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Artykuły w czasopismach na temat „Machine Learning Informé”

Autor: Grafiati

Data publikacji: 21 grudnia 2024

Data aktualizacji: 31 lipca 2025

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1

Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.

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Pateras, Joseph, Pratip Rana, and Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning." Applied Sciences 13, no. 12 (2023): 6892. http://dx.doi.org/10.3390/app13126892.

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Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information. This work discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINNs) in particular are highlighted. Additionally, we explore recent works toward utilizing neural operator learning to intuit relationships in physics systems

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Minasny, Budiman, Toshiyuki Bandai, Teamrat A. Ghezzehei, et al. "Soil Science-Informed Machine Learning." Geoderma 452 (December 2024): 117094. http://dx.doi.org/10.1016/j.geoderma.2024.117094.

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Xypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco, and Marco Leonetti. "Physics-informed machine learning for microscopy." EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.

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We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvements starting from low exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution which we included in the training loss function. Our approach surpasses previous algorithms performance for microscopy data, moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.

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Zhao, Hefei, Yinglun Zhan, Joshua Nduwamungu, Yuzhen Zhou, Changmou Xu, and 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, no. 4 (2020): 12–15. http://dx.doi.org/10.21748/inform.04.2020.12.

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Serre, Thomas. "Deep Learning: The Good, the Bad, and the Ugly." Annual Review of Vision Science 5, no. 1 (2019): 399–426. http://dx.doi.org/10.1146/annurev-vision-091718-014951.

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Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern mac

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Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. "Historical maps inform landform cognition in machine learning." Abstracts of the ICA 6 (August 11, 2023): 1–2. http://dx.doi.org/10.5194/ica-abs-6-10-2023.

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Karimpouli, Sadegh, and Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation." Geoscience Frontiers 11, no. 6 (2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.

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Oneto, Luca, Sandro Ridella, and Davide Anguita. "Informed Machine Learning: Excess risk and generalization." Neurocomputing 646 (September 2025): 130521. https://doi.org/10.1016/j.neucom.2025.130521.

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Zhang, Xi. "Application of Machine Learning in Stock Price Analysis." Highlights in Science, Engineering and Technology 107 (August 15, 2024): 143–49. http://dx.doi.org/10.54097/tjhsx998.

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With the advancement of technology, machine learning has emerged as a powerful tool for analyzing complex financial data, including stock prices. By leveraging algorithms capable of identifying patterns and trends, it offers insights into market behavior. This study explores the application of machine learning techniques in stock price analysis, aiming to enhance prediction accuracy and inform investment decisions. Through rigorous analysis, our research demonstrates that machine learning models can effectively capture the dynamic nature of stock markets, leading to improved forecasting capabi

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Liu, Yang, Ruo Jia, Jieping Ye, and Xiaobo Qu. "How machine learning informs ride-hailing services: A survey." Communications in Transportation Research 2 (December 2022): 100075. http://dx.doi.org/10.1016/j.commtr.2022.100075.

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Wang, Yingxu, Yousheng Tian, and Kendal Hu. "Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 3 (2011): 1–29. http://dx.doi.org/10.4018/ijcini.2011070101.

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Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is

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Schwartz, Oscar. "Competing Visions for AI." Digital Culture & Society 4, no. 1 (2018): 87–106. http://dx.doi.org/10.14361/dcs-2018-0107.

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Abstract In this paper, I will investigate how two competing visions of machine intelligence put forward by Alan Turing and J. C. R Licklider - one that emphasized automation and another that emphasized augmentation - have informed experiments in computational creativity, from early attempts at computer-generated art and poetry in the 1960s, up to recent experiments that utilise Machine Learning to generate paintings and music. I argue that while our technological capacities have changed, the foundational conflict between Turing’s vision and Licklider’s vision plays itself out in generations o

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14

Hancock, Kristy. "Machine-learning Recommender Systems Can Inform Collection Development Decisions." Evidence Based Library and Information Practice 19, no. 2 (2024): 133–35. http://dx.doi.org/10.18438/eblip30521.

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A Review of: Xiao, J., & Gao, W. (2020). Connecting the dots: reader ratings, bibliographic data, and machine-learning algorithms for monograph selection. The Serials Librarian, 78(1-4), 117-122. https://doi.org/10.1080/0361526X.2020.1707599 Objective – To illustrate how machine-learning book recommender systems can help librarians make collection development decisions. Design – Data analysis of publicly available book sales rankings and reader ratings. Setting – The internet. Subjects – 192 New York Times hardcover fiction best seller titles from 2018, and 1,367 Goodreads ratings posted i

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Berk, Richard, and Jordan Hyatt. "Machine Learning Forecasts of Risk to Inform Sentencing Decisions." Federal Sentencing Reporter 27, no. 4 (2015): 222–28. http://dx.doi.org/10.1525/fsr.2015.27.4.222.

