Network attacks detection methods based on deep learning techniques: a survey
With the development of the fifth‐generation networks and artificial intelligence
technologies, new threats and challenges have emerged to wireless communication system …
technologies, new threats and challenges have emerged to wireless communication system …
Big data and AI-driven product design: A survey
H Quan, S Li, C Zeng, H Wei, J Hu - Applied Sciences, 2023 - mdpi.com
As living standards improve, modern products need to meet increasingly diversified and
personalized user requirements. Traditional product design methods fall short due to their …
personalized user requirements. Traditional product design methods fall short due to their …
[HTML][HTML] Unsupervised learning approach in defining the similarity of catchments: Hydrological response unit based k-means clustering, a demonstration on Western …
E Aytaç - International soil and water conservation research, 2020 - Elsevier
This study investigated the similarity of the catchments with the k-means clustering method
by using the hydrological response unit (HRU) images of 33 catchments located in the …
by using the hydrological response unit (HRU) images of 33 catchments located in the …
TRCA-net: using TRCA filters to boost the SSVEP classification with convolutional neural network
Objective. The steady-state visual evoked potential (SSVEP)-based brain–computer
interface has received extensive attention in research due to its simple system, less training …
interface has received extensive attention in research due to its simple system, less training …
Adaptive multi-granularity sparse subspace clustering
Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic
perspectives and has been widely applied to high-dimensional data. The key to SSC is to …
perspectives and has been widely applied to high-dimensional data. The key to SSC is to …
Anomaly Event Detection in Security Surveillance Using Two‐Stream Based Model
Anomaly event detection has been extensively researched in computer vision in recent
years. Most conventional anomaly event detection methods can only leverage the single …
years. Most conventional anomaly event detection methods can only leverage the single …
SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning
Y Dong, H **ao, Y Dong - Neurocomputing, 2022 - Elsevier
Imbalanced data can always be observed in our daily life and various practical tasks. A lot of
well-constructed machine learning methodologies may produce ineffective performance …
well-constructed machine learning methodologies may produce ineffective performance …
The defense of adversarial example with conditional generative adversarial networks
Deep neural network approaches have made remarkable progress in many machine
learning tasks. However, the latest research indicates that they are vulnerable to adversarial …
learning tasks. However, the latest research indicates that they are vulnerable to adversarial …
DMGAN: Discriminative metric-based generative adversarial networks
With the proposed of Generative Adversarial Networks (GANs), the generative adversarial
models have been extensively studied in recent years. Although probability-based methods …
models have been extensively studied in recent years. Although probability-based methods …
Fine-grained predicting urban crowd flows with adaptive spatio-temporal graph convolutional network
X Yang, Q Zhu, P Li, P Chen, Q Niu - Neurocomputing, 2021 - Elsevier
Predicting crowd flows is important for traffic management and public safety, which is very
challenging as it is affected by many complex factors. In this paper, we propose a novel fine …
challenging as it is affected by many complex factors. In this paper, we propose a novel fine …