Results 81 to 90 of about 22,071 (254)

Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study. [PDF]

Sci Rep, 2023
Lee J, Jung M, Park J, Kim S, Im Y, Lee N, Song HT, Lee YH.   +7 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Inverse synthetic aperture radar imaging using complex-value deep neural network

The Journal of Engineering, 2019
As compared with traditional ISAR imaging methods, the compressive sensing (CS)-based imaging methods can obtain high-quality images using much less under-sampled data.
ChangYu Hu, Ling Wang, Ze Li, Lingling Sun, Otmar Loffeld   +4 more
doaj   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. [PDF]

Magn Reson Med, 2021
Jafari R, Spincemaille P, Zhang J, Nguyen TD, Luo X, Cho J, Margolis D, Prince MR, Wang Y.   +8 more
europepmc   +1 more source

Justifying the prediction of major soil nutrients levels (N, P, and K) in cabbage cultivation

MethodsX
In a recent paper by Sajindra et al. [1], the soil nutrient levels, specifically nitrogen, phosphorus, and potassium, in organic cabbage cultivation were predicted using a deep learning model.
Thilina Abekoon, Hirushan Sajindra, B.L.S.K. Buthpitiya, Namal Rathnayake, D.P.P. Meddage, Upaka Rathnayake   +5 more
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Advancing Environmental Sustainability: The Role of Artificial Intelligence in Monitoring, Predicting, and Controlling Sulfur Dioxide Emissions

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Air pollution from sulfur dioxide (SO2) emissions poses significant threats to environmental sustainability, public health, and climate change efforts. Despite advances in monitoring and regulatory frameworks, traditional methods often fall short in accuracy, scalability, and efficiency.
Turkan Kopac
wiley   +1 more source