{"id":114,"date":"2018-01-17T14:06:51","date_gmt":"2018-01-17T13:06:51","guid":{"rendered":"https:\/\/www.hsu-hh.de\/ai\/?page_id=114"},"modified":"2025-11-17T17:49:25","modified_gmt":"2025-11-17T16:49:25","slug":"research","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/ai\/en\/research\/","title":{"rendered":"Research &amp; Innovations"},"content":{"rendered":"\n<p>The chair covers a wide range of methods, domains, and data formats. It has extensive expertise in developing innovative machine learning approaches, particularly modern deep learning architectures, and their application in business, public administration, and defense:<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong><strong>Autonomous Robots\/Drones:<\/strong><\/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>In the field of autonomous robots\/drones the chair has expertise in the <strong>coordination of autonomous robot swarms<\/strong> (B\u00fcttner 2010), <strong>localization and position recognition<\/strong> (Baumgartl &amp; B\u00fcttner 2020), as well as in <strong>attack scenarios<\/strong> (Bertram\/Eisentraut\/B\u00fcttner 2025), and the <strong>detection of forensically relevant signals<\/strong> (Gohe et al. 2024) and <strong>important markers in rescue scenarios<\/strong> (B\u00fcttner &amp; Baumgartl 2019), also in <strong>optimizing the energy efficiency of drones<\/strong> (Gatscher\/Breitenbach\/B\u00fcttner 2023) and <strong>drone-based object detection<\/strong>, for example of <strong>landmines<\/strong> (Heuschmid et al. 2025). We design and improve systems for all kinds of object detection for military as well as civil applications, such as care environments (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>More publications related to Autonomous Robots\/Drones<\/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>Behavioral analytics<\/strong> for accurate situational awareness:<\/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>In the field of behavioral analytics, we are specialized in <strong>social media data<\/strong> (B\u00fcttner 2014, 2017d), <strong>digital footprints<\/strong> (B\u00fcttner 2019), <strong>speech signals<\/strong> (B\u00fcttner et al. 2022), <strong>eye tracking and<\/strong> <strong>video data <\/strong>for<strong>predicting user performance<\/strong> (B\u00fcttner et al. 2018), <strong>workload<\/strong> (B\u00fcttner 2017c), <strong>attention<\/strong> (Sauer\/B\u00fcttner et al. 2018), <strong>career opportunities<\/strong> (B\u00fcttner 2017b), <strong>purchase intentions<\/strong> (B\u00fcttner 2017a), <strong>trends<\/strong> (Contala et al. 2024), and <strong>sentiments<\/strong> (Braig et al. 2023), as well as <strong>gesture recognition<\/strong> (Hax et al. 2024) and <strong>data protection compliant face recognition<\/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>More publications related to Behavioral Analytics<\/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<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>To safeguard infrastructure under <strong>high-load scenarios,<\/strong> we are developing robust AI architectures for <strong>infrastructure monitoring<\/strong>. For instance, <strong>reinforced concrete bridges<\/strong> can be monitored in real-time using ultrasonic sensors and video\/image data (B\u00fcttner\/Bertram\/Fischer-Brandies, 2025). This enables the early detection of <strong>fine cracks<\/strong>, <strong>spalling,<\/strong> and emerging <strong>cavities<\/strong>. These AI architectures are designed to function <strong>robustly<\/strong> under <strong>varying lighting conditions<\/strong>, <strong>contamination<\/strong>, and <strong>material properties<\/strong>.<\/p>\n\n\n\n<p>Further information on protection of <strong>critical infrastructure (KRITIS)<\/strong> can be found on the <a href=\"https:\/\/www.bbk.bund.de\/DE\/Themen\/Kritische-Infrastrukturen\/KRITIS-Gefahrenlagen\/kritis-gefahrenlagen_node.html\" data-type=\"link\" data-id=\"https:\/\/www.bbk.bund.de\/DE\/Themen\/Kritische-Infrastrukturen\/KRITIS-Gefahrenlagen\/kritis-gefahrenlagen_node.html\" rel='nofollow'>information pages<\/a> of the <a href=\"https:\/\/www.bbk.bund.de\/DE\/Themen\/Kritische-Infrastrukturen\/KRITIS-Gefahrenlagen\/kritis-gefahrenlagen_node.html\" rel='nofollow'>Federal Office of Civil Protection and Disaster Assistance<\/a> and the <a href=\"https:\/\/www.bsi.bund.de\/EN\/Themen\/Regulierte-Wirtschaft\/Kritische-Infrastrukturen\/kritis_node.html\" rel='nofollow'>Federal Office for Information Security<\/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><strong>Countering <\/strong><a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" rel='nofollow'>hybrid attacks<\/a> requires organizational and technological protective measures. We analyze corporate<strong> threats<\/strong> (Ulrich\/Frank\/B\u00fcttner 2021) and develop <strong>AI-based defense technologies <\/strong>(B\u00fcttner et al. 2021, 2022), e.g. for <strong>detecting fake voices<\/strong> (B\u00fcttner et al. 2022) or <strong>GNSS spoofing attacks<\/strong> (Bertram\/Eisentraut\/B\u00fcttner 2025).