Results 131 to 140 of about 1,171,833 (351)
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems
Advanced Intelligent Discovery, EarlyView.This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingReservoir hydrocarbon detection is of great interest for reservoir characterization and quality assessment. However, deep carbonate reservoirs exhibit weak seismic response features, making it extremely difficult to extract and utilize reservoir ...
Jun Wang +3 more
doaj +1 more source
Unsupervised Machine Learning in Astronomy
This review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables the researchers to analyze large, high-dimensional, and unlabeled data sets and is sometimes considered more helpful for exploratory analysis because it is not limited by present knowledge and can therefore be
Chih-Ting Kuo, Duo Xu, Rachel Friesen
openaire +1 more source
Automatic Determination of Quasicrystalline Patterns from Microscopy Images
Advanced Intelligent Discovery, EarlyView.This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender +2 more
wiley +1 more source
Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning
The Seismic RecordSeismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments.
Chengping Chai +6 more
doaj +1 more source
Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data [PDF]
, 2023 Mario Zanfardino +9 more
openalex +1 more source
Advanced Intelligent Discovery, EarlyView.A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia +3 more
wiley +1 more source