcttner et al. 2018), <strong>Workload<\/strong> (B\u00fcttner 2017c), <strong>Aufmerksamkeit<\/strong> (Sauer\/B\u00fcttner et al. 2018), <strong>Karrierechancen<\/strong> (B\u00fcttner 2017b), <strong>Kaufabsichten<\/strong> (B\u00fcttner 2017a), <strong>Trends<\/strong> (Contala et al. 2024) und <strong>Stimmungen<\/strong> (Braig et al. 2023), der <strong>Erkennung von Gesten<\/strong> (Hax et al. 2024) und zur <strong>datenschutzkonformen Gesichtserkennung<\/strong> (B\u00fcttner 2018).<\/p>\n<\/div><\/div>\n\n\n\n<p>Hax\/Penava\/Krodel\/Razova\/Buettner (2024): A Novel Hybrid Deep Learning Architecture for Dynamic Hand Gesture Recognition. IEEE Access 12:28761-28774. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2024.3365274\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2024.3365274<\/a>.<\/p>\n\n\n\n<p>Contala\/Gerk\/Hoettler\/Buettner (2024): Topic trends in sustainability disclosure of German DAX 40 companies \u2013 A text mining-based analysis. IEEE Access 12:77300-77335. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2024.3404368\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2024.3404368<\/a>.<\/p>\n\n\n\n<p>Braig\/Benz\/Voth\/Breitenbach\/Buettner (2023): Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data. IEEE Access 11:14778-14803. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3242234\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2023.3242234<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Behavioral Analytics zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>Buettner\/Gross\/Roessler\/Winter\/Sauter\/Baumgartl\/Ulrich (2022): High-Performance Fake Voice Detection on Automatic Speaker Verification Systems for the Prevention of Cyber Fraud with Convolutional Neural Networks. HICSS-55 Proceedings, pp. 6302-6311. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2022.764\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2022.764<\/a>. <em>Best Paper Award<\/em><\/p>\n\n\n\n<p>B\u00fcttner (2019): Online user behavior and digital footprints. Zugl.: Kumulative Habil.-schrift. Univ. Trier 2019. <a href=\"https:\/\/www.researchgate.net\/publication\/342276766_Online_user_behavior_and_digital_footprints\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Buettner (2018): Robust user identification based on facial action units unaffected by users\u2019 emotions. HICSS-51 Proceedings, pp. 265-273. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2018.036\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2018.036<\/a>.<\/p>\n\n\n\n<p>Sauer\/Buettner\/Heidenreich\/Lemke\/Berg\/Kurz (2018): Mindful Machine Learning: Using Machine Learning Algorithms to Predict the Practice of Mindfulness. European Journal of Psychological Assessment 34(1):6-13. <a href=\"https:\/\/doi.org\/10.1027\/1015-5759\/a000312\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1027\/1015-5759\/a000312<\/a>.<\/p>\n\n\n\n<p>Buettner\/Sauer\/Maier\/Eckhardt (2018): Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking. AIS Transactions on Human-Computer Interaction 10(1):26-56. <a href=\"https:\/\/doi.org\/10.17705\/1thci.00103\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.17705\/1thci.00103<\/a>. <em>Best Paper Award<\/em><\/p>\n\n\n\n<p>Buettner (2017a): Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework. Electronic Markets: The International Journal on Networked Business 27(3):247-265. <a href=\"https:\/\/doi.org\/10.1007\/s12525-016-0228-z\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/s12525-016-0228-z<\/a>. <em>Paper of the Year Award<\/em><\/p>\n\n\n\n<p>Buettner (2017b): Getting a job via career-oriented social networking markets: The weakness of too many ties. Electronic Markets: The International Journal on Networked Business 27(4):371-385. <a href=\"https:\/\/doi.org\/10.1007\/s12525-017-0248-3\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/s12525-017-0248-3<\/a>.<\/p>\n\n\n\n<p>Buettner (2017c): Asking both the User\u2019s Brain and its Owner using Subjective and Objective Psychophysiological NeuroIS Instruments. ICIS 2017 Proceedings: 38th International Conference on Information Systems (ICIS), December 10-13, 2017, Seoul, South Korea. <a href=\"https:\/\/aisel.aisnet.org\/cgi\/viewcontent.cgi?article=1181&amp;context=icis2017\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>B\u00fcttner (2017d): Pr\u00e4diktive Algorithmen zur Pers\u00f6nlichkeitsprognose auf Basis von Social-Media-Daten. PERSONALquarterly 3:22-27, 2017. <a href=\"https:\/\/www.haufe.de\/download\/personalquarterly-32017-people-analytics-personalquarterly-420922.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Buettner (2014): A Framework for Recommender Systems in Online Social Network Recruiting: An Interdisciplinary Call to Arms. HICSS-47 Proceedings, pp. 1415-1424. <a href=\"https:\/\/doi.org\/10.1109\/HICSS.2014.184\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HICSS.2014.184<\/a>.