Auswahl der wissenschaftlichen Literatur zum Thema „Hybrid AI“
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Zeitschriftenartikel zum Thema "Hybrid AI"
Hopgood, A. „Hybrid AI“. ITNOW 55, Nr. 4 (26.11.2013): 10–11. http://dx.doi.org/10.1093/itnow/bwt066.
Der volle Inhalt der QuelleIkegaya, Yuji. „Brain-AI hybrid“. Proceedings for Annual Meeting of The Japanese Pharmacological Society 97 (2023): 3—B—SL16. http://dx.doi.org/10.1254/jpssuppl.97.0_3-b-sl16.
Der volle Inhalt der QuelleSilva, Felipe Leno Da, Silvio Stanzani, Jefferson Fialho, Jorge Mondadori, Muriel Mazzetto, Felipe Sanches Couto und Raphael Cobe. „Designing a Hybrid AI Residency“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 17 (18.05.2021): 15640–46. http://dx.doi.org/10.1609/aaai.v35i17.17842.
Der volle Inhalt der QuelleSiddique, Nazmul H., Balasundram P. Amavasai und Akira Ikuta. „Editorial: Hybrid Techniques in AI“. Artificial Intelligence Review 27, Nr. 2-3 (März 2007): 77–78. http://dx.doi.org/10.1007/s10462-008-9085-2.
Der volle Inhalt der QuelleMateas, Michael. „Expressive AI: A Hybrid Art and Science Practice“. Leonardo 34, Nr. 2 (April 2001): 147–53. http://dx.doi.org/10.1162/002409401750184717.
Der volle Inhalt der QuelleMonostori, L., Cs Egresits und B. Kádár. „Hybrid AI Approaches to Intelligent Manufacturing“. IFAC Proceedings Volumes 29, Nr. 1 (Juni 1996): 571–76. http://dx.doi.org/10.1016/s1474-6670(17)57723-x.
Der volle Inhalt der QuelleKhriapynskyi, Anton, Ihor Khmyrov, Ivo Svoboda, Mykhailo Shevchuk und Vira Iastrebova. „State information security strategies in conditions of hybrid threats“. Revista Amazonia Investiga 12, Nr. 69 (30.09.2023): 84–93. http://dx.doi.org/10.34069/ai/2023.69.09.7.
Der volle Inhalt der QuelleAlmusaed, Amjad, Asaad Almssad, Ibrahim Yitmen und Raad Z. Homod. „Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis“. Education Sciences 13, Nr. 7 (21.06.2023): 632. http://dx.doi.org/10.3390/educsci13070632.
Der volle Inhalt der QuelleYang, J. B. „Hybrid AI system for retaining wall selection“. Construction Innovation 4, Nr. 1 (März 2004): 33–52. http://dx.doi.org/10.1108/14714170410814999.
Der volle Inhalt der QuelleYang, J. B. „Hybrid AI system for retaining wall selection“. Construction Innovation 4, Nr. 1 (01.03.2004): 33–52. http://dx.doi.org/10.1191/1471417504ci065oa.
Der volle Inhalt der QuelleDissertationen zum Thema "Hybrid AI"
Piotrowski, Wiktor Mateusz. „Heuristics for AI planning in hybrid systems“. Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/heuristics-for-ai-planning-in-hybrid-systems(bbe2ba21-3449-4689-8bf8-6e441515cd10).html.
Der volle Inhalt der QuelleKhan, Laiq. „Hybrid AI paradigms applied to power system damping controls“. Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273412.
Der volle Inhalt der QuelleLilla, Abdurahman Daleel. „AI-based hybrid optimisation of multi-megawatt scale permanent magnet synchronous generators for offshore wind energy capture“. Master's thesis, Faculty of Engineering and the Built Environment, 2019. https://hdl.handle.net/11427/31667.
Der volle Inhalt der QuelleISAKSSON, LARS JOHANNES. „HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER“. Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.
Der volle Inhalt der QuelleAbdullah, Siti Norbaiti binti. „Machine learning approach for crude oil price prediction“. Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/machine-learning-approach-for-crude-oil-price-prediction(949fa2d5-1a4d-416a-8e7c-dd66da95398e).html.
