Results 151 to 160 of about 38,822 (259)

Some new quantitative randomized response models using optional and partial scrambling for sensitive data. [PDF]

Sci Rep
Iqbal S, Hussain Z, Omer T.
europepmc   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Dictionary Learning Methods for Brain Activity Mapping with MEG Data. [PDF]

Brain Topogr
Calvetti D, Somersalo E.
europepmc   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

A Fast-Fourier-Transform-Based Dynamic Likelihood Ratio Framework for Controlling False Positives in DNA Database Matching. [PDF]

Genes (Basel)
Laurent FX, Burgers W, Wiegerinck W, Gout C, Hitchin S.   +4 more
europepmc   +1 more source

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

Advanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna, Hugo A. Ordoñez Erazo, Carlos A. Cobos Lozada   +2 more
wiley   +1 more source

Ambiguity and free will: the topology of decision in quantum and quantum-like sciences. [PDF]

Philos Trans A Math Phys Eng Sci
Plotnitsky A.
europepmc   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang, Zichao Wang, Zhiwei He, Qingquan Wei, Yong Zuo, Linzhen Nie, Zhishuai Yin   +6 more
wiley   +1 more source

Plasma lactate and severity of illness for acute pulmonary embolism: A Bayesian probability model.

Int J Crit Illn Inj Sci
Báez AA, Ibu F, Halasz H.
europepmc   +1 more source

Assessing SARS-CoV-2 transmission in African households from the reanalysis of serosurveys. [PDF]

Am J Epidemiol
Cristancho-Fajardo L, Nkuba-Ndaye A, Simons E, Hozé N, Guichet E, Izia YA, Ndongo FA, Mbala-Kingebeni P, Soumah AA, Muzinga J, Tshiminyi-Munkamba P, Diallo MSK, Bissek AZ, Makiala-Mandanda S, Toure A, Ahuka-Mundeke S, Ayouba A, Etard JF, Peeters M, Roche B, Nikolay B, Delaporte E, Cauchemez S.   +22 more
europepmc   +1 more source