Literatura científica selecionada sobre o tema "Machine Learning Informé"
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Artigos de revistas sobre o assunto "Machine Learning Informé"
Shoureshi, R., D. Swedes e R. Evans. "Learning Control for Autonomous Machines". Robotica 9, n.º 2 (abril de 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.
Texto completo da fontePateras, Joseph, Pratip Rana e Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning". Applied Sciences 13, n.º 12 (7 de junho de 2023): 6892. http://dx.doi.org/10.3390/app13126892.
Texto completo da fonteMinasny, Budiman, Toshiyuki Bandai, Teamrat A. Ghezzehei, Yin-Chung Huang, Yuxin Ma, Alex B. McBratney, Wartini Ng et al. "Soil Science-Informed Machine Learning". Geoderma 452 (dezembro de 2024): 117094. http://dx.doi.org/10.1016/j.geoderma.2024.117094.
Texto completo da fonteXypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco e Marco Leonetti. "Physics-informed machine learning for microscopy". EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.
Texto completo da fonteZhao, Hefei, Yinglun Zhan, Joshua Nduwamungu, Yuzhen Zhou, Changmou Xu e Zheng Xu. "Machine learning-driven Raman spectroscopy for rapidly detecting type, adulteration, and oxidation of edible oils". INFORM International News on Fats, Oils, and Related Materials 31, n.º 4 (1 de abril de 2020): 12–15. http://dx.doi.org/10.21748/inform.04.2020.12.
Texto completo da fonteSerre, Thomas. "Deep Learning: The Good, the Bad, and the Ugly". Annual Review of Vision Science 5, n.º 1 (15 de setembro de 2019): 399–426. http://dx.doi.org/10.1146/annurev-vision-091718-014951.
Texto completo da fonteArundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan e Philip T. Thiem. "Historical maps inform landform cognition in machine learning". Abstracts of the ICA 6 (11 de agosto de 2023): 1–2. http://dx.doi.org/10.5194/ica-abs-6-10-2023.
Texto completo da fonteKarimpouli, Sadegh, e Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation". Geoscience Frontiers 11, n.º 6 (novembro de 2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.
Texto completo da fonteZhang, Xi. "Application of Machine Learning in Stock Price Analysis". Highlights in Science, Engineering and Technology 107 (15 de agosto de 2024): 143–49. http://dx.doi.org/10.54097/tjhsx998.
Texto completo da fonteLiu, Yang, Ruo Jia, Jieping Ye e Xiaobo Qu. "How machine learning informs ride-hailing services: A survey". Communications in Transportation Research 2 (dezembro de 2022): 100075. http://dx.doi.org/10.1016/j.commtr.2022.100075.
Texto completo da fonteTeses / dissertações sobre o assunto "Machine Learning Informé"
Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI". Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.
Texto completo da fonteFrom conception onward, environmental factors such as air quality or dietary habits can significantly impact the risk of developing various chronic diseases. Within the epidemiological literature, indicators known as Environmental Risk Scores (ERSs) are used not only to identify individuals at risk but also to study the relationships between environmental factors and health. A limit of most ERSs is that they are expressed as linear combinations of a limited number of factors. This doctoral thesis aims to develop ERS indicators able to investigate nonlinear relationships and interactions across a broad range of exposures while discovering actionable factors to guide preventive measures and interventions, both in adults and children. To achieve this aim, we leverage the predictive abilities of non-parametric machine learning methods, combined with recent Explainable AI tools and existing domain knowledge. In the first part of this thesis, we compute machine learning-based environmental risk scores for mental, cardiometabolic, and respiratory general health for children. On top of identifying nonlinear relationships and exposure-exposure interactions, we identified new predictors of disease in childhood. The scores could explain a significant proportion of variance and their performances were stable across different cohorts. In the second part, we propose SEANN, a new approach integrating expert knowledge in the form of Pooled Effect Sizes (PESs) into the training of deep neural networks for the computation of extit{informed environmental risk scores}. SEANN aims to compute more robust ERSs, generalizable to a broader population, and able to capture exposure relationships that are closer to evidence known from the literature. We experimentally illustrate the approach's benefits using synthetic data, showing improved prediction generalizability in noisy contexts (i.e., observational settings) and improved reliability of interpretation using Explainable Artificial Intelligence (XAI) methods compared to an agnostic neural network. In the last part of this thesis, we propose a concrete application for SEANN using data from a cohort of Spanish adults. Compared to an agnostic neural network-based ERS, the score obtained with SEANN effectively captures relationships more in line with the literature-based associations without deteriorating the predictive performances. Moreover, exposures with poor literature coverage significantly differ from those obtained with the agnostic baseline method with more plausible directions of associations.In conclusion, our risk scores demonstrate substantial potential for the data-driven discovery of unknown nonlinear environmental health relationships by leveraging existing knowledge about well-known relationships. Beyond their utility in epidemiological research, our risk indicators are able to capture holistic individual-level non-hereditary risk associations that can inform practitioners about actionable factors in high-risk individuals. As in the post-genetic era, personalized medicine prevention will focus more and more on modifiable factors, we believe that such approaches will be instrumental in shaping future healthcare paradigms
Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations". Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.
