Opportunities and challenges in explainable artificial intelligence (xai): A survey
Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …
healthcare, self-driving vehicles, and military which have direct impact on human lives …
[HTML][HTML] Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey
Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …
and complex datasets but have been criticized as a black-box. This downside has recently …
Opportunities and challenges in deep learning adversarial robustness: A survey
SH Silva, P Najafirad - arXiv preprint arXiv:2007.00753, 2020 - arxiv.org
As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
Deep symmetric network for underexposed image enhancement with recurrent attentional learning
Underexposed image enhancement is of importance in many research domains. In this
paper, we take this problem as image feature transformation between the underexposed …
paper, we take this problem as image feature transformation between the underexposed …
The fusion of deep learning and fuzzy systems: A state-of-the-art survey
Deep learning presents excellent learning ability in constructing learning model and greatly
promotes the development of artificial intelligence, but its conventional models cannot …
promotes the development of artificial intelligence, but its conventional models cannot …
Learning from few samples: A survey
N Bendre, HT Marín, P Najafirad - arXiv preprint arXiv:2007.15484, 2020 - arxiv.org
Deep neural networks have been able to outperform humans in some cases like image
recognition and image classification. However, with the emergence of various novel …
recognition and image classification. However, with the emergence of various novel …
[HTML][HTML] Few-shot image classification algorithm based on attention mechanism and weight fusion
X Meng, X Wang, S Yin, H Li - Journal of Engineering and Applied Science, 2023 - Springer
Aiming at the existing problems of metric-based methods, there are problems such as
inadequate feature extraction, inaccurate class feature representation, and single similarity …
inadequate feature extraction, inaccurate class feature representation, and single similarity …
Fearless steps challenge (fs-2): Supervised learning with massive naturalistic apollo data
The Fearless Steps Initiative by UTDallas-CRSS led to the digitization, recovery, and
diarization of 19,000 hours of original analog audio data, as well as the development of …
diarization of 19,000 hours of original analog audio data, as well as the development of …
[HTML][HTML] An FA-SegNet image segmentation model based on fuzzy attention and its application in cardiac MRI segmentation
R Yang, J Yu, J Yin, K Liu, S Xu - International Journal of Computational …, 2022 - Springer
Aiming at the medical images segmentation with low-recognition and high background
noise, a deep convolution neural network image segmentation model based on fuzzy …
noise, a deep convolution neural network image segmentation model based on fuzzy …
AI-augmented behavior analysis for children with developmental disabilities: building toward precision treatment
Autism spectrum disorder is a developmental disorder characterized by significant social,
communication, and behavioral challenges. Individuals diagnosed with autism, intellectual …
communication, and behavioral challenges. Individuals diagnosed with autism, intellectual …