<\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Infrastructure Security<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Spot on Site: Construction Solution\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/0NYJ_9FIHZA?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"730\" height=\"541\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/SUT-Crack-images.png\" data-credit=\"CC BY-NC-ND 4.0, https:\/\/doi.org\/10.1016\/j.dib.2023.109642\" alt=\"\" class=\"wp-image-580 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/SUT-Crack-images.png 730w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/SUT-Crack-images-300x222.png 300w\" sizes=\"auto, (max-width: 730px) 100vw, 730px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Zur Sicherung der Infrastruktur bei <strong>Hochbelastungsszenarien<\/strong> entwickeln wir robuste KI-Architekturen zur <strong>Infrastruktur\u00fcberwachung<\/strong>. Beispielsweise lassen sich <strong>Stahlbetonbr\u00fccken<\/strong> in Echtzeit mittels Ultraschallsensorik und Video-\/Bilddaten \u00fcberwachen (B\u00fcttner\/Bertram\/Fischer-Brandies 2025). Damit lassen sich <strong>feine Risse<\/strong>, <strong>Abplatzungen <\/strong>und entstehende <strong>Hohlr\u00e4ume <\/strong>fr\u00fch erkennen. Diese KI-Architekturen sind so entwickelt, dass sie bei <strong>unterschiedlichen Lichtverh\u00e4ltnissen<\/strong>, <strong>Verschmutzungen<\/strong> und <strong>Materialien<\/strong> <strong>robust<\/strong> funktionieren.<\/p>\n\n\n\n<p>Weitere Informationen zum <strong>Schutz kritischer Infrastruktur (KRITIS)<\/strong> erhalten Sie auf den <a href=\"https:\/\/www.bbk.bund.de\/DE\/Themen\/Kritische-Infrastrukturen\/KRITIS-Gefahrenlagen\/kritis-gefahrenlagen_node.html\" target=\"_blank\" rel=\"noreferrer noopener\">zugeh\u00f6rigen Informationsseiten<\/a> des <a href=\"https:\/\/www.bbk.bund.de\/DE\/Themen\/Kritische-Infrastrukturen\/KRITIS-Gefahrenlagen\/kritis-gefahrenlagen_node.html\" target=\"_blank\" rel=\"noreferrer noopener\">Bundesamts f\u00fcr Bev\u00f6lkerungsschutz und Katastrophenhilfe<\/a> und des <a href=\"https:\/\/www.bsi.bund.de\/DE\/Themen\/Regulierte-Wirtschaft\/Kritische-Infrastrukturen\/kritis_node.html\" target=\"_blank\" rel=\"noreferrer noopener\">Bundesamts f\u00fcr Sicherheit in der Informationstechnik<\/a>.<\/p>\n<\/div><\/div>\n\n\n\n<p>Buettner\/Bertram\/Fischer-Brandies (2025): The Impact of Combining Datasets on the Robustness of Deep Learning Architectures: A Cross-Dataset Analysis. IEEE Access 13:151993-152009, 2025. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3604689\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3604689<\/a>.<\/p>\n\n\n\n<p>Fischer-Brandies\/Bertram\/Mai\/Buettner (2025): AsphaltCrackNet: A Novel Architecture for Classifying Cracks in Asphalt Pavement. IEEE Access, 13:177160-177174, 2025. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3620212\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3620212<\/a>.<\/p>\n\n\n\n<p>Pagers\/Penava\/Buettner (2025): A High-Performance Deep-Learning-based Ground Penetrating Radar Classification Approach with Special Focus on Multi-Class Robustness. IEEE Access, 13:175847-175857, 2025. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3619159\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3619159<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-default\" \/>\n\n\n\n<p><strong>Cyber Security<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Why Data Centers Need a New Security Approach | Hybrid Mesh Firewall\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/Bl7BnKgOtCc?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"583\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-1024x583.jpg\" data-credit=\"Getty Images\" alt=\"\" class=\"wp-image-633 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-1024x583.jpg 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-300x171.jpg 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-768x437.jpg 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-1536x874.jpg 1536w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-2048x1165.jpg 2048w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2185390906-1100x626.jpg 1100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Die <strong>Abwehr <a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" target=\"_blank\" rel=\"noreferrer noopener\">hybrider Angriffe<\/a><\/strong> erfordert organisatorische und technologische Schutzma\u00dfnahmen. Hier analysieren wir <strong>Bedrohungen<\/strong> f\u00fcr Unternehmen (Ulrich\/Frank\/B\u00fcttner 2021) und entwickeln <strong>KI-basierte Schutztechnologien<\/strong> (B\u00fcttner et al. 2021, 2022), bspw. zur <strong>Detektion gef\u00e4lschter Stimmen<\/strong> (B\u00fcttner et al. 2022) oder von <strong>GNSS-Spoofing-Angriffen<\/strong> (Bertram\/Eisentraut\/B\u00fcttner 2025).