Texto completo da fonteLeung, Jason W. "Application of machine learning : automated trading informed by event driven data". Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105982.
Texto completo da fonteThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-65).
Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be employed as an artificial trader used to trade on any given stock exchange. The performance of the model is evaluated using the S&P 500 index.
by Jason W. Leung.
M. Eng.
Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.
Texto completo da fontePh. D.
Reynolds-Averaged Navier–Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.
Reichert, Nils. "CORRELATION BETWEEN COMPUTER RECOGNIZED FACIAL EMOTIONS AND INFORMED EMOTIONS DURING A CASINO COMPUTER GAME". Thesis, Fredericton: University of New Brunswick, 2012. http://hdl.handle.net/1882/44596.
Texto completo da fonteWang, Jianxun. "Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77035.
Texto completo da fontePh. D.
Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications". Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.
Texto completo da fonteEmerson, Guy Edward Toh. "Functional distributional semantics : learning linguistically informed representations from a precisely annotated corpus". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/284882.
Texto completo da fonteGiuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
Texto completo da fonteAugustin, Lefèvre. "Méthodes d'apprentissage appliquées à la séparation de sources mono-canal". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00764546.
Texto completo da fonteLivros sobre o assunto "Machine Learning Informé"
Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure. Elsevier Science & Technology, 2023.
Encontre o texto completo da fonteMadhu, G., Sandeep Kautish, A. Govardhan e Avinash Sharma, eds. Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150792721220101.
Texto completo da fonteSmith, Gary, e Jay Cordes. The 9 Pitfalls of Data Science. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844396.001.0001.
Texto completo da fonteAnderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.
Texto completo da fonteEl-Nasr, Magy Seif, Alessandro Canossa, Truong-Huy D. Nguyen e Anders Drachen. Game Data Science. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192897879.001.0001.
Texto completo da fonteDowd, Cate. Digital Journalism, Drones, and Automation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190655860.001.0001.
Texto completo da fonteOulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi e Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.
Texto completo da fonteGiudici, Paolo, e Giulio Mignola. Big Data & Advanced Analytics per il Risk Management. AIFIRM, 2022. http://dx.doi.org/10.47473/2016ppa00035.
Texto completo da fonteDobson, James E. Critical Digital Humanities. University of Illinois Press, 2019. http://dx.doi.org/10.5622/illinois/9780252042270.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Machine Learning Informé"
Neuer, Marcus J. "Physics-Informed Learning". In Machine Learning for Engineers, 173–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-69995-9_6.
Texto completo da fonteBraga-Neto, Ulisses. "Physics-Informed Machine Learning". In Fundamentals of Pattern Recognition and Machine Learning, 293–324. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60950-3_12.
Texto completo da fonteWang, Sifan, e Paris Perdikaris. "Adaptive Training Strategies for Physics-Informed Neural Networks". In Knowledge-Guided Machine Learning, 133–60. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.
Texto completo da fonteSimm, Jaak, Adam Arany, Edward De Brouwer e Yves Moreau. "Expressive Graph Informer Networks". In Machine Learning, Optimization, and Data Science, 198–212. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3_15.
Texto completo da fonteAfroze, Lameya, Silke Merkelbach, Sebastian von Enzberg e Roman Dumitrescu. "Domain Knowledge Injection Guidance for Predictive Maintenance". In Machine Learning for Cyber-Physical Systems, 75–87. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47062-2_8.
Texto completo da fonteSun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih e Zhi Zhong. "Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey". In Knowledge-Guided Machine Learning, 111–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-5.
Texto completo da fonteDani, Harsh, Jundong Li e Huan Liu. "Sentiment Informed Cyberbullying Detection in Social Media". In Machine Learning and Knowledge Discovery in Databases, 52–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71249-9_4.
Texto completo da fonteMumtaz, Zahid. "Machine Learning-Based Approach for Exploring the Household Survey Data". In Informal Social Protection and Poverty, 141–200. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6474-9_7.
Texto completo da fonteCross, Elizabeth J., S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang e T. J. Rogers. "Physics-Informed Machine Learning for Structural Health Monitoring". In Structural Integrity, 347–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_17.
Texto completo da fonteSudarshan, Viswanath P., K. Pavan Kumar Reddy, Mohana Singh, Jayavardhana Gubbi e Arpan Pal. "Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior". In Machine Learning for Medical Image Reconstruction, 145–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17247-2_15.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Machine Learning Informé"
Oneto, Luca, Nicolò Navarin, Alessio Micheli, Luca Pasa, Claudio Gallicchio, Davide Bacciu e Davide Anguita. "Informed Machine Learning for Complex Data". In ESANN 2024, 1–10. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-1.