<\/p>\n\n\n\n<p>Further information on <a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" rel='nofollow'>hybrid threats<\/a> is available from the <a href=\"https:\/\/www.bmvg.de\/de\/themen\/sicherheitspolitik\/hybride-bedrohungen\" rel='nofollow'>Federal Ministry of Defense<\/a> and the <a href=\"https:\/\/www.bnd.bund.de\/DE\/Die_Themen\/hybride-bedrohungen\/hybride-bedrohungen-node.html\" rel='nofollow'>Federal Intelligence Service (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>More publications related to Cyber Security<\/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>In the field of <strong>industrial data science<\/strong>, we develop deep learning architectures and image processing algorithms for <strong>AI-based quality control in manufacturing<\/strong>. We have conducted numerous projects involving the <strong>light-camera-based inspection<\/strong> of various <strong>fabrics<\/strong>, including <strong>leather<\/strong> (Mai\/Penava\/B\u00fcttner 2024) or <strong>cotton<\/strong> (Wiedemann\/Penava\/Mai\/B\u00fcttner 2025), <strong>packaging<\/strong>, <strong>welding seams<\/strong> (Breitenbach et al. 2021), and <strong>soldering joints<\/strong> (Eisentraut et al. 2025). We are also internationally recognized in a broad range of image data formats, including <strong>CT-based quality inspection <\/strong>of<strong> jet engines<\/strong>, <strong>ultrasound-based control<\/strong> of <strong>semiconductor boards,<\/strong> and <strong>thermal imaging-based inspection <\/strong>of<strong> metal 3D-printed products<\/strong> (Baumgartl et al. 2020).<\/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>More publications related to Industrial Data Science<\/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><strong>Medical Data Science I (<strong>AI-based Biosignal Analysis<\/strong>):<\/strong><\/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>The chair has extensive expertise in <strong>EEG<\/strong>-based analysis of <strong>personality traits<\/strong> (Penava &amp; B\u00fcttner 2025; Rieck\/Penava\/B\u00fcttner 2025), <strong>personality disorders<\/strong> (Baumgartl et al. 2020), <strong>diagnosis of depression<\/strong> (Penava &amp; B\u00fcttner 2024), <strong>alcoholism<\/strong> (Flathau et al. 2021, Rieg et al. 2019), <strong>schizophrenia<\/strong> (B\u00fcttner et al. 2019, 2020; Frick\/Rieg\/B\u00fcttner 2021; Baumgartl et al. 2021), <strong>epilepsy prevalence<\/strong> (B\u00fcttner\/Frick\/Rieg 2019; Rieg\/Frick\/B\u00fcttner 2020), <strong>non-substance addictions <\/strong>(Gro\u00df\/Baumgartl\/B\u00fcttner 2020), <strong>developmental disorders<\/strong> (Breitenbach et al. 2021; Gro\u00df et al. 2021; B\u00fcttner et al. 2021), <strong>anxiety disorders<\/strong> (Gro\u00df et al. 2021), <strong>sleep disorders<\/strong> (B\u00fcttner\/Grimmeisen\/Gotschlich 2020; Breitenbach\/Baumgartl\/B\u00fcttner 2020; B\u00fcttner\/Fuhrmann\/Kolb 2019), <strong>stress<\/strong> (Baumgartl\/Fezer\/B\u00fcttner 2020), and <strong>eating disorders<\/strong> (Raab\/Baumgartl\/B\u00fcttner 2020).<\/p>\n<\/div><\/div>\n\n\n\n<p>We also apply other biosignals such as <strong>ECG<\/strong> for machine-learning-based robust detection of <strong>arrhythmias<\/strong> (Rieg et al. 2020) and <strong>heart disease<\/strong> (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>More publications related to Medical Data Science I (AI-based Biosignal Analysis)<\/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><strong>Medical Data Science II (<strong>AI-based Imaging<\/strong>):<\/strong><\/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>We develop robust and highly precise AI architectures for the automated evaluation of imaging systems. Examples include <strong>ophthalmological scans<\/strong> for the detection of <strong>eye diseases<\/strong> (Gro\u00df et al. 2021; Rieck et al. 2025, 2025b), as well as <strong>X-ray\/CT images<\/strong> for the diagnosis of <strong>tuberculosis<\/strong> (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 (2025): 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>More publications related to Medical Data Science II (AI-based Imaging)<\/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.