Der volle Inhalt der QuelleJha, Alok Kumar. „Intelligent Control and Path Planning of Multiple Mobile Robots Using Hybrid Ai Techniques“. Thesis, 2016. http://ethesis.nitrkl.ac.in/7416/1/2016_PhD_AKJha_510ME109.pdf.
Der volle Inhalt der Quelle„Representing Hybrid Transition Systems in an Action Language Modulo ODEs“. Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44191.
Der volle Inhalt der QuelleDissertation/Thesis
Masters Thesis Computer Science 2017
Thurner, Thomas. „The influence factors of the patients’ usage intention of AI-based preliminary diagnosis tools : the case study of Ada“. Master's thesis, 2020. http://hdl.handle.net/10400.14/29804.
Der volle Inhalt der QuelleAtualmente, a inteligência artificial está a transformar os mecanismos e limitações de diversas indústrias. O sector da saúde é particularmente afetado pelo potencial informativo de processamento e análise de dados de pacientes através de tecnologias de inteligência artificial. Cortes orçamentais públicos e ineficiências a nível estrutural evidenciam a necessidade de, idealmente, empregar os dados de pacientes. Na sua maioria, as instalações de saúde carecem de recursos ou de conhecimento técnico para se inteirarem do potencial da inteligência artificial. Consequentemente, as empresas emergentes, que teoricamente podem ser classificadas como um formato intermédio entre estabelecimentos públicos e privados, definem um novo conceito. A adaptação estrutural das organizações híbridas facilita a oferta de produtos e serviços especializados às necessidades dos pacientes. Neste sentido, aplicações móveis de diagnóstico preliminar recorrendo a inteligência artificial, representam uma oportunidade promissora por conceder autonomia aos pacientes e influenciando positivamente a qualidade do sector da saúde. Os fatores determinantes da adoção e intenção de uso por parte dos pacientes está, ainda, por explorar. A presente dissertação examinou a perspetiva dos pacientes relativamente às ferramentas de diagnóstico preliminar com recurso à inteligência artificial, com o intuito inicial de expandir a literatura referente a esta temática e de identificar elementos fundamentais para as medidas de marketing e estratégia de organizações híbridas que operam neste meio. As implicações deste estudo incluem o reconhecimento de pacientes que tencionem recorrer a aplicações móveis semelhantes e suas subsequentes implicações estratégicas, assim como diretrizes a nível de marketing e estratégia para negócios equivalentes.
Weißenburger, Julius Eric. „Disruption in HR : the impact of Artificial Intelligence and machine learning innovation on recruiting“. Master's thesis, 2020. http://hdl.handle.net/10400.14/31314.
Der volle Inhalt der QuelleO talento é cada vez mais importante para as organizações que utilizam o recrutamento corporativo como uma função contínua e significativa. O recrutamento dos melhores talentos não pode ocorrer onde existem ineficiências, altos custos e falta de inovação. Ao mesmo tempo, a inteligência artificial (IA) e machine learning (ML) estão rompendo indústrias e diferentes áreas de prática de negócios. Essa tecnologia tem o potencial de criar um valor sem precedentes nas funções de recrutamento, impactando positivamente a eficiência, os custos e a adequação dos funcionários. Apesar do rápido desenvolvimento no campo da IA, a literatura acadêmica sobre IA no recrutamento é escassa. Os pesquisadores gostariam que existisse mais trabalho colaborativo entre profissionais e acadêmicos. Esta tese visa abordar essa lacuna, avaliando como a IA e o ML modificam os processos tradicionais de recrutamento e trazem novos resultados potenciais. Ao integrar as experiências de especialistas, executivos e as percepções de possíveis candidatos a emprego, esta tese elucida implicações práticas para a adoção de IA e ML no recrutamento. A tese utiliza coleta de dados qualitativa e quantitativa. Os resultados apresentam oportunidades e também as limitações da IA e ML. Além disso, os efeitos da tecnologia no recrutamento eficiente e válido são avaliados. Isso cria a base para recomendações práticas para as organizações com relação à adoção desta tecnologia. Notavelmente, nos aspectos mais padronizados dos processos de recrutamento, essa tecnologia cria valor na contratação.