Texto completo da fonteOneto, Luca, Davide Anguita e Sandro Ridella. "Informed Machine Learning: Excess Risk and Generalization". In ESANN 2024, 11–16. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-20.
Texto completo da fonteFarlessyost, William, e Shweta Singh. "Improving Mechanistic Model Accuracy with Machine Learning Informed Physics". In Foundations of Computer-Aided Process Design, 275–82. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.121371.
Texto completo da fonteZhu, Shijie, Hao Li, Yejie Jiang e Yingjun Deng. "Inner Defect Detection via Physics-Informed Machine Learning". In 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), 212–16. IEEE, 2024. https://doi.org/10.1109/srse63568.2024.10772527.
Texto completo da fonteYu, Yue, Jiageng Tong, Jinhui Xia, Jinya Su e Shihua Li. "PMSM System Identification by Knowledge-informed Machine Learning". In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), 1–6. IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774223.
Texto completo da fonteZhang, Tianren, Yuanbin Wang, Ruizhe Dong, Wenhu Wang, Zhongxue Yang e Mingzhu Zhu. "Informed Machine Learning for Real-time Grinding Force Prediction". In 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/m2vip62491.2024.10746047.
Texto completo da fonteSurner, Martin, e Abdelmajid Khelil. "CIML-R: Causally Informed Machine Learning Based on Feature Relevance". In 2024 11th IEEE Swiss Conference on Data Science (SDS), 68–75. IEEE, 2024. http://dx.doi.org/10.1109/sds60720.2024.00018.
Texto completo da fonteFilipovic, Lado, Tobias Reiter, Julius Piso e Roman Kostal. "Equipment-Informed Machine Learning-Assisted Feature-Scale Plasma Etching Model". In 2024 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/sispad62626.2024.10733099.
Texto completo da fonteIto, Rikuto, Yasuhiro Oikawa e Kenji Ishikawa. "Tomographic Reconstruction of Sound Field From Optical Projections Using Physics-Informed Neural Networks". In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/mlsp58920.2024.10734743.
Texto completo da fonteWade, Daniel, Hieu Ngo, Frances Love, Jeremy Partain, Andrew Wilson, Matthew Statham e Perumal Shanthakumaran. "Measurement of Vibration Transfer Functions to Inform Machine Learning Based HUMS Diagnostics". In Vertical Flight Society 72nd Annual Forum & Technology Display, 1–14. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11479.
Texto completo da fonteRelatórios de organizações sobre o assunto "Machine Learning Informé"
Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask e Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), outubro de 2020. http://dx.doi.org/10.2172/1706217.
Texto completo da fonteMcDermott, Jason, Song Feng, Christine Chang, Darren Schmidt e Vincent Danna. Structural- and Functional-Informed Machine Learning for Protein Function Prediction. Office of Scientific and Technical Information (OSTI), setembro de 2021. http://dx.doi.org/10.2172/1988630.
Texto completo da fonteGuthrie, George Drake Jr, e Hari S. Viswanathan. Science-informed Machine Learning to Increase Recovery Efficiency in Unconventional Reservoirs. Office of Scientific and Technical Information (OSTI), abril de 2020. http://dx.doi.org/10.2172/1614818.
Texto completo da fonteWang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino e Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), setembro de 2016. http://dx.doi.org/10.2172/1562229.
Texto completo da fonteBailey Bond, Robert, Pu Ren, James Fong, Hao Sun e Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, agosto de 2024. http://dx.doi.org/10.17760/d20680141.
Texto completo da fonteMueller, Juliane. Machine Learning to Enable Efficient Uncertainty Quantification, Data Assimilation, and Informed Data Acquisition. Office of Scientific and Technical Information (OSTI), março de 2021. http://dx.doi.org/10.2172/1769743.
Texto completo da fonteAthon, Matthew, Danielle Ciesielski, Jordan Corbey, Shenyang Hu, Ethan King, Yulan Li, Jacqueline Royer, Panagiotis Stinis, Robert Surbella e Scott Swenson. Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning. Office of Scientific and Technical Information (OSTI), setembro de 2023. http://dx.doi.org/10.2172/2338176.
Texto completo da fonteUllrich, Paul, Tapio Schneider e Da Yang. Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769762.
Texto completo da fonteGhanshyam, Pilania, Kenneth James McClellan, Christopher Richard Stanek e Blas P. Uberuaga. Physics-Informed Machine Learning for Discovery and Optimization of Materials: A Case Study of Scintillators. Office of Scientific and Technical Information (OSTI), agosto de 2018. http://dx.doi.org/10.2172/1463529.
Texto completo da fonteBao, Jie, Chao Wang, Zhijie Xu e Brian J. Koeppel. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. Office of Scientific and Technical Information (OSTI), setembro de 2019. http://dx.doi.org/10.2172/1569289.
Texto completo da fonte