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Journal articles on the topic 'Machine Learning Informé'

Author: Grafiati

Published: 21 December 2024

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1

Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 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. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.

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Pateras, Joseph, Pratip Rana, and Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning." Applied Sciences 13, no. 12 (June 7, 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 traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics problems are served by the physics-informed learning machines and assisting in identifying suitable targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

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Minasny, 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 (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 (April 1, 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 (September 15, 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 machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

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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 (November 2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.

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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 capabilities. The results indicate a significant enhancement in prediction accuracy, suggesting that these techniques could significantly contribute to financial analysis. The significance of this study lies in its potential to revolutionize stock market analysis. By harnessing the predictive power of machine learning, investors can make more informed decisions, reduce risks, and enhance returns. This not only benefits individual investors but also contributes to the overall stability and efficiency of financial markets. Machine Learning, Stock Price Analysis, Prediction Accuracy, Financial Markets, Investment Decision-Making. The methods employed in this study include the utilization of various machine learning algorithms such as support vector machines and neural networks, as well as the application of statistical techniques for data analysis and validation.

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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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11

Schwartz, Oscar. "Competing Visions for AI." Digital Culture & Society 4, no. 1 (March 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 of programmers and artists who explore the computer’s creative potential. Moreover, I will demonstrate that this conflict does not only inform technical/artistic practice, but speaks to a deeper philosophical and ideological divide concerning the narrative of a post-human future. While Turing’s conception of human-equivalent AI informs a transhumanist imaginary of super-intelligent, conscious, anthropomorphic machines, Licklider’s vision of symbiosis underpins formulations of the cyborg as human-machine hybrid, aligning more closely with a critical post-human imaginary in which boundaries between the human and technological become mutable and up for re-negotiation. In this article, I will explore how one of the functions of computational creativity is to highlight, emphasise and sometimes thematise these conflicting post-human imaginaries.

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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 (July 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 formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

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Pandey, Mrs Arjoo. "Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 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 processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.

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Hancock, Kristy. "Machine-learning Recommender Systems Can Inform Collection Development Decisions." Evidence Based Library and Information Practice 19, no. 2 (June 14, 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 in 2018. Methods – Data were collected using Application Programming Interfaces. The researchers retrieved weekly hardcover fiction best seller rankings published by the New York Times in 2018 in CSV file format. All 52 files, each containing bibliographic data for 15 hardcover fiction titles, were combined and duplicate titles removed, resulting in 192 unique best seller titles. The researchers retrieved reader ratings of the 192 best seller titles from Goodreads. The ratings were limited to those posted in 2018 by the top Goodreads reviewers. A Bayes estimator produced a list of the top ten highest rated New York Times best sellers. The researchers built the recommender system using Python and employed several content-based and collaborative filtering recommender techniques (e.g., cosine similarity, term frequency-inverse document frequency, and matrix factorization algorithms) to identify novels similar to the highest rated best sellers. Main Results – Each recommender technique generated a different list of novels. Conclusion – The main finding from this study is that recommender systems can simplify collection development for librarians and facilitate greater access to relevant library materials for users. Academic libraries can use the same recommender techniques employed in the study to identify titles similar to highly circulated monographs or frequently requested interlibrary loans. There are several limitations to using recommender systems in libraries, including privacy concerns when analyzing user behaviour data and potential biases in machine-learning algorithms.

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Berk, Richard, and Jordan Hyatt. "Machine Learning Forecasts of Risk to Inform Sentencing Decisions." Federal Sentencing Reporter 27, no. 4 (April 1, 2015): 222–28. http://dx.doi.org/10.1525/fsr.2015.27.4.222.

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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 (February 18, 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 experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.

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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 (December 28, 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 East Java. The results of this study reveal that students are able to make associations from the thrasher and rice mill machine through the determination of the input and output of the machines to the formula of the composite function concept. So, the stages in the learning trajectory have an important role in understanding the composition function concept from informal level to formal level and also make the study of mathematics more easy, simple, fun, and comfortable.