<\/p>\n\n\n\n<p>Weitere Informationen zu <strong><a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" target=\"_blank\" rel=\"noreferrer noopener\">hybriden Bedrohungen<\/a><\/strong> erhalten Sie auf den <a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" target=\"_blank\" rel=\"noreferrer noopener\">zugeh\u00f6rigen Informationsseiten<\/a> des <a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" target=\"_blank\" rel=\"noreferrer noopener\">Bundesministeriums der Verteidigung<\/a> und des <a href=\"https:\/\/www.bnd.bund.de\/DE\/Die_Themen\/hybride-bedrohungen\/hybride-bedrohungen-node.html\" target=\"_blank\" rel=\"noreferrer noopener\">BND<\/a>.<\/p>\n<\/div><\/div>\n\n\n\n<p>Bertram\/Eisentraut\/Buettner (2025): A Systematic Literature Review of Current Machine Learning Approaches for Detecting GNSS Spoofing Attacks. IEEE Access 13:108898-108917. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3582435\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3582435<\/a>.<\/p>\n\n\n\n<p>Buettner\/Gross\/Roessler\/Winter\/Sauter\/Baumgartl\/Ulrich (2022): High-Performance Fake Voice Detection on Automatic Speaker Verification Systems for the Prevention of Cyber Fraud with Convolutional Neural Networks. HICSS-55 Proceedings, pp. 6302-6311. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2022.764\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2022.764<\/a>. <em>Best Paper Award<\/em><\/p>\n\n\n\n<p>Buettner\/Sauter\/Klopfer\/Breitenbach\/Baumgartl (2021): A review of recent advances in machine learning approaches for cyber defense. IEEE BigData 2021 Proceedings, 5th International Workshop on Big Data Analytics for Cyber Intelligence and Defense, pp. 3969-3974. <a href=\"https:\/\/doi.org\/10.1109\/BigData52589.2021.9671918\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/BigData52589.2021.9671918<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Cyber Security zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>Ulrich\/Frank\/Buettner (2021): One Single Click is enough \u2013 an Empirical Study on Human Threats in Family Firm Cyber Security. HICSS-54 Proceedings, pp. 4548-4556. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2021.551\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2021.551<\/a>.<\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Industrial Data Science:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"What&#039;s Next In Industrial AI?\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/RLZk-Fr7wz4?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"499\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-1024x499.png\" data-credit=\"Breitenbach\/Eckert\/Mahal\/Baumgartl\/Buettner (2022): Automated Defect Detection of Screws in the Manufacturing Industry using Convolutional Neural Networks. HICSS-55 Proceedings, pp. 1226-1235.\" alt=\"\" class=\"wp-image-213 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-1024x499.png 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-300x146.png 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-768x374.png 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-1536x748.png 1536w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-2048x997.png 2048w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Industrial_Screw_kompr-1100x536.png 1100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Im Bereich <strong>Industrial Data Science<\/strong> entwickeln wir Deep-Learning-Architekturen und Bildverarbeitungs-Algorithmen f\u00fcr die <strong>KI-basierte Qualit\u00e4tskontrolle<\/strong> in der <strong>Produktion<\/strong>. Beispielsweise haben wir zahlreiche Projekte zur <strong>lichtkamerabasierten Kontrolle<\/strong> von <strong>Stoffen <\/strong>wie <strong>Leder <\/strong>(Mai\/Penava\/B\u00fcttner 2024) oder <strong>Baumwolle <\/strong>(Wiedemann\/Penava\/Mai\/B\u00fcttner 2025), <strong>Verpackungen<\/strong>, <strong>Schwei\u00dfn\u00e4hten<\/strong> (Breitenbach et al. 2021) und <strong>L\u00f6tstellen<\/strong> (Eisentraut et al. 2025) durchgef\u00fchrt. Zudem sind wir in einem breiten Bilddatenformatspektrum, bspw. zur <strong>computertomografiebasierten Qualit\u00e4tspr\u00fcfung<\/strong> von <strong>Flugturbinen<\/strong>, <strong>ultraschallbasierten Kontrolle<\/strong> von <strong>Halbleiterplatten<\/strong> und zur <strong>w\u00e4rmebildkamerabasierten Kontrolle<\/strong> von <strong>Metall-3D-Druck-Produkten<\/strong> (Baumgartl et al. 2020) international ausgewiesen.<\/p>\n<\/div><\/div>\n\n\n\n<p>Wiedemann\/Penava\/Mai\/Buettner (2025): Deep-Learning-based Determination of Textile Properties: A Novel Triplet Architecture Approach for Classifying Cotton Content. IEEE Access 13:164395-164408, 2025. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3610920\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3610920<\/a>.<\/p>\n\n\n\n<p>Eisentraut\/Hosch\/Roytenberg\/Benecke\/Penava\/Buettner (2025): Defect Detection in Industrial Soldering Processes Using Machine Learning: A Critical Literature Review. IEEE Access 13:41533-41558. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3547847\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3547847<\/a>.<\/p>\n\n\n\n<p>Mai\/Penava\/Buettner (2024): A novel deep learning-based approach for defect detection of synthetic leather using Gaussian filtering. IEEE Access 12:196702-196714. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2024.3521497\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2024.3521497<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Industrial Data Science zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>Breitenbach\/Dauser\/Illenberger\/Traub\/Buettner (2021): A Systematic Literature Review on Machine Learning Approaches for Quality Monitoring and Control Systems for Welding Processes. IEEE BigData 2021 Proceedings, pp. 2019-2025. <a href=\"https:\/\/doi.org\/10.1109\/BigData52589.2021.9671887\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/BigData52589.2021.9671887<\/a>.<\/p>\n\n\n\n<p>Baumgartl\/Tomas\/Buettner\/Merkel (2020): A deep learning based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Progress in Additive Manufacturing 5:277-285. <a href=\"https:\/\/doi.org\/10.1007\/s40964-019-00108-3\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/s40964-019-00108-3<\/a>.<\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-default\" \/>\n\n\n\n<p><strong>Medical Data Science I (KI-basierte Biosignalanalyse):<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"AI for Health Program\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/ii-FfE-7C-k?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"540\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-1024x540.jpg\" data-credit=\"Getty Images\" alt=\"\" class=\"wp-image-505 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-1024x540.jpg 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-300x158.jpg 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-768x405.jpg 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-1536x810.jpg 1536w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-2048x1080.jpg 2048w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-1221400480-1100x580.jpg 1100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Der Lehrstuhl verf\u00fcgt \u00fcber umfangreiche Expertise zur <strong>EEG<\/strong>-basierten Auswertung von <strong>Pers\u00f6nlichkeitsmerkmalen<\/strong> (Penava &amp; B\u00fcttner 2025; Rieck\/Penava\/B\u00fcttner 2025), <strong>Pers\u00f6nlichkeitsst\u00f6rungen<\/strong> (Baumgartl et al. 2020), <strong>Diagnose von<\/strong> <strong>Depression<\/strong> (Penava &amp; B\u00fcttner 2024), <strong>Alkoholismus<\/strong> (Flathau et al. 2021; Rieg et al. 2019), <strong>Schizophrenie<\/strong> (B\u00fcttner et al. 2019, 2020; Frick\/Rieg\/B\u00fcttner 2021; Baumgartl et a. 2021), <strong>Epilepsiepr\u00e4valenz<\/strong> (B\u00fcttner\/Frick\/Rieg 2019; Rieg\/Frick\/B\u00fcttner 2020), <strong>stoffungebundenen S\u00fcchte<\/strong> (Gro\u00df\/Baumgartl\/B\u00fcttner 2020), <strong>Entwicklungsst\u00f6rungen<\/strong> (Breitenbach et al. 2021; Gro\u00df et al. 2021; B\u00fcttner et al. 2021), <strong>Angstst\u00f6rungen<\/strong> (Gro\u00df et al. 2021), <strong>Schlafst\u00f6rungen<\/strong> (B\u00fcttner\/Grimmeisen\/Gotschlich 2020; Breitenbach\/Baumgartl\/B\u00fcttner 2020; B\u00fcttner\/Fuhrmann\/Kolb 2019), <strong>Stress<\/strong> (Baumgartl\/Fezer\/B\u00fcttner 2020) und <strong>Essst\u00f6rungen<\/strong> (Raab\/Baumgartl\/B\u00fcttner 2020).<\/p>\n<\/div><\/div>\n\n\n\n<p>Zudem nutzen wir weitere Biosignale wie&nbsp;<strong>EKG<\/strong>&nbsp;zur machine-learning-basierten robusten Erkennung von&nbsp;<strong>Herzrhythmusst\u00f6rungen<\/strong>&nbsp;(Rieg et al. 2020)&nbsp;<strong>und -krankheiten<\/strong>&nbsp;(B\u00fcttner &amp; Schunter 2019).<\/p>\n\n\n\n<p>Penava &amp; Buettner (2025): A novel subject-independent deep learning approach for user behavior prediction in electronic markets based on electroencephalographic data. Electronic Markets 35, article no. 37. <a href=\"https:\/\/doi.org\/10.1007\/s12525-025-00778-8\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/s12525-025-00778-8<\/a>.<\/p>\n\n\n\n<p>Rieck\/Penava\/Buettner (2025): A Systematic Literature Review of Machine Learning-based Personality Trait Detection using Electroencephalographic Data. IEEE Access 13:114812-114833. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3586005\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3586005<\/a>.<\/p>\n\n\n\n<p>Penava &amp; Buettner (2024): Early-Stage non-severe Depression Detection using a novel Convolutional Neural Network Approach based on resting-state EEG data. IEEE Access 12:173380-173389. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2024.3502540\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2024.3502540<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Medical Data Science I (KI-basierte Biosignalanalyse) zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>Flathau\/Breitenbach\/Baumgartl\/Buettner (2021): Early Detection of Alcohol Use Disorder Based on a Novel Machine Learning Approach Using EEG Data. IEEE BigData 2021 Proceedings, pp. 3897-3904. <a href=\"https:\/\/doi.org\/10.1109\/BigData52589.2021.9671448\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/BigData52589.2021.9671448<\/a>.<\/p>\n\n\n\n<p>Baumgartl\/Scholz\/Sauter\/Buettner (2021): Detection of Schizophrenia Using Machine Learning on the Five Most Predictive EEG-Channels. PACIS 2021 Proceedings, article no. 38. <a href=\"https:\/\/aisel.aisnet.org\/pacis2021\/38\/\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Frick\/Rieg\/Buettner (2021): Detection of schizophrenia: A machine learning algorithm for potential early detection and prevention based on event-related potentials. HICSS-54 Proceedings, pp. 3794-3803. <a href=\"https:\/\/aisel.aisnet.org\/hicss-54\/hc\/wellness_management\/4\/\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Rieg\/Frick\/Baumgartl\/Buettner (2020): Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLOS ONE 15(12):e0243615. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0243615\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1371\/journal.pone.0243615<\/a>.<\/p>\n\n\n\n<p>Buettner\/Beil\/Scholtz\/Djemai (2020): Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings. HICSS-53 Proceedings, pp. 3216-3225. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2020.393\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2020.393<\/a>.<\/p>\n\n\n\n<p>Baumgartl\/Dikici\/Sauter\/Buettner (2020): Detecting Antisocial Personality Disorder Using a Novel Machine Learning Algorithm Based on Electroencephalographic Data. PACIS 2020 Proceedings, paper no. 48. <a href=\"https:\/\/aisel.aisnet.org\/pacis2020\/48\/\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Buettner &amp; Schunter (2019): Efficient machine learning based detection of heart disease. IEEE Healthcom Proceedings, pp. 1-6. <a href=\"https:\/\/doi.org\/10.1109\/HealthCom46333.2019.9009429\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HealthCom46333.2019.9009429<\/a>.<\/p>\n\n\n\n<p>Buettner\/Hirschmiller\/Schlosser\/Roessle\/Fernandes\/Timm (2019): High-performance exclusion of schizophrenia using a novel machine learning method on EEG data. IEEE Healthcom Proceedings, pp. 1-6. <a href=\"https:\/\/doi.org\/10.1109\/HealthCom46333.2019.9009437\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HealthCom46333.2019.9009437<\/a>.<\/p>\n\n\n\n<p>Rieg\/Frick\/Hitzler\/Buettner (2019): High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method. HICSS-52 Proceedings, pp. 3769-3777.&nbsp;<a href=\"https:\/\/doi.org\/10.24251\/HICSS.2019.455\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2019.455<\/a>.&nbsp;<em>Best Paper Award<\/em><\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Medical Data Science II (KI-basierte Bildgebung):<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"You Contain Multitudes: The future of Health &amp; AI | Google for Health\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/vekmDxgPey8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"452\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-1024x452.png\" data-credit=\"Gross\/Breitenbach\/Baumgartl\/Buettner (2021): High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks. HICSS-54 Proceedings, pp. 3416-3425.\" alt=\"\" class=\"wp-image-210 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-1024x452.png 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-300x132.png 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-768x339.png 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-1536x678.png 1536w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-2048x904.png 2048w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2024\/09\/210922_Medical_Corneal_Ulcer_1_kompr-1100x486.png 1100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Hier entwickeln wir hochpr\u00e4zise und robuste KI-Architekturen zur automatisierten Auswertung bildgebender Systeme, bspw. <strong>ophthalmologischer Aufnahmen<\/strong>&nbsp;zur Detektion von&nbsp;<strong>Augenkrankheiten<\/strong>&nbsp;(Gro\u00df et al. 2021; Rieck et al. 2025a, 2025b) oder zur Diagnose von&nbsp;<strong>Tuberkulose<\/strong>&nbsp;in <strong>R\u00f6ntgen\/CT-Aufnahmen<\/strong>&nbsp;(Eisentraut et al. 2025).