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. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots. Social Sciences, 8(5), 143. https://doi.org/10.3390/socsci8050143
. (2019). The emergence of complexity. https://doi.org/10.1007/978-3-030-31839-0
. (2019). Emotional processes in human-robot interaction during brief cognitive testing. Computers In Human Behavior, 90, 331 - 342. https://doi.org/10.1016/j.chb.2018.08.013
. (2019). Fairer but not fair enough on the equitability of knowledge tracing. In the 9th International ConferenceProceedings of the 9th International Conference on Learning Analytics & Knowledge - LAK19 (335 - 339). https://doi.org/10.1145/3303772.3303838
. (2019). From immigrants to robots: The changing locus of substitutes for workers. https://doi.org/10.3386/w25438
. (2019). How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace (). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.013
. (2019). How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany (). In Electronic Government (pp. 233-247). https://doi.org/10.1007/978-3-030-27325-5
. (2019). How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany (). In Electronic Government (pp. 233-247). https://doi.org/10.1007/978-3-030-27325-5
. (2019). The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research, 1, 5. https://doi.org/10.12688/emeraldopenres.12907.1
. (2019). Impacts of machine learning on work. In Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52). https://doi.org/10.24251/HICSS.2019.719
Impacts_of_machine_learning_on_work__revision_.pdf (300.76 KB). (2019). Machine learning in Artificial Intelligence: Towards a common understanding (). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.630
. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers In Human Behavior, 90, 215 - 222. https://doi.org/10.1016/j.chb.2018.09.009
. (2019). The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172 - 181. https://doi.org/10.1016/j.tourman.2019.02.006