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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 (June 28, 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 (November 9, 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 (August 2, 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 (June 28, 2018): 241729. http://dx.doi.org/10.1063/1.5025819.

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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, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco, and Agnese Seminara. "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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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 (June 1, 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 point, causing difficulty in the convergence of the loss function. We propose to approximate the Dirac delta function with a Gaussian function. The incorporated Gaussian function physical equations are used in the physics-informed neural architecture to simulate beam deflections and to predict the magnitude of the load. Numerical results show that PIML is an effective method for simulating the forward and inverse problems for the considered model of a moving load.

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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 (August 9, 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 under given conditions. Researchers use three main methodologies to perform battery health diagnostics and to predict the lifetime of batteries. One approach revolves around using mechanistic, first-principle electrochemical models, also known as physics-based models. If equipped with proper thermodynamic theories, such models can show promising capabilities; however, holistic degradation prediction with these models is still challenging due to the computational complexities and the multitude of parameters that need to be fine-tuned in this approach. The other common strategy is to use empirical models to predict battery degradation. These models generally entail fewer parameters to be identified and are computationally less intensive to solve. Nonetheless, empirical models can suffer from the accuracy point-of-view as they are constrained to predict degradation trends introduced by certain degradation modes. Another common technique in battery life prediction is to utilize purely data-driven methods, such as machine learning (ML) algorithms, which also have shown promising results in the literature on rapid health predictions. However, these methods require large volumes of experimental data for training and testing ML models to ensure accuracy. In addition, data-driven methods are likely to extrapolate poorly to conditions beyond their training data and are indifferent towards the underlying degradation mechanisms. Recently, physics-informed machine learning (PI-ML) methods have garnered significant attention. They integrate physics-based or empirical models (developed based on physics) with a data-driven approach and allow one to train ML models on a smaller set of experimental data. To the best of the authors’ knowledge, the performance comparison between first-principle and empirical models when integrated within PI-ML remains unclear. Therefore, in this work, we aim to compare these two models when applied to prognostics (capacity forecasting and remaining useful life prediction) of a set of Li-ion batteries. To perform this study, we generate aging data for 40 Li-ion coin cells cycled under randomized conditions. Each cell undergoes a three-step charging stage followed by a two-step discharge stage. After obtaining the aging data, we will develop two PI-ML models, one equipped with a physics-based model and another with a set of empirical models. Both PI-ML models in this work will follow the sequential integration approach, where the training data for the final PI-ML model come from both experimental and computational data, the latter of which are obtained from the physics-based or empirical models. The parameters for the physics-based and empirical models are identified from another set of experimental data. Finally, the PI-ML models will be tested with experimental data obtained at different cycling conditions. The data flow for the sequential architecture of PI-ML is shown in the attached figure. This comparative study will help identify the performance of physics-based and empirical models when integrated into PI-ML. The main performance metric considered in this work is each model’s ability to extrapolate beyond the experimental training data set, hence aiding the final PI-ML model in generalizing to conditions not covered by its experimental training data set. Figure 1

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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 uncontrolled environments, offering insights into safety and efficiency. The findings provide valuable information for policymakers, transportation planners, and technology developers aiming to enhance urban mobility and safety in the informal transport sector.

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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 (November 30, 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 on developers to You are accepting the install without trying to analyze your intent.

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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 (May 31, 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 on developers to You are accepting the install without trying to analyze your intent.

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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 (October 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 and temporal resolution. Physics-based models have been applied to predict transportation noise at high spatial and temporal resolution on regional scales, but these predictions do not include other anthropogenic, biological, or geophysical sound sources. In this work, physics-based predictions of transportation noise are combined with machine learning models to predict ambient sound levels at high spatial and temporal resolution across the conterminous United States. The physics-based predictions of transportation noise are incorporated into the machine learning models as a geospatial feature. The result is a physics-informed machine learning model that predicts ambient sound levels at high spatial and temporal resolution across the United States. [Work funded by an Army SBIR]

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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 (August 17, 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. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM2.5 pollution.