<\/p>\n<\/div><\/div>\n\n\n\n<p>Eisentraut\/Mai\/Hosch\/Benecke\/Penava\/Buettner (2025): Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens. IEEE Access 13:36065-36081. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3544923\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3544923<\/a>.<\/p>\n\n\n\n<p>Rieck\/Mai\/Eisentraut\/Buettner (2025a): A Novel Transformer-CNN Hybrid Deep Learning Architecture for Robust Broad-Coverage Diagnosis of Eye Diseases on Color Fundus Images. IEEE Access 13:156285-156300. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3606334\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3606334<\/a>.<\/p>\n\n\n\n<p>Gross\/Breitenbach\/Baumgartl\/Buettner (2021): High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks. HICSS-54 Proceedings, pp. 3416-3425. <a href=\"https:\/\/doi.org\/10.24251\/HICSS.2021.415\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.24251\/HICSS.2021.415<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich Medical Data Science II (KI-basierte Bildgebung) zugeh\u00f6rige Ver\u00f6ffentlichungen <\/summary>\n<p><\/p>\n\n\n\n<p>Rieck\/Eisentraut\/Buettner (2025b): Broad-Spectrum Eye Disease Classification Using a Deep Learning-based Tailored Software Lens. PLOS ONE, 20(11):e0335419, 2025. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0335419\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1371\/journal.pone.0335419<\/a>.<\/p>\n<\/details>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Materials Data Science<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"NVIDIA ALCHEMI: AI for Chemistry and Materials Science\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/O-2TLhaTcuw?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"542\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom-1024x542.png\" data-credit=\"S\u00f6rgel\/Buettner\/...\/Bund (2021). Journal of Electrochemistry and Plating Technology 14(1):2-11.\" alt=\"\" class=\"wp-image-501 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom-1024x542.png 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom-300x159.png 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom-768x407.png 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom-1100x582.png 1100w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/digichrom.png 1256w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Im Bereich Materials Data Science entwickeln wir KI-basierte Verfahren, um nichtlineare physikalische und elektrochemische Zusammenh\u00e4nge sichtbar zu machen. Diese Verfahren setzen wir bspw. zur Ableitung von <strong>Massendichtemodellen<\/strong> f\u00fcr <strong>Hartmagneten<\/strong> (Kini et al. 2023) und zur Analyse der nichtlinearen Eigenschafts-Prozess-Zusammenh\u00e4nge in <strong>Galvanisierungsprozessen<\/strong> (S\u00f6rgel\/B\u00fcttner et al. 2021) ein.<\/p>\n<\/div><\/div>\n\n\n\n<p>Beispielsweise entwickeln wir hier im BMFTR-Verbundprojekt mit der <abbr title=\"Technische Universit\u00e4t\">TU<\/abbr> Ilmenau, der Hochschule Aalen, der Hochschule Offenburg, dem Fraunhofer Institut f\u00fcr Produktionstechnik und Automatisierung IPA sowie den Unternehmen Hansgrohe SE, DiTEC <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr>, PlanB. <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr>, Atotech Deutschland <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr> &amp; Co. <abbr title=\"Kommanditgesellschaft\">KG<\/abbr>, Betz-Chrom <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr>, IPT International Plating Technologies <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr> digitale Werkzeuge zur Verbesserung galvanischer Schichten am Beispiel Chrom (III)-basierter Prozesse (Kurzbezeichnung: DigiChrom). <a href=\"https:\/\/www.materialdigital.de\/project\/25\" target=\"_blank\" rel=\"noreferrer noopener\">DigiChrom<\/a> ist Bestandteil der Initiative der <a href=\"https:\/\/www.materialdigital.de\" target=\"_blank\" rel=\"noreferrer noopener\">Plattform MaterialDigital<\/a>.<\/p>\n\n\n\n<p>Kini\/Choudhary\/Hohs\/Jansche\/Baumgartl\/Buettner\/Bernthaler\/Goll\/Schneider (2023): Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features. Chemical Physics Letters 811:140231. <a href=\"https:\/\/doi.org\/10.1016\/j.cplett.2022.140231\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1016\/j.cplett.2022.140231<\/a>.