Results 191 to 200 of about 234,090 (282)

Neural mechanisms of flexible perceptual inference. [PDF]

PLoS Comput Biol
Schwarcz J, Bauer J, Rajabi H, Marmur G, Reiner R, Lottem E, Kadmon J.   +6 more
europepmc   +1 more source

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Automating wastewater characteristic parameter quantitation using neural architecture search in AutoML systems on spectral reflectance data. [PDF]

Sci Rep
Ankalaki S.
europepmc   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

Bayesian deep learning for probabilistic aquifer vulnerability and uncertainty prediction. [PDF]

Sci Rep
Mengistu TD, Kim MG, Chung IM, Chang SW.
europepmc   +1 more source

Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing

Carbon Energy, EarlyView.
Considering complex process parameters and poor reproducibility in perovskite thin film fabrication, this study uses machine learning to analyze and predict high‐dimensional process variables. The Random Forest model, identified as the most effective, can effectively analyze and rapidly predict optimal process parameters from extensive data.
Hong Liu, Kangyan Liu, Biao Zhang, Ziang Chen, Yi Yang, Qiang Sun, Tao Ye, Bed Poudel, Kai Wang, Congcong Wu   +9 more
wiley   +1 more source

Federated TriNet-AQ: Explainable english proficiency classification in augmented and virtual reality learning. [PDF]

PLoS One
Zhang C, Liu Z.
europepmc   +1 more source

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Chemistry – A European Journal, EarlyView.
Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain, Max Pinheiro Jr., Matheus de Oliveira Bispo, Mario Barbatti   +3 more
wiley   +1 more source

Designing a novel radial basis neural structure for solving the dynamical hepatitis C virus model. [PDF]

Sci Rep
Sabir Z, Yessengaliyev A, Temirzhan A, Koyunbakan H, Bhatti S, Nicolas R.   +5 more
europepmc   +1 more source

An observation‐driven state‐space model for claims size modelling

Canadian Journal of Statistics, EarlyView.
Abstract State‐space models are popular in econometrics. Recently, these models have gained some popularity in the actuarial literature. The best known state‐space models are of the Kalman‐filter type. These are called parameter‐driven because the observations do not impact the state‐space dynamics.
Jae Youn Ahn, Himchan Jeong, Mario V. Wüthrich   +2 more
wiley   +1 more source