Small data machine learning in materials science

npj Computational Materials, Год журнала: 2023, Номер 9(1)

Опубликована: Март 25, 2023

Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed limitations brought data. Then, workflow learning has been introduced. Next, methods dealing with were introduced, including extraction from publications, database construction, high-throughput computations and experiments source level; modeling algorithms for imbalanced algorithm active transfer strategy level. Finally, future directions in science proposed.

Язык: Английский

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Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Advanced Functional Materials, Год журнала: 2023, Номер 33(17)

Опубликована: Фев. 15, 2023

Abstract Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability efficiency to handle nonlinear game‐playing problems unmatched by traditional simulation computing software trial‐error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist perovskite materials, transport layer electrodes. Predicting properties screening component related strong point ML. However, applications ML has only begun boom last two years, so it necessary provide a review involved technologies, application status, facing urgent challenges blueprint.

Язык: Английский

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Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2023, Номер 24(6), С. 6053 - 6064

Опубликована: Март 16, 2023

Health monitor of bogie-bearing on the train can ensure constant operation rail transit system. Since metro or other have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing bogie-bearings under variable working conditions challenging due wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for diagnosis proposed deal with above problems. A third-order tensor model established be suitable conditions. Furthermore, a density-based affinity propagation (DAP-Tensor) clustering algorithm presented identify different failures unlabeled. Train bogie public data sets were employed simulate three probable operation: high-frequency impact, change. Compared existing methods in cases, DAP-Tensor performs better identifying bearing faults Moreover, The DAP-tensor has comparable recognition rate some deep learning methods, which unsupervised characteristics show potential applications trains.

Язык: Английский

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Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery

Journal of Hazardous Materials, Год журнала: 2023, Номер 446, С. 130722 - 130722

Опубликована: Янв. 3, 2023

Язык: Английский

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Identification of Antibiotic Resistance in ESKAPE Pathogens through Plasmonic Nanosensors and Machine Learning

ACS Nano, Год журнала: 2023, Номер 17(5), С. 4551 - 4563

Опубликована: Март 3, 2023

Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention control infections. However, current techniques like genotype susceptibility testing are generally time-consuming require large-scale equipment. Herein, we develop a rapid, facile, sensitive technique determine phenotype among through plasmonic nanosensors machine learning. Key this sensor array contains gold nanoparticles functionalized with peptides differing in hydrophobicity surface charge. The can interact generate bacterial fingerprints alter plasmon resonance (SPR) spectra nanoparticles. In combination learning, it enables 12 less than 20 min an overall accuracy 89.74%. This machine-learning-based approach allows antibiotic-resistant from patients holds great promise as clinical tool biomedical diagnosis.

Язык: Английский

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A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

Information Fusion, Год журнала: 2024, Номер 108, С. 102369 - 102369

Опубликована: Март 22, 2024

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses underscored urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, use Artificial Intelligence (AI) wildfires, propelled by integration Unmanned Aerial Vehicles (UAVs) deep learning models, has created an unprecedented momentum implement develop more effective Although survey papers explored learning-based approaches wildfire, drone disaster management, risk assessment, a comprehensive review emphasizing application AI-enabled UAV systems investigating role methods throughout overall workflow multi-stage including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire fire growth modeling), post-fire tasks evacuation planning) is notably lacking. This synthesizes integrates state-of-the-science reviews research at nexus observations modeling, AI, UAVs - topics forefront advances elucidating AI performing monitoring actuation from pre-fire, through stage, To this aim, we provide extensive analysis remote sensing with particular focus on advancements, device specifications, sensor technologies relevant We also examine management approaches, monitoring, prevention strategies, well planning, damage operation strategies. Additionally, summarize wide range computer vision emphasis Machine Learning (ML), Reinforcement (RL), Deep (DL) algorithms for classification, segmentation, detection, tasks. Ultimately, underscore substantial advancement modeling cutting-edge UAV-based data, providing novel insights enhanced predictive capabilities understand dynamic behavior.

Язык: Английский

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Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107408 - 107408

Опубликована: Авг. 29, 2023

Язык: Английский

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An extensive review of hyperspectral image classification and prediction: techniques and challenges

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(34), С. 80941 - 81038

Опубликована: Март 9, 2024

Язык: Английский

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High‐temperature ablation resistance prediction of ceramic coatings using machine learning

Journal of the American Ceramic Society, Год журнала: 2024, Номер 108(1)

Опубликована: Сен. 20, 2024

Abstract Surface ablation temperature and linear rate are two crucial indicators for ceramic coatings under ultrahigh temperatures service, yet the results collection of such in process is difficult due to long‐period material preparation high‐cost test. In this work, four kinds machine learning models applied predict above indicators. The Random Forest (RF) model exhibits a high accuracy 87% predicting surface temperature, while low 60% rate. To optimize model, novel features constructed based on original by sum importance weights model. Thereafter, newly increases significantly, optimized RF improved 11%, exceeding 70% accuracy. By validation with available data experiments, demonstrates precise predictions target variables.

Язык: Английский

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A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 86, С. 101486 - 101486

Опубликована: Фев. 3, 2024

Язык: Английский

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