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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 (August 1, 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 personalization in achieving business success and highlights how AI and ML enable retailers to monitor fashion trends and individual customer purchasing behavior. In addition, the abstract points to the potential for further research and growth in the field of fashion technology, indicating the expanding influence of AI and machine learning in the retail sector. The paper will further explore the practical applications and implications of these technologies in big data analytics within the retail industry, building upon the insights provided in the abstract.

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Ravi, Aravind. "Optimizing Retail Operations: The Role of Machine Learning and Big Data in Data Science." Global Research and Development Journals 9, no. 6 (June 5, 2024): 1–10. http://dx.doi.org/10.70179/grdjev09i100015.

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In the rapidly evolving retail landscape, optimizing operations has become critical to maintaining competitive advantage and enhancing customer satisfaction. This paper explores the transformative impact of machine learning (ML) and big data analytics on retail operations. By leveraging advanced data science techniques, retailers can gain actionable insights into inventory management, customer behavior, and supply chain efficiencies. Machine learning algorithms facilitate predictive analytics, enabling accurate demand forecasting and personalized marketing strategies. Big data analytics, on the other hand, empowers retailers to process vast amounts of information to uncover patterns and trends that inform strategic decisions. This paper reviews the integration of ML and big data in retail operations, highlighting case studies that demonstrate their practical applications and benefits. Key challenges such as data privacy, integration complexities, and the need for specialized skills are also discussed. The findings underscore the potential of these technologies to revolutionize retail operations, offering a framework for practitioners to harness their capabilities effectively.

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K., Mrs Tejaswi. "Unmasking DeepFakes Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 30, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29808.

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Our task objectives to deal with the trouble of Deepfake videos, which can be inflicting fear because of their capacity to unfold fake records and manage human beings. Deepfake veideos appearance so actual that it`s tough to inform them other than actual ones, making them a severe danger. This sensible look can result in sizable damage .We are using Machine Learning Algorithms discover Deepfake videos. This entails the usage of era to differentiate among actual and manipulated content. By growing this, we lessen the effect of incorrect information and manipulation, safeguarding human beings from cappotential damage as a result of deceptive content. Keywords: Deepfake Detection, Machine Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Facial Analysis, Anomaly Detection, Synthetic Media, Image and Video Analysis, Temporal and Spatial Patterns, Training Datasets, Real-time Detection, Visual Content Security

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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 (June 19, 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 detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

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Lee, Jonghwan. "Physics-informed machine learning model for bias temperature instability." AIP Advances 11, no. 2 (February 1, 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 (July 16, 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 models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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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 (January 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 (November 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 applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.

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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 (March 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 (March 24, 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, referred to as neural oscillators. Through effectively capturing long-time dependencies and mitigating the exploding and vanishing gradient problem, neural oscillators foster improved generalization in PIML tasks. Extensive experimentation involving time-dependent nonlinear PDEs and biharmonic beam equations demonstrates the efficacy of the proposed approach. Incorporating neural oscillators outperforms existing state-of-the-art methods on benchmark problems across various metrics. Consequently, the proposed method improves the generalization capabilities of PIML, providing accurate solutions for extrapolation and prediction beyond the training data.

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Marian, Max, and Stephan Tremmel. "Physics-Informed Machine Learning—An Emerging Trend in Tribology." Lubricants 11, no. 11 (October 30, 2023): 463. http://dx.doi.org/10.3390/lubricants11110463.

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Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods.

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Liu, Hao-Xuan, Hai-Le Yan, Ying Zhao, Nan Jia, Shuai Tang, Daoyong Cong, Bo Yang, 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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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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Cheraghlou, Shayan, Praneeth Sadda, George O. Agogo, and Michael Girardi. "A machine‐learning modified CART algorithm informs Merkel cell carcinoma prognosis." Australasian Journal of Dermatology 62, no. 3 (May 24, 2021): 323–30. http://dx.doi.org/10.1111/ajd.13624.

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Gao, Junbo. "Applications of machine learning in quantitative trading." Applied and Computational Engineering 82, no. 1 (November 8, 2024): 124–29. http://dx.doi.org/10.54254/2755-2721/82/20240984.

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Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

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