Bücher zum Thema "Hybrid AI"
1960-, Sun Ron, und Alexandre Frederic, Hrsg. Connectionist-symbolic integration: From unified to hybrid approaches. Mahwah, N.J: Lawrence Erlbaum Associates, 1997.
Den vollen Inhalt der Quelle findenJian, Lirong. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.
Den vollen Inhalt der Quelle findenAlexandrov, Eugeniu G. Hibrizii distanți ai viței de vie (Vitis vinifera L. x Muscadinia rotundifolia Michx.): Aspecte biomorfologice și uvologice = Les hybrides interspécifiques de vigne (V. vinifera L. x M. rotundifolia Michx. : aspects biomorphologiques = Otdalennye gibridy vinograda (Vitis vinifera L. x Muscadinia rotundifolia Michx.) : biomorfologicheskie i uvologicheskie aspekty. Chișinău: Grădina Botanică (Institut) a AȘM, 2012.
Den vollen Inhalt der Quelle findenSanjeevikumar, P., Sulabh Sachan und Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.
Den vollen Inhalt der Quelle findenSanjeevikumar, P., Sulabh Sachan und Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.
Den vollen Inhalt der Quelle findenSachan. AI-Based Solutions for Hybrid and Electric Vehicle S. Wiley & Sons, Limited, John, 2023.
Den vollen Inhalt der Quelle findenRomanelli, Ricardo, Antonio Pannullo und Marco Zanello. Endurance WEC: Dalle Gruppo C Ai Prototipi Ibridi/ from Group C to Hybrid Prototypes. Giorgio Nada Editore, 2021.
Den vollen Inhalt der Quelle finden(Editor), Ron Sun, und Frederic Alexandre (Editor), Hrsg. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Lawrence Erlbaum, 1997.
Den vollen Inhalt der Quelle findenLiu, Sifeng, Yi Lin und Lirong Jian. Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor & Francis Group, 2018.
Den vollen Inhalt der Quelle findenBiswas, Gautam, und Sheila McIlraith. Hybrid Systems and AI - Modeling Analysis and Control of Discrete Plus Continuous Systems: Papers from the AAAI Spring Symposium. AAAI Press, 1999.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Hybrid AI"
Henning, Klaus. „The Age of Hybrid Intelligence“. In Gamechanger AI, 61–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52897-3_7.
Der volle Inhalt der QuelleMainzer, Klaus, und Reinhard Kahle. „Prospects for Hybrid AI“. In Technik im Fokus, 113–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68290-6_5.
Der volle Inhalt der QuelleBloch, Isabelle. „Subsymbolic, hybrid and explainable AI“. In KI-Kritik / AI Critique, 179–96. Bielefeld, Germany: transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839467664-010.
Der volle Inhalt der QuelleVassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova und Wiktor Sowinski-Mydlarz. „AI-Based Hybrid Data Platforms“. In Data Spaces, 147–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_8.
Der volle Inhalt der QuelleDavidzon, Guido A., und Henry Li. „AI for Decision Support in Molecular Neuroimaging“. In Hybrid PET/MR Neuroimaging, 67–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82367-2_8.
Der volle Inhalt der QuelleHnich, Brahim, Roberto Rossi, S. Armagan Tarim und Steven Prestwich. „A Survey on CP-AI-OR Hybrids for Decision Making Under Uncertainty“. In Hybrid Optimization, 227–70. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1644-0_7.
Der volle Inhalt der QuelleWall, Riley, und Parimala Thulasiraman. „An Island Model Genetic Algorithm Approach to Tuning AI Bots“. In Hybrid Intelligent Systems, 617–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_60.
Der volle Inhalt der QuelleZannos, Iannis, und Haruka Hirayama. „Towards an Aesthetic of Hybrid Performance Practice“. In Music in the AI Era, 111–21. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35382-6_10.
Der volle Inhalt der QuelleBarke, Daniel. „AI as a Driver of Hybrid Forms of Employment“. In Work and AI 2030, 151–58. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40232-7_17.