&nbsp;<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>In materials data science, we develop AI-based methods to reveal non-linear physical and electrochemical relationships. These methods can be used to derive <strong>mass density models<\/strong> for <strong>hard magnetics<\/strong> (Kini et al. 2023), and to analyze nonlinear property-process relationships in <strong>electroplating <\/strong>(S\u00f6rgel\/B\u00fcttner et al. 2021).<\/p>\n<\/div><\/div>\n\n\n\n<p>For example, within the BMFTR joint project with <abbr title=\"Technische Universit\u00e4t\">TU<\/abbr> Ilmenau, Aalen University, Offenburg University of Applied Sciences, the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, as well as the companies 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>, and IPT International Plating Technologies <abbr title=\"Gesellschaft mit beschr\u00e4nkter Haftung\">GmbH<\/abbr>, we are developing digital tools to improve electroplated coatings using chromium (III)-based processes (short title: DigiChrom). <a href=\"https:\/\/www.materialdigital.de\/project\/25\" rel='nofollow'>DigiChrom<\/a> is part of the <a href=\"https:\/\/www.materialdigital.de\/\" rel='nofollow'>MaterialDigital platform<\/a> initiative.<\/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><strong><strong>Food Industry<\/strong><\/strong>:<\/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>We examine the <strong>extent<\/strong> to which <strong>AI technologies<\/strong> are implemented and their <strong>potential<\/strong> in the food industry (Gradl\/Reis\/B\u00fcttner 2025). Particular focus is given to developing <strong>high-performance deep learning architectures<\/strong> that can robustly recognize <strong>fruit ripeness across different types of fruit <\/strong>(Fischer-Brandies et al. 2025). Other projects focus on specific fruits, for example cashew apples (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>AI in Human Resources<\/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>We cover the entire human resource process chain. From <strong>tailored recruitment and aptitude assessment<\/strong> (including B\u00fcttner 2014, 2016), through <strong>automated contract processes<\/strong> (including B\u00fcttner 2013), <strong>HR analytics<\/strong> (including B\u00fcttner 2015a, 2015b, 2017b) and <strong>controlling<\/strong> for assessing personnel situations and planning, to <strong>language model-based evaluation of exit interviews<\/strong> (e.g., M\u00f6nks\/Penava\/B\u00fcttner 2025), we have carried out numerous BMFTR research and application projects with commercial enterprises such as Airbus, Philips, and Deutsche Bahn.<\/p>\n<\/div><\/div>\n\n\n\n<p>All current projects employ AI, particularly for <strong>talent intelligence<\/strong>, <strong>intelligent training<\/strong>, <strong>performance management<\/strong>, <strong>retention management<\/strong>, <strong>organizational culture mining<\/strong>, and <strong>workplace safety<\/strong>. <strong>Generative AI<\/strong> is used in particular for <strong>personnel marketing<\/strong> and <strong>employer branding<\/strong>, <strong>employee self-services<\/strong>, <strong>chatbots in onboarding<\/strong>, <strong>AI-based generation<\/strong> <strong>and optimization of job advertisements<\/strong>, as well as for <strong>AI interviewing tools and chatbots<\/strong>.<\/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>More publications related to AI in Human Resources:<\/summary>\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","protected":false},"excerpt":{"rendered":"<p>The chair covers a wide range of methods, domains, and data formats. It has extensive expertise in developing innovative machine learning approaches, particularly modern deep learning architectures, and their application [&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":[7],"tags":[],"class_list":["post-114","page","type-page","status-publish","hentry","category-research"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/114","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=114"}],"version-history":[{"count":18,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/114\/revisions"}],"predecessor-version":[{"id":924,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/pages\/114\/revisions\/924"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/media?parent=114"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/categories?post=114"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/ai\/wp-json\/wp\/v2\/tags?post=114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}