Results 1 to 10 of about 3,896 (184)

AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement

Applied Sciences, 2023
With the continuous expansion of the darknet and the increase in various criminal activities in the darknet, darknet traffic identification has become increasingly essential.
Tao Yang, Rui Jiang, Hongli Deng, Qinru Li, Ziyu Liu   +4 more
doaj   +2 more sources

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors, 2019
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction
Huadong Ma, Honghai Wu, Wu Honghai
exaly   +3 more sources

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Algorithms, 2019
Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision.
Pei-Yin Chen, Jih-Jeng Huang, Huang Jih-Jeng   +2 more
exaly   +3 more sources

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study [PDF]

JMIR Medical Informatics
BackgroundBy analyzing electronic health record snapshots of similar patients, physicians can proactively predict disease onsets, customize treatment plans, and anticipate patient-specific trajectories.
Deyi Li, Aditi Shukla, Sravani Chandaka, Bradley Taylor, Jie Xu, Mei Liu   +5 more
doaj   +2 more sources

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder [PDF]

Scientific Reports
In this paper, we propose a new hybrid model, called AVE, that integrates the strengths of Autoencoder (AE) and Variational Autoencoder (VAE) to enhance outlier detection for numerous high-dimensional datasets.
Ahmed M. Daoud, Osama M. Elkomy, Walid I. Khedr, Khalid M. Hosny   +3 more
doaj   +2 more sources

Dimensionality Reduction of Human Gait for Prosthetic Control

Frontiers in Bioengineering and Biotechnology, 2021
We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data.
David Boe, Alexandra A. Portnova-Fahreeva, Abhishek Sharma, Vijeth Rai, Astrini Sie, Pornthep Preechayasomboon, Eric Rombokas, Eric Rombokas   +7 more
doaj   +1 more source

Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification

Earth and Space Science, 2022
Deep autoencoder (AE) networks show a powerful ability for geochemical anomaly identification. Because of little contribution to the AE network, small probability samples (again, please check this) having comparatively high reconstructed errors can be ...
Bin Feng, Lirong Chen, Yongyang Xu, Yu Zhang   +3 more
doaj   +1 more source

Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network

Frontiers in Genetics, 2023
Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial ...
Xinxing Li, Wendong Huang, Xuan Xu, Hong-Yu Zhang, Qianqian Shi   +4 more
doaj   +1 more source

Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

IEEE Photonics Journal, 2021
This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE
Po-Han Chiu, Yu-Shen Lin, Yibeltal Chanie Manie, Jyun-Wei Li, Ja-Hon Lin, Peng-Chun Peng   +5 more
doaj   +1 more source

Early Gas Kick Warning Based on Temporal Autoencoder

Energies, 2023
The timing of the data is not taken into account by the majority of risk warnings today. However, identifying temporal fluctuations in data, which is a vital method for detecting risk, is neglected by the majority of intelligent gas kick warning models ...
Zhaopeng Zhu, Detao Zhou, Donghan Yang, Xianzhi Song, Mengmeng Zhou, Chengkai Zhang, Shiming Duan, Lin Zhu   +7 more
doaj   +1 more source