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pro vyhledávání: '"Adibi Peyman"'
Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns
Externí odkaz:
http://arxiv.org/abs/2407.21041
Meta-learning problem is usually formulated as a bi-level optimization in which the task-specific and the meta-parameters are updated in the inner and outer loops of optimization, respectively. However, performing the optimization in the Riemannian s
Externí odkaz:
http://arxiv.org/abs/2402.18605
Deep neural networks have achieved promising results in automatic image captioning due to their effective representation learning and context-based content generation capabilities. As a prominent type of deep features used in many of the recent image
Externí odkaz:
http://arxiv.org/abs/2303.10766
Publikováno v:
In Knowledge-Based Systems 9 October 2024 301
Publikováno v:
In Neurocomputing 14 September 2024 598
Publikováno v:
The Scientific World Journal, Vol 2013 (2013)
Objectives. Hepatitis B virus (HBV) is a health problem among injection drug users (IDUs) in prison. The aim of this study is to evaluate the association of factors of incarceration with HBV infection in prisoners with history of drug injection in Is
Externí odkaz:
https://doaj.org/article/4636c25b0e0e4ac8aa03b12339402807
Multimodal data provide complementary information of a natural phenomenon by integrating data from various domains with very different statistical properties. Capturing the intra-modality and cross-modality information of multimodal data is the essen
Externí odkaz:
http://arxiv.org/abs/2111.13361
Multimodal manifold modeling methods extend the spectral geometry-aware data analysis to learning from several related and complementary modalities. Most of these methods work based on two major assumptions: 1) there are the same number of homogeneou
Externí odkaz:
http://arxiv.org/abs/2105.05631
Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems. However, SVMs are likely to perform poorly in the classification of imbalanced data. The rough set theory presents a mathematical tool f
Externí odkaz:
http://arxiv.org/abs/2105.01198
Publikováno v:
In Expert Systems With Applications January 2024 235