<\/p>\n\n\n\n<p>S\u00f6rgel\/Buettner\/Baumgartl\/Seifert\/Metzner\/Feige\/Ispas\/Endikrat\/Leimbach\/Bund (2021): The need for digitalisation in electroplating &#8211;<br>How digital approaches can help to optimize the electrodeposition of chromium from trivalent electrolytes. Journal of Electrochemistry and Plating Technology 14(1):2-11. <a href=\"https:\/\/doi.org\/10.12850\/ISSN2196-0267.JEPT7147\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.12850\/ISSN2196-0267.JEPT7147<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Lebensmittelindustrie:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"NVIDIA AI Solutions for Efficient Supply Chain Operation\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/he5I6ByoaB4?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2172032104-1024x683.jpg\" data-credit=\"Getty Images\" alt=\"\" class=\"wp-image-669 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2172032104-1024x683.jpg 1024w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2172032104-300x200.jpg 300w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2172032104-768x512.jpg 768w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/09\/iStock-2172032104-1100x733.jpg 1100w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Hier untersuchen wir den <strong>Umsetzungsgrad<\/strong> und die <strong>Potentiale<\/strong> des Einsatzes von <strong>KI-Technologien<\/strong> in der Lebensmittelindustrie (Gradl\/Reis\/B\u00fcttner 2025). Ein besonderer Schwerpunkt unserer Arbeiten bezieht sich auf die Entwicklung von <strong>performanten Deep-Learning-Architekturen<\/strong>, die \u00fcber <strong>unterschiedliche Fruchtarten<\/strong> hinweg <strong>robust<\/strong> den <strong>Reifegrad<\/strong> von Fr\u00fcchten erkennen (Fischer-Brandies et al. 2025). Andere Arbeiten fokussieren spezielle Fr\u00fcchte, bspw. zur Bestimmung des Reifegrads von Cashew-\u00c4pfeln (Winklmair et al. 2025).<\/p>\n<\/div><\/div>\n\n\n\n<p>Fischer-Brandies\/M\u00fcller\/Riegger\/Buettner (2025): Fresh or Rotten? Enhancing Rotten Fruit Detection with Deep Learning and Gaussian Filtering. IEEE Access 13:31857-31869. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3542612\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3542612<\/a>.<\/p>\n\n\n\n<p>Gradl\/Reis\/Buettner (2025): Industrial Maturity of Machine Learning Solutions within the Food Industry. IEEE Access 13:62831-62855. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3558091\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3558091<\/a>.<\/p>\n\n\n\n<p>Winklmair\/Sekulic\/Kraus\/Penava\/Buettner (2025): A deep learning based approach for classifying the maturity of cashew apples. PLOS ONE 20(6):e0326103. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0326103\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1371\/journal.pone.0326103<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>KI im Personalwesen<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"AI in HR Use Cases: 7 Ways AI is Transforming Human Resources\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/NJnq0A3G4H8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-media-text is-stacked-on-mobile\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"377\" src=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/10\/E-Recruiting_Framework-e1760782887116.jpg\" data-credit=\"B\u00fcttner (2017): PERSONALquarterly 3:22-27.\" alt=\"\" class=\"wp-image-767 size-full\" srcset=\"https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/10\/E-Recruiting_Framework-e1760782887116.jpg 723w, https:\/\/www.hsu-hh.de\/ai\/wp-content\/uploads\/sites\/922\/2025\/10\/E-Recruiting_Framework-e1760782887116-300x156.jpg 300w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p>Hier decken wir die gesamte Prozesskette ab. Angefangen von <strong>passgenauer Personalgewinnung und Eignungsfeststellung<\/strong> (<abbr title=\"unter anderem\">u.a.<\/abbr> B\u00fcttner 2014, 2016) \u00fcber <strong>automatisierte Vertragsprozesse<\/strong> (<abbr title=\"unter anderem\">u.a.<\/abbr> B\u00fcttner 2013), <strong>HR Analytics<\/strong> (<abbr title=\"unter anderem\">u.a.<\/abbr> B\u00fcttner 2015a, 2015b, 2017b) und <strong>Controlling<\/strong> zur Einsch\u00e4tzung von Personallage und -planung, bis zur <strong>sprachmodellbasierten Auswertung von Abgangsinterviews<\/strong> (<abbr title=\"unter anderem\">u.a.<\/abbr> M\u00f6nks\/Penava\/B\u00fcttner 2025) haben wir zahlreiche BMFTR-Forschungs- und Anwendungsprojekte mit Wirtschaftsunternehmen wie Airbus, Philips und Deutsche Bahn durchgef\u00fchrt.<\/p>\n<\/div><\/div>\n\n\n\n<p>In allen aktuellen Projekten wird <strong>KI<\/strong> eingesetzt, <abbr title=\"insbesondere\">insb.<\/abbr> f\u00fcr <strong>Talent Intelligence<\/strong>, <strong>Intelligent Training<\/strong>, <strong>Performance Management<\/strong>, <strong>Retention Management<\/strong>, <strong>Organizational Culture Mining<\/strong> und <strong>Workplace safety<\/strong>. <strong>Generative KI<\/strong> wird <abbr title=\"insbesondere\">insb.