Der volle Inhalt der QuelleAchterberg, Tobias, und Timo Berthold. „Hybrid Branching“. In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 309–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01929-6_23.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Hybrid AI"
Krishna, Siddanth, Siri S, Saif Kamalsha, Sai Amruth und Shruti Jadon. „PRIVATE-AI: A Hybrid Approach to privacy-preserving AI“. In 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD). IEEE, 2023. http://dx.doi.org/10.1109/bcd57833.2023.10466330.
Der volle Inhalt der QuelleScala, Enrico. „AI Planning for Hybrid Systems“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/805.
Der volle Inhalt der QuelleWang, Haoran. „Freeing hybrid distributed AI training configuration“. In ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468264.3473104.
Der volle Inhalt der QuelleTaran, Ekaterina, Veronika Malanina und Fabio Casati. „Crowd Science for Hybrid AI Applications“. In 2021 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE, 2021. http://dx.doi.org/10.1109/sose52839.2021.00027.
Der volle Inhalt der QuelleAzam, Md Ali, Md Abir Hossen und Md Hafizur Rahman. „Hybrid Ant Swarm-Based Data Clustering“. In 2021 IEEE World AI IoT Congress (AIIoT). IEEE, 2021. http://dx.doi.org/10.1109/aiiot52608.2021.9454238.
Der volle Inhalt der QuelleFreitag, Marina. „Revolutionizing Indoor Energy Harvesting: From Advanced Materials to AI Integration“. In International Conference on Hybrid and Organic Photovoltaics. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2024. http://dx.doi.org/10.29363/nanoge.hopv.2024.096.
Der volle Inhalt der QuelleDiniz Junqueira Barbosa, Gabriel, und Simone Diniz Junqueira Barbosa. „Towards Diverse AI: Can an AI-Human Hybrid Council Prevent Future Apartheids?“ In 17th IFIP TC.13 International Conference on Human-Computer Interaction. Cardiff University Press, 2020. http://dx.doi.org/10.18573/book3.aa.
Der volle Inhalt der QuelleAghamohseni, Akram, und Rasool Ramezanian. „An efficient hybrid approach based on K-means and generalized fashion algorithms for cluster analysis“. In 2015 AI & Robotics (IRANOPEN). IEEE, 2015. http://dx.doi.org/10.1109/rios.2015.7270727.
Der volle Inhalt der QuellePelosi, Andrea, Claudio Felicioli, Andrea Canciani und Fabio Severino. „A Hybrid-DLT Based Trustworthy AI Framework“. In 2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2023. http://dx.doi.org/10.1109/wetice57085.2023.10477792.
Der volle Inhalt der QuelleFang, Tao, Jingwei Li, Tongyu Wu, Ming Cheng und Xiaowen Dong. „Efficient training for the hybrid optical diffractive deep neural network“. In AI and Optical Data Sciences III, herausgegeben von Ken-ichi Kitayama und Bahram Jalali. SPIE, 2022. http://dx.doi.org/10.1117/12.2607567.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Hybrid AI"
Wang, Jiali, Rao Kotamarthi, Virendra Ghate, Bethany Lusch, Prasanna Balaprakash, Justin Wozniak, Xingqiu Yuan et al. A Hybrid Climate Modeling System Using AI-assisted Process Emulators. Office of Scientific and Technical Information (OSTI), Februar 2021. http://dx.doi.org/10.2172/1769645.
Der volle Inhalt der QuelleDjordjevich, Donna D., Patrick Gordon Xavier, Nathan Gregory Brannon, Brian E. Hart, Derek H. Hart, Charles Quentin Little, Fred John III Oppel, John Michael Linebarger und Eric Paul Parker. LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC. Office of Scientific and Technical Information (OSTI), Januar 2012. http://dx.doi.org/10.2172/1034891.
Der volle Inhalt der QuelleMohanty, Subhasish, und Joseph Listwan. A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1688432.
Der volle Inhalt der QuelleMohanty, Subhasish. Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction. Office of Scientific and Technical Information (OSTI), Juni 2022. http://dx.doi.org/10.2172/1874565.
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