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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 864–69. http://dx.doi.org/10.22214/ijraset.2023.55224.

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Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning pr

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Sedej, Owen, Eric Mbonimpa, Trevor Sleight, and Jeremy Slagley. "Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review." Journal of Energy and Power Technology 4, no. 3 (2022): 1. http://dx.doi.org/10.21926/jept.2203027.

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Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous exper

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Masamah, Ulfa, and Dadan Sumardani. "Utilization of The Thrasher and Rice Mill Machines in Composition Function Learning: A Hypothetical Learning Trajectory Design." Hipotenusa : Journal of Mathematical Society 3, no. 2 (2021): 144–57. http://dx.doi.org/10.18326/hipotenusa.v3i2.5994.

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The study aims to design mathematics learning in composite function concepts with farm tools, which are thrasher and rice mill machines; this farm’s tool is used as to starting point in the learning process. The research method used is design research with a preliminary design, design experiment, and analysis retrospective stages. This study describes the design of the thrasher and rice mill machine to facilitate a real contribution for student understanding of the composite function concept. The participant of this research is 10 eleventh-grade students from one of the senior high school in E

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Pazzani, Michael, Severine Soltani, Robert Kaufman, Samson Qian, and Albert Hsiao. "Expert-Informed, User-Centric Explanations for Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12280–86. http://dx.doi.org/10.1609/aaai.v36i11.21491.

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We argue that the dominant approach to explainable AI for explaining image classification, annotating images with heatmaps, provides little value for users unfamiliar with deep learning. We argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We provide an expanded set of goals of explainable AI systems and propose a Turing Test for explainable AI.

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Gao, Kaifu, Dong Chen, Alfred J. Robison, and Guo-Wei Wei. "Proteome-Informed Machine Learning Studies of Cocaine Addiction." Journal of Physical Chemistry Letters 12, no. 45 (2021): 11122–34. http://dx.doi.org/10.1021/acs.jpclett.1c03133.

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Barmparis, G. D., and G. P. Tsironis. "Discovering nonlinear resonances through physics-informed machine learning." Journal of the Optical Society of America B 38, no. 9 (2021): C120. http://dx.doi.org/10.1364/josab.430206.

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Pilania, G., K. J. McClellan, C. R. Stanek, and B. P. Uberuaga. "Physics-informed machine learning for inorganic scintillator discovery." Journal of Chemical Physics 148, no. 24 (2018): 241729. http://dx.doi.org/10.1063/1.5025819.

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Kapoor, Taniya, Hongrui Wang, Alfredo Núñez, and Rolf Dollevoet. "Physics-informed machine learning for moving load problems." Journal of Physics: Conference Series 2647, no. 15 (2024): 152003. http://dx.doi.org/10.1088/1742-6596/2647/15/152003.

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Abstract This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads. The mathematical representation of the moving load considered involves a Dirac delta function, to capture the effect of the load moving across the structure. Approximating the Dirac delta function with PINNs is challenging because of its instantaneous change of output at a single

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Behtash, Mohammad, Sourav Das, Sina Navidi, Abhishek Sarkar, Pranav Shrotriya, and Chao Hu. "Physics-Informed Machine Learning for Battery Capacity Forecasting." ECS Meeting Abstracts MA2024-01, no. 2 (2024): 210. http://dx.doi.org/10.1149/ma2024-012210mtgabs.

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Batteries, recognized as effective energy storage solutions, are considered the main facilitators of the world-wide transition towards clean and renewable energy sources. Among different types of batteries, lithium-ion (Li-ion) variants offer higher energy densities and relatively longer life spans when compared to other types. Nonetheless, a primary concern with these batteries is their lifetime. Batteries undergo various degradation mechanisms under storage and use, significantly impacting their lifespan. To this end, it is crucial to predict the degradation and lifetime of Li-ion batteries

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Cele, Nomfundo, Alain Kibangou, and Walter Musakwa. "Machine Learning Analysis of Informal Minibus Taxi Driving." ITM Web of Conferences 69 (2024): 03003. https://doi.org/10.1051/itmconf/20246903003.

