Results 61 to 70 of about 9,325,572 (289)

KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph

Applied Sciences, 2022
Recently, the lexicon method has been proven to be effective for named entity recognition (NER). However, most existing lexicon-based methods cannot fully utilize common-sense knowledge in the knowledge graph.
Weiwei Hu, Liang He, Hanhan Ma, Kai Wang, Jingfeng Xiao   +4 more
doaj   +1 more source

Subjective Cognitive Concerns and Cognitive Trajectories in Parkinson's Disease: Biomarker Impact

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the relationship of subjective cognitive concerns (SCC) with biomarkers of Alzheimer's disease (AD), neurodegeneration, and Parkinson's disease (PD) and domain‐specific cognitive trajectories among cognitively unimpaired persons with de novo PD. Method Cognitively unimpaired participants with SCC (n = 294) and without SCC (
Francesca V. Lopez, Fareshte Erani, Katherine J. Bangen, David G. Coughlin, Douglas Galasko, Kelsey R. Thomas   +5 more
wiley   +1 more source

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Remote Sensing, 2017
This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image ...
Chu He, Xinlong Liu, Di Feng, Bo Shi, Bin Luo, Mingsheng Liao   +5 more
doaj   +1 more source

Conditional Random Fields as Recurrent Neural Networks [PDF]

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks.
Shuai Zheng 0001, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr   +7 more
openaire   +3 more sources

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

TIER: Temporal Convolutional Network Information Extractor With Conditional Random Field

IEEE Access
As data scales expand, effectively modeling long-distance dependencies has become crucial for information extraction. We propose TIER, which combines the Temporal Convolutional Network (TCN) and the Conditional Random Field (CRF). The dilated convolution
Huiwen Wu, Xiubo Zhang, Tengyuan Zhou, An Hu   +3 more
doaj   +1 more source

S³CRF: Sparse Spatial-Spectral Conditional Random Field Target Detection Framework for Airborne Hyperspectral Data

IEEE Access, 2020
Airborne hyperspectral data have both high spectral and spatial resolutions. Although the finer spatial resolution allows more abundant spatial characteristics to be exhibited, the spectral variability problem remains. However, few of the current spatial-
Shaoyu Wang, Yanfei Zhong, Ji Zhao, Xinyu Wang, Xin Hu   +4 more
doaj   +1 more source

Improving part-of-speech tagging in Amharic language using deep neural network

Heliyon, 2023
To date, several POS taggers have been introduced to facilitate the success of semantic analysis for different languages. However, the task of POS tagging becomes a bit intricate in morphologically complex languages, like Amharic.
Sintayehu Hirpassa, G.S. Lehal
doaj   +1 more source

On Independence Nuetrosophic Random Variables

, 2021
In this article we study independence neutrosophic random variables and conditioned expectation, we prove that conditional variance is equal to neutrosophic conditional variance.
Carlos Granados In this article we study independence neutrosophic random variables and conditioned expectation, we prove that conditional variance is equal to neutrosophic conditional variance. These notions are important for neutrosophic probability due to neutrosophic random variables, neutrosophic central limit theorem and neutrosophic laws of large numbers can be studied., Jos´e Sanabria   +1 more
core   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

Advanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio, Jonas Elsborg, Elif Coşkun, Andreas Burger, Pradipkumar Manubhai Leuaa, Thu Pham Anh Phan, Jin Hyun Chang, Christodoulos Chatzichristodoulou   +7 more
wiley   +1 more source