Results 191 to 200 of about 73,492 (280)

Exact Learning of Augmented Naive Bayes Classifiers

Exact Learning of Augmented Naive Bayes Classifiers
2022
openaire  

Frontline Workers and Civic Tech: Bridging the Responsiveness Gap in Digital Client Encounters

Public Administration Review, EarlyView.
ABSTRACT As governments increasingly digitalize client encounters, there are growing concerns that standardized platforms may reduce bureaucratic responsiveness, particularly for historically underserved communities. We examine whether frontline workers help close that gap through their use of civic‐tech platforms.
Gregory A. Porumbescu, Vishal Trehan, Agbonlahor Edomwonyi   +2 more
wiley   +1 more source

An approach for handling imbalanced datasets using borderline shifting. [PDF]

Sci Rep
Malhat MG, Elsobky AM, Keshk AE, Abdallah HA, Hussein M.   +4 more
europepmc   +1 more source

Sensitivity analysis for generalized estimating equation with non‐ignorable missing data

Scandinavian Journal of Statistics, EarlyView.
Abstract Many incomplete‐data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real‐data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed
Hui Gong, Kin Wai Chan
wiley   +1 more source

Improving Laboratory Continuous Professional Development Training in Africa: The ASLM Academy Experience

Tropical Medicine &International Health, EarlyView.
ABSTRACT Objectives To examine the uptake rates of the African Society for Laboratory Medicine (ASLM) Academy Continuous Professional Development (CPD) programmes training courses by laboratory professionals within the African region and explore factors influencing participation.
Nqobile Ndlovu, Rajiv T. Erasmus, Silver Mashate, Maina Michael Waweru, Annalise E. Zemlin   +4 more
wiley   +1 more source

Comparison Study of Different Feature Selection Techniques for the Diagnosis of Alzheimer's Disease. [PDF]

Am J Alzheimers Dis Other Demen
Masood F, Al-Hyari A, Al-Wajidi W, Alzheimer’s Disease Neuroimaging Initiative.   +3 more
europepmc   +1 more source

Processing Fluency and Predictive Processing: How the Predictive Mind Becomes Aware of its Cognitive Limitations

Topics in Cognitive Science, EarlyView.
Abstract Predictive processing is an influential theoretical framework for understanding human and animal cognition. In the context of predictive processing, learning is often reduced to optimizing the parameters of a generative model with a predefined structure. This is known as Bayesian parameter learning.
Philippe Servajean, Wanja Wiese
wiley   +1 more source

Capturing sequence ambiguity among taxa in a primer-specific manner to improve taxonomic classification of amplicon sequencing. [PDF]

Nucleic Acids Res
Nearing JT, Thompson KN, Kuntz T, Louyakis AS, Bhosle A, Branck T, Badri DV, Franzosa EA, Brockel C, Huttenhower C, Short MI.   +10 more
europepmc   +1 more source

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding ...
Haley Schuhl, Keely E. Brown, Hudanyun Sheng, Parag K. Bhatt, Jorge Gutierrez, Dominik Schneider, Anna L. Casto, Lucia Acosta‐Gamboa, Joe G. Ballenger, Fabio Barbero, Jackson Braley, Autumn M. Brown, Leonardo Chavez, Shannon Cunningham, Malinda Dilhara, Adam M. Dimech, Joseph G. Duenwald, Annika Fischer, Jared M. Gordon, Chloe Hendrikse, Gabriela L. Hernandez, John G. Hodge, Martina Huber, Brandon M. Hurr, Sanaz Jarolmasjed, Karina Medina Jimenez, Samuel Kenney, Grant Konkel, Alexander Kutschera, Sunita Lama, Matthew Lohbihler, Argelia Lorence, Collin Luebbert, Nathaniel Ly, Heather K. Manching, Annarita Marrano, Susan Meerdink, Nicholas M. Miklave, Pavan Mudrageda, Katherine M. Murphy, J. David Peery, Ronald Pierik, Seth Polydore, Caleb Robey, Tess Rogers, Tyler J. Schultz, Eliza Seigel, Dhiraj Srivastava, Stephan Summerer, Josh Sumner, Chong Teng, Adriane E. Thompson, Jose C. Tovar, Tim van Daalen, Mark Watson, John J. Wheeler, Mark C. Wilson, Kaitlyn R. Ying, Alina Zare, Yutai Zhou, Malia A. Gehan, Noah Fahlgren   +61 more
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source