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This paper presents a machine learning analysis of driving behaviors in informal minibus taxis, focusing on both controlled and uncontrolled environments. Informal minibus taxis play a crucial role in urban transportation, particularly in developing countries, yet their driving patterns and safety implications remain under-explored. We utilize exploratory factor analysis to analyze data collected from smartphone GPS carried by a passenger of a minibus taxi, identifying key driving behaviors and patterns. Our study highlights significant differences in driving styles between controlled and unco

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Bai, Tao, and Pejman Tahmasebi. "Accelerating geostatistical modeling using geostatistics-informed machine Learning." Computers & Geosciences 146 (January 2021): 104663. http://dx.doi.org/10.1016/j.cageo.2020.104663.

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Lagomarsino-Oneto, Daniele, Giacomo Meanti, Nicolò Pagliana, et al. "Physics informed machine learning for wind speed prediction." Energy 268 (April 2023): 126628. http://dx.doi.org/10.1016/j.energy.2023.126628.

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Tóth, Máté, Adam Brown, Elizabeth Cross, Timothy Rogers, and Neil D. Sims. "Resource-efficient machining through physics-informed machine learning." Procedia CIRP 117 (2023): 347–52. http://dx.doi.org/10.1016/j.procir.2023.03.059.

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Yang, Shaoze. "A Study of Heart Disease Diagnosis Using Machine Learning and Data Mining." Journal of Clinical Medicine Research 5, no. 4 (2024): 565. https://doi.org/10.32629/jcmr.v5i4.3135.

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This study explores the application of machine learning and data mining techniques for the diagnosis of heart disease. We focus on the development and evaluation of various machine learning models, including logistic regression, decision trees, random forests, support vector machines, and neural networks. These models are trained and tested on a comprehensive dataset, with performance assessed using accuracy, sensitivity, specificity, and the area under the ROC curve. Additionally, data mining techniques such as association rule mining and cluster analysis are employed to uncover underlying pa

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Oneto, Luca, and Davide Chicco. "Eight quick tips for biologically and medically informed machine learning." PLOS Computational Biology 21, no. 1 (2025): e1012711. https://doi.org/10.1371/journal.pcbi.1012711.

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Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. To address this drawback, we present eight guidelines outlining bes

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Lympany, Shane V., Matthew F. Calton, Mylan R. Cook, Kent L. Gee, and Mark K. Transtrum. "Mapping ambient sound levels using physics-informed machine learning." Journal of the Acoustical Society of America 152, no. 4 (2022): A48—A49. http://dx.doi.org/10.1121/10.0015498.

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Mapping the spatial and temporal distribution of ambient sound levels is critical for understanding the impacts of natural sounds and noise pollution on humans and the environment. Previously, ambient sound levels have been predicted using either machine learning or physics-based modeling. Machine learning models have been trained on acoustical measurements at geospatially diverse locations to predict ambient sound levels across the world based on geospatial features. However, machine learning requires a large number of acoustical measurements to predict ambient sound levels at high spatial an

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Thete, Prof Sharda, Siddheshwar Midgule, Nikesh Konde, and Suraj Kale. "Malware Detection Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1942–45. http://dx.doi.org/10.22214/ijraset.2022.47682.

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Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications' access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying

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Midgule, Siddheshwar. "Malware Detection Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4755–58. http://dx.doi.org/10.22214/ijraset.2023.52704.

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Abstract: Android application security is based on permission-based mechanisms that restrict third-party Android applications’ access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well-understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying

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Chen, James Ming, Mira Zovko, Nika Šimurina, and Vatroslav Zovko. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution." International Journal of Environmental Research and Public Health 18, no. 16 (2021): 8688. http://dx.doi.org/10.3390/ijerph18168688.

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This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Line

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O'Donncha, Fearghal, and Jon Grant. "Precision Aquaculture." IEEE Internet of Things Magazine 2, no. 4 (2020): 26–30. https://doi.org/10.1109/IOTM.0001.1900033.

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Precision aquaculture is founded on a set of disparate, interconnected sensors deployed within the marine environment to monitor, analyze, interpret, and provide decision support for farm operations. Recent technological innovations facilitate aquaculture becoming part of the Internet of Things (IoT) -- modern farms are characterized by hundreds of interconnected sensors that store and serve data, interact with other sensors and devices, and connect with a fog and cloud ecosystem. We describe the implementation of the precision aquaculture concept to a number of farms in eastern Canada. The wo

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Shah, Chirag Vinalbhai. "Transforming Retail: The Impact of AI and Machine Learning on Big Data Analytics." Global Research and Development Journals 8, no. 8 (2023): 1–8. http://dx.doi.org/10.70179/grdjev09i100010.

