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PAMPAS: real-valued graphical models for computer vision

Michael Isard • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (2003)

Probabilistic models have been adopted for many computer vision applications, however inference in high-dimensional spaces remains problematic. As the state-space of a model grows, the dependencies between the dimensions lead to an exponent...

OPENALEX DOI: https://doi.org/10.1109/cvpr.2003.1211410

Robust Techniques for Computer Vision

Peter Meer, B. Tordoff, P. H. S. Torr, C. Davidson et al. (2004)

ysis 1995, pages 191--208. AddisonWesley Longman, 1996. [116] R. H. Zamar. Robust estimation in the errors-in-variables model. Biometrika, 76:149-- 160, 1989. [117] Z. Zhang. Determining the epipolar geometry and its uncertainty: A review. ...

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Knowledge, attitude, and practice among kindergarten teachers regarding vision and eye screening of preschool children

Dora Hamad S. AlHarkan • Oman Journal of Ophthalmology (2024)

PURPOSE To estimate the level and determinants of knowledge, attitude, and practices (KAPs) of kindergarten teachers regarding vision and eye screening of preschool children of Qassim region of Saudi Arabia. METHODS As a part of a study to ...

DOAJ Open Access (DOAJ) DOI: 10.4103/ojo.ojo_260_23

AnimalTA: A highly flexible and easy‐to‐use program for tracking and analysing animal movement in different environments

Violette Chiara, Sin‐Yeon Kim • Methods in Ecology and Evolution (2023)

Abstract Computer programs for video tracking of animal movement are evolving increasingly efficient, using complex algorithms or artificial intelligence systems. Despite the consequent progress in this field, researchers still face some fu...

DOAJ Open Access (DOAJ) DOI: 10.1111/2041-210X.14115

Spatiotemporal patterns induced by four mechanisms in a tussock sedge model with discrete time and space variables

You Li, Jingjing Cao, Ying Sun, Dan Song et al. • Advances in Difference Equations (2021)

Abstract In this paper, we investigate the spatiotemporal patterns of a freshwater tussock sedge model with discrete time and space variables. We first analyze the kinetic system and show the parametric conditions for flip and Neimark–Sacke...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13662-021-03557-9

Evaluating the ergonomic deficiencies in computer workstations and investigating their correlation with reported musculoskeletal disorders and visual symptoms among computer users in Bangladeshi university

Md Golam Kibria, Md Shohel Parvez, Palash Saha, Subrata Talapatra • Heliyon (2023)

Background: Nowadays, computer users are facing musculoskeletal disorders (MSDs) and visual symptoms. Prolonged sitting in inappropriate, awkward, and static postures on the computer workstation may cause musculoskeletal disorders (MSDs). S...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.heliyon.2023.e22179

Bayesian Regularization of Neural Networks

Frank R. Burden, David Alan Winkler • Methods in molecular biology (2008)

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...

OPENALEX DOI: https://doi.org/10.1007/978-1-60327-101-1_3

Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France

Timothée Corchia, Bertrand Bonan, Nemesio Rodríguez-Fernández, Gabriel Colas et al. • Remote Sensing (2023)

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-M...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs15174258

Adjustment of the Minimum Spouting Velocity in a Conical Spouted Bed from Artificial Neural Networks

Juan F. Saldarriaga, Fabio Freire, Jose Freire • Chemical Engineering Transactions (2018)

The minimum spouting velocity is one of the fundamental parameters in the application of the conical spouted bed pyrolysis, gasification and combustion, therefore it is necessary the adequate calculation and the use of a correlation with go...

DOAJ Open Access (DOAJ) DOI: 10.3303/CET1870208

Predicting air quality index using artificial neural network: a case study of the Coimbatore region

P. Jagadesh, J. Harshini, D. Arul, Solomon Oyebisi et al. • Discover Civil Engineering (2026)

Abstract This study predicts the Air Quality Index (AQI) in the Coimbatore region using Artificial Neural Networks (ANN) to enhance air quality management and environmental science. The researchers measured six input variables over a two-ye...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44290-026-00423-z

neuralnet: Training of Neural Networks

Frauke Günther, Stefan Gerd Fritsch • The R Journal (2010)

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

OPENALEX Open Access DOI: https://doi.org/10.32614/rj-2010-006

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Su-Hsin Huang, Joon-Khim Loh, Jinn-Tsong Tsai, Ming-Feng Houg et al. • Chinese Journal of Cancer (2017)

Abstract Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40880-017-0192-9

Artificial Neural Networks

Hojjat Adeli, Xiaomo Jiang • CRC Press eBooks (2008)

An artificial neural network (ANN) is a functional abstraction of the biologic neural structures of the central nervous system (Aleksander and Morton 1993; Rudomin et al. 1993; Arbib 1995; Anderson 1995). It is essentially a simple mathemat...

OPENALEX DOI: https://doi.org/10.1201/9781482281767-9

Spatial analysis and risk mapping of Fasciola hepatica infection in dairy cattle at the Peruvian central highlands

Daniel Alexis Zárate-Rendón, David Godoy Padilla, Samuel Pizarro Carcausto, Alberto del Águila et al. • Parasite Epidemiology and Control (2023)

This study aimed to develop maps for Fasciola hepatica infection occurrence in dairy cattle in the districts of Matahuasi and Baños in the Peruvian central highlands. For this, a model based on the correlation between environmental variable...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.parepi.2023.e00329

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

Understanding the role of autoencoders for stiff dynamical systems using information theory

Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im • Energy and AI (2025)

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....

DOAJ Open Access (DOAJ) DOI: 10.1016/j.egyai.2025.100567

Scalable and accurate deep learning with electronic health records

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai et al. • npj Digital Medicine (2018)

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variabl...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0029-1 PMC: 6550175