Results 171 to 180 of about 3,700 (193)

AE-DCNN: Autoencoder Enhanced Deep Convolutional Neural Network For Malware Classification

2021 International Conference on Intelligent Technologies (CONIT), 2021
Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware
Shashank Kumar, Shivangi Meena, Savya Khosla, Anil Singh Parihar   +3 more
openaire   +1 more source

PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020
Anomaly detection for textured surface is a key task in product quality control. In recent years, supervised deep learning approaches have begun to be applied in this field, whereas most of the approaches are usually impracticable in collecting a large scale of defect samples. To this end, this paper proposes an efficient pyramid memory autoencoder.
Haiming Yao, Dan Li, Yiwen Zhu, Wenyong Yu   +3 more
openaire   +1 more source

AE-MCCF: An Autoencoder-Based Multi-criteria Recommendation Algorithm

Arabian Journal for Science and Engineering, 2019
Recommender systems enable users to deal with the information overload problem by serving personalized predictions. Traditional recommendation techniques produce referrals for users by considering their overall opinions over items. On the other hand, users may consider several criteria while evaluating an item.
Zeynep Batmaz, Cihan Kaleli
openaire   +1 more source

SF-AE: Split Federated Autoencoder for Unsupervised IoT Intrusion Detection

Smart systems have become increasingly popular in recent years, widening the attack surface of cyber threats. Machine learning algorithms have been successfully integrated into modern security mechanisms to detect such attacks. Internet of Things (IoT) systems often have limited computational resources and are unable to execute entire machine learning ...
Augello, Andrea, De Paola, Alessandra, Giosuè, Domenico, Lo Re, Giuseppe   +3 more
openaire   +1 more source

AE-LSTM: Autoencoder with LSTM-Based Intrusion Detection in IoT

2022 International Telecommunications Conference (ITC-Egypt), 2022
Mohamed Mahmoud, Mahmoud Kasem, Abdelrahman Abdallah, Hyun Soo Kang   +3 more
openaire   +1 more source

SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection

Neural Networks
Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder
Yueyue Yao, Jianghong Ma, Shanshan Feng, Yunming Ye   +3 more
openaire   +2 more sources

Autoencoders (AE)

2021
Cao Xiao, Jimeng Sun
openaire   +1 more source

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Applied Soft Computing
Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data.
Chengjia Han, Zixin Wang, Yuguang Fu, Shirley Dyke, Adnan Shahriar   +4 more
openaire   +2 more sources

MoEP-AE: Autoencoding Mixtures of Exponential Power Distributions for Open-Set Recognition

IEEE Transactions on Circuits and Systems for Video Technology, 2023
Jiayin Sun, Hong Wang, Qiulei Dong
openaire   +1 more source

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Physica Scripta
Abstract In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed.
Lei Zhu, Wenchen Du, Liang Dong, Jinxu Wei   +3 more
openaire   +1 more source