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Статті в журналах з теми "Hybrid AI":
Hopgood, A. "Hybrid AI." ITNOW 55, no. 4 (November 26, 2013): 10–11. http://dx.doi.org/10.1093/itnow/bwt066.
Ikegaya, 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.
Silva, Felipe Leno Da, Silvio Stanzani, Jefferson Fialho, Jorge Mondadori, Muriel Mazzetto, Felipe Sanches Couto, and Raphael Cobe. "Designing a Hybrid AI Residency." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15640–46. http://dx.doi.org/10.1609/aaai.v35i17.17842.
Siddique, Nazmul H., Balasundram P. Amavasai, and Akira Ikuta. "Editorial: Hybrid Techniques in AI." Artificial Intelligence Review 27, no. 2-3 (March 2007): 77–78. http://dx.doi.org/10.1007/s10462-008-9085-2.
Mateas, Michael. "Expressive AI: A Hybrid Art and Science Practice." Leonardo 34, no. 2 (April 2001): 147–53. http://dx.doi.org/10.1162/002409401750184717.
Monostori, L., Cs Egresits, and B. Kádár. "Hybrid AI Approaches to Intelligent Manufacturing." IFAC Proceedings Volumes 29, no. 1 (June 1996): 571–76. http://dx.doi.org/10.1016/s1474-6670(17)57723-x.
Khriapynskyi, Anton, Ihor Khmyrov, Ivo Svoboda, Mykhailo Shevchuk, and Vira Iastrebova. "State information security strategies in conditions of hybrid threats." Revista Amazonia Investiga 12, no. 69 (September 30, 2023): 84–93. http://dx.doi.org/10.34069/ai/2023.69.09.7.
Almusaed, Amjad, Asaad Almssad, Ibrahim Yitmen, and Raad Z. Homod. "Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis." Education Sciences 13, no. 7 (June 21, 2023): 632. http://dx.doi.org/10.3390/educsci13070632.
Yang, J. B. "Hybrid AI system for retaining wall selection." Construction Innovation 4, no. 1 (March 2004): 33–52. http://dx.doi.org/10.1108/14714170410814999.
Yang, J. B. "Hybrid AI system for retaining wall selection." Construction Innovation 4, no. 1 (March 1, 2004): 33–52. http://dx.doi.org/10.1191/1471417504ci065oa.
Дисертації з теми "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.
Khan, 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.
Lilla, 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.
ISAKSSON, 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.
Abdullah, 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.
Jha, 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.
"Representing Hybrid Transition Systems in an Action Language Modulo ODEs." Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44191.
Dissertation/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.
Atualmente, 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.
O 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.
Книги з теми "Hybrid AI":
1960-, Sun Ron, and Alexandre Frederic, eds. Connectionist-symbolic integration: From unified to hybrid approaches. Mahwah, N.J: Lawrence Erlbaum Associates, 1997.
Jian, Lirong. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.
Alexandrov, 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.
Sanjeevikumar, P., Sulabh Sachan, and Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.
Sanjeevikumar, P., Sulabh Sachan, and Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.
Sachan. AI-Based Solutions for Hybrid and Electric Vehicle S. Wiley & Sons, Limited, John, 2023.
Romanelli, Ricardo, Antonio Pannullo, and Marco Zanello. Endurance WEC: Dalle Gruppo C Ai Prototipi Ibridi/ from Group C to Hybrid Prototypes. Giorgio Nada Editore, 2021.
(Editor), Ron Sun, and Frederic Alexandre (Editor), eds. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Lawrence Erlbaum, 1997.
Liu, Sifeng, Yi Lin, and Lirong Jian. Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor & Francis Group, 2018.
Biswas, Gautam, and Sheila McIlraith. Hybrid Systems and AI - Modeling Analysis and Control of Discrete Plus Continuous Systems: Papers from the AAAI Spring Symposium. AAAI Press, 1999.
Частини книг з теми "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.
Mainzer, Klaus, and 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.
Bloch, 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.
Vassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova, and 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.
Davidzon, Guido A., and 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.
Hnich, Brahim, Roberto Rossi, S. Armagan Tarim, and 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.
Wall, Riley, and 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.
Zannos, Iannis, and 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.
Barke, 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.
Achterberg, Tobias, and 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.
Тези доповідей конференцій з теми "Hybrid AI":
Krishna, Siddanth, Siri S, Saif Kamalsha, Sai Amruth, and 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.
Scala, 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.
Wang, 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.
Taran, Ekaterina, Veronika Malanina, and 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.
Azam, Md Ali, Md Abir Hossen, and 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.
Freitag, 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.
Diniz Junqueira Barbosa, Gabriel, and 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.
Aghamohseni, Akram, and 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.
Pelosi, Andrea, Claudio Felicioli, Andrea Canciani, and 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.
Fang, Tao, Jingwei Li, Tongyu Wu, Ming Cheng, and Xiaowen Dong. "Efficient training for the hybrid optical diffractive deep neural network." In AI and Optical Data Sciences III, edited by Ken-ichi Kitayama and Bahram Jalali. SPIE, 2022. http://dx.doi.org/10.1117/12.2607567.
Звіти організацій з теми "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), February 2021. http://dx.doi.org/10.2172/1769645.
Djordjevich, Donna D., Patrick Gordon Xavier, Nathan Gregory Brannon, Brian E. Hart, Derek H. Hart, Charles Quentin Little, Fred John III Oppel, John Michael Linebarger, and Eric Paul Parker. LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1034891.
Mohanty, Subhasish, and 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.
Mohanty, 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), June 2022. http://dx.doi.org/10.2172/1874565.