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The abstract of this paper provides a concise overview of the significant role played by AI and machine learning in big data analytics within the retail industry. It emphasizes how these technologies are reshaping the methods through which retailers collect and analyze data to inform business decisions and enhance customer experiences. The use of AI and machine learning in the retail sector is not only transforming customer service and inventory management but also driving smart manufacturing processes and virtual merchandising. Moreover, the abstract underscores the importance of personalizat

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Siontis, Konstantinos C., Xiaoxi Yao, James P. Pirruccello, Anthony A. Philippakis, and Peter A. Noseworthy. "How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?" Circulation Research 127, no. 1 (2020): 155–69. http://dx.doi.org/10.1161/circresaha.120.316401.

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Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to det

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Lee, Jonghwan. "Physics-informed machine learning model for bias temperature instability." AIP Advances 11, no. 2 (2021): 025111. http://dx.doi.org/10.1063/5.0040100.

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Mondal, B., T. Mukherjee, and T. DebRoy. "Crack free metal printing using physics informed machine learning." Acta Materialia 226 (March 2022): 117612. http://dx.doi.org/10.1016/j.actamat.2021.117612.

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Howland, Michael F., and John O. Dabiri. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning." Energies 12, no. 14 (2019): 2716. http://dx.doi.org/10.3390/en12142716.

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical mo

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von Bloh, Malte, David Lobell, and Senthold Asseng. "Knowledge informed hybrid machine learning in agricultural yield prediction." Computers and Electronics in Agriculture 227 (December 2024): 109606. http://dx.doi.org/10.1016/j.compag.2024.109606.

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Liu, Hao-Xuan, Hai-Le Yan, Ying Zhao, et al. "Machine learning informed tetragonal ratio c/a of martensite." Computational Materials Science 233 (January 2024): 112735. http://dx.doi.org/10.1016/j.commatsci.2023.112735.

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Osorio, Julian D., Mario De Florio, Rob Hovsapian, Chrys Chryssostomidis, and George Em Karniadakis. "Physics-Informed machine learning for solar-thermal power systems." Energy Conversion and Management 327 (March 2025): 119542. https://doi.org/10.1016/j.enconman.2025.119542.

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Tartakovsky, A. M., D. A. Barajas-Solano, and Q. He. "Physics-informed machine learning with conditional Karhunen-Loève expansions." Journal of Computational Physics 426 (February 2021): 109904. http://dx.doi.org/10.1016/j.jcp.2020.109904.

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Hsu, Abigail, Baolian Cheng, and Paul A. Bradley. "Analysis of NIF scaling using physics informed machine learning." Physics of Plasmas 27, no. 1 (2020): 012703. http://dx.doi.org/10.1063/1.5130585.

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Karpov, Platon I., Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer, Stan Woosley, and Ghanshyam Pilania. "Physics-informed Machine Learning for Modeling Turbulence in Supernovae." Astrophysical Journal 940, no. 1 (2022): 26. http://dx.doi.org/10.3847/1538-4357/ac88cc.

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Abstract Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The

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Lang, Xiao, Da Wu, and Wengang Mao. "Physics-informed machine learning models for ship speed prediction." Expert Systems with Applications 238 (March 2024): 121877. http://dx.doi.org/10.1016/j.eswa.2023.121877.

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Uganya, G., I. Bremnavas, K. V. Prashanth, M. Rajkumar, R. V. S. Lalitha, and Charanjeet Singh. "Empowering autonomous indoor navigation with informed machine learning techniques." Computers and Electrical Engineering 111 (October 2023): 108918. http://dx.doi.org/10.1016/j.compeleceng.2023.108918.

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Piccialli, Francesco, Maizar Raissi, Felipe A. C. Viana, Giancarlo Fortino, Huimin Lu, and Amir Hussain. "Guest Editorial: Special Issue on Physics-Informed Machine Learning." IEEE Transactions on Artificial Intelligence 5, no. 3 (2024): 964–66. http://dx.doi.org/10.1109/tai.2023.3342563.

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Kapoor, Taniya, Abhishek Chandra, Daniel M. Tartakovsky, Hongrui Wang, Alfredo Nunez, and Rolf Dollevoet. "Neural Oscillators for Generalization of Physics-Informed Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13059–67. http://dx.doi.org/10.1609/aaai.v38i12.29204.

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A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs). This paper aims to enhance the generalization capabilities of PIML, facilitating practical, real-world applications where accurate predictions in unexplored regions are crucial. We leverage the inherent causality and temporal sequential characteristics of PDE solutions to fuse PIML models with recurrent neural architectures based on systems of ordinary differential equations

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