Results 111 to 120 of about 8,505,406 (249)

Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting

open access: yesFrontiers in Blockchain
The increasing occurrence of structural breaks, financial crises, and economic uncertainty has significantly weakened the predictive performance of traditional macroeconomic forecasting models.
Oumaima Abouzaid, Faouzi Boussedra
doaj   +1 more source

Variational multiple shooting for Bayesian ODEs with Gaussian processes

open access: yes, 2022
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative solutions or point estimates.
Yildiz, Cagatay   +5 more
core  

Advancing Human Skin Equivalents: The Crucial Role of Neurovascular Integration

open access: yesAdvanced Healthcare Materials, EarlyView.
This review discusses the importance of integrating vascular and peripheral nerve systems into human skin equivalents (HSEs) to better recapitulate native skin physiology. Recent advances in vascularized, innervated, and neurovascularized HSEs are highlighted, together with emerging bioengineering strategies, current challenges, and future ...
Hao Wu   +4 more
wiley   +1 more source

Hybridization of machine learning/GPR Full-Waveform Inversion for pavement tack coat characterization: Numerical validation

open access: yesTransportation Engineering
The characterization of tack coat layers in pavements using conventional techniques, such as forward processing applied to ground-penetrating radar (GPR), is challenging due to the low temporal resolution of raw signals, among other factors.
Grégory Andreoli   +2 more
doaj   +1 more source

Gaussian Processes with Bayesian Inference of Covariate Couplings

open access: yes
Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Gaussian processes are powerful probabilistic models that are often coupled with Automatic Relevance Determination (ard) capable of uncovering the importance of ...
Rosso, Mattia   +4 more
core  

Review: machine learning techniques applied to cybersecurity

open access: yes, 2019
Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization.
Paulino J. García-Nieto   +8 more
core   +1 more source

Blood–Brain Barrier Regulation: Evolving From Classic Strategies to Electrochemical Ion and Reactive Oxygen Species Control

open access: yesAdvanced Healthcare Materials, EarlyView.
Conventional therapies suffer from poor blood–brain barrier (BBB) penetration and disordered ion/reactive oxygen species (ROS) homeostasis, hindering precise neurological treatment. Electrochemical strategies achieve accurate spatiotemporal regulation of neural homeostasis.
Xiaokang Hu   +5 more
wiley   +1 more source

Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity

open access: yesPlants
The extent of single- and multi-cropping systems in any region, as well as potential changes to them, has consequences on food security and land- and water-resource use, raising important management questions.
Hossein Noorazar   +8 more
doaj   +1 more source

Advances in 3D Bioprinting for Breast Cancer Modeling: Tumor Microenvironment, Metastasis, and Drug Screening

open access: yesAdvanced Healthcare Materials, EarlyView.
Three‐dimensional bioprinting transforms breast cancer modeling from simple constructs into spatially organized, multicellular, perfused, and patient‐derived platforms. This review shows how bioinks, cellular composition, architecture, and flow govern tumor behavior and drug response, while revealing why complexity alone is insufficient without ...
Mustafa Nakipoglu   +7 more
wiley   +1 more source

Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning

open access: yes
Publisher Copyright: © 2026, Transactions on Machine Learning Research. All rights reserved.Natural gradients have long been studied in deep reinforcement learning due to their fast convergence properties and covariant weight updates.
Dash, Satya Prakash   +4 more
core  

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