<\/abbr> f\u00fcr <strong>Personalmarketing und Employer Branding<\/strong>, <strong>Employee Self Services<\/strong>, <strong>Chatbots im Onboarding<\/strong>, <strong>KI-basierter Generierung und Optimierung von Stellenanzeigen<\/strong> sowie f\u00fcr <strong>KI Interviewing Tools und Chatbots<\/strong> eingesetzt.<\/p>\n\n\n\n<p>Moenks\/Penava\/Buettner (2025): A Systematic Literature Review of Large Language Model Applications in Industry. IEEE Access, 13:160010-160033, 2025. <a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3608650\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/ACCESS.2025.3608650<\/a>.<\/p>\n\n\n\n<p>Buettner (2017a): Getting a job via career-oriented social networking markets: The weakness of too many ties. Electronic Markets: The International Journal on Networked Business 27(4):371-385. <a href=\"https:\/\/doi.org\/10.1007\/s12525-017-0248-3\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1007\/s12525-017-0248-3<\/a>.<\/p>\n\n\n\n<p>B\u00fcttner (2016): Abschlussbericht zum <abbr title=\"Bundesministerium f\u00fcr Bildung und Forschung\">BMBF<\/abbr> Forschungsprojekt &#8222;Effizientes Recruiting von Fachkr\u00e4ften im Web 2.0 (EfficientRecruiting 2.0): Hochautomatisierte Identifikation und Rekrutierung von Fachkr\u00e4ften durch Analyse internetbasierter sozialer Netzwerke mittels intelligenter Softwareagenten&#8220;. <a href=\"https:\/\/doi.org\/10.13140\/RG.2.2.13529.31840\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.13140\/RG.2.2.13529.31840<\/a>.<\/p>\n\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Weitere zum Forschungsbereich KI im Personalwesen zugeh\u00f6rige Ver\u00f6ffentlichungen<\/summary>\n<p><\/p>\n\n\n\n<p>B\u00fcttner (2017b): Pr\u00e4diktive Algorithmen zur Pers\u00f6nlichkeitsprognose auf Basis von Social-Media-Daten. PERSONALquarterly 3:22-27, 2017. <a href=\"https:\/\/www.haufe.de\/download\/personalquarterly-32017-people-analytics-personalquarterly-420922.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Weckmueller\/B\u00fcttner (2017): Big Data in der Personalauswahl. Personalmagazin, 3:26-28, 2017. <a href=\"https:\/\/www.researchgate.net\/profile\/Ricardo-Buettner\/publication\/314235391_Big_Data_in_der_Personalauswahl\/links\/58bc38f692851c471d56372b\/Big-Data-in-der-Personalauswahl.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n\n\n\n<p>Buettner (2015a): Analyzing the Problem of Employee Internal Social Network Site Avoidance: Are Users Resistant due to their Privacy Concerns? In: HICSS-48 Proceedings, pp. 1819-1828. <a href=\"https:\/\/doi.org\/10.1109\/HICSS.2015.220\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HICSS.2015.220<\/a>.<\/p>\n\n\n\n<p>Buettner (2015b): A Systematic Literature Review of Crowdsourcing Research from a Human Resource Management Perspective. In: HICSS-48 Proceedings, pp. 4609-4618. <a href=\"https:\/\/doi.org\/10.1109\/HICSS.2015.549\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HICSS.2015.549<\/a>.<\/p>\n\n\n\n<p>Buettner (2014): A Framework for Recommender Systems in Online Social Network Recruiting: An Interdisciplinary Call to Arms. HICSS-47 Proceedings, pp. 1415-1424. <a href=\"https:\/\/doi.org\/10.1109\/HICSS.2014.184\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1109\/HICSS.2014.184<\/a>.<\/p>\n\n\n\n<p>B\u00fcttner (2013): Abschlussbericht zum <abbr title=\"Bundesministerium f\u00fcr Bildung und Forschung\">BMBF<\/abbr> Forschungsprojekt &#8222;Entwicklung einer elektronischen Marktplattform&nbsp;f\u00fcr Zeitarbeitskr\u00e4fte zur F\u00f6rderung von Besch\u00e4ftigung und Wertsch\u00f6pfung (eMarkt&nbsp;Zeitarbeit)&#8220;. <a href=\"https:\/\/edocs.tib.eu\/files\/e01fb14\/807120421.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Link<\/a>.<\/p>\n<\/details>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Der Lehrstuhl deckt ein breites Methoden-, Dom\u00e4nen- und Datenformatspektrum ab und verf\u00fcgt \u00fcber umfangreiche Expertise bei der Entwicklung innovativer maschineller Lernverfahren, insbesondere moderner Deep-Learning-Architekturen sowie deren Anwendung in Wirtschaft, Verwaltung [&hellip;]<\/p>\n","protected":false},"author":26,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-112","page","type-page","status-publish","hentry","category-forschung"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/112","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/comments?post=112"}],"version-history":[{"count":275,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/112\/revisions"}],"predecessor-version":[{"id":922,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/112\/revisions\/922"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/media?parent=112"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/categories?post=112"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/tags?post=112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}