A survey of unsupervised generative models for exploratory data analysis and representation learning
For more than a century, the methods for data representation and the exploration of the
intrinsic structures of data have developed remarkably and consist of supervised and …
intrinsic structures of data have developed remarkably and consist of supervised and …
A privacy-aware framework for detecting cyber attacks on internet of medical things systems using data fusion and quantum deep learning
M Al-Hawawreh, MS Hossain - Information Fusion, 2023 - Elsevier
Abstract Internet of Medical Things (IoMT) devices and systems are often designed without
adequate security, leaving them highly susceptible to cyber threats. Unlike other IoT …
adequate security, leaving them highly susceptible to cyber threats. Unlike other IoT …
Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders
This paper presents a novel non-intrusive surrogate modeling scheme based on deep
learning for predictive modeling of complex systems, described by parametrized time …
learning for predictive modeling of complex systems, described by parametrized time …
A new many-objective evolutionary algorithm based on generalized Pareto dominance
In the past several years, it has become apparent that the effectiveness of Pareto-dominance-
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
Deep learning in lane marking detection: A survey
Y Zhang, Z Lu, X Zhang, JH Xue… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can
not only provide relevant road condition information to prevent lane departure but also assist …
not only provide relevant road condition information to prevent lane departure but also assist …
Long Short-Term Memory-Based Twin Support Vector Regression for Probabilistic Load Forecasting
Z Zhang, Y Dong, WC Hong - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
A probabilistic load forecast that is accurate and reliable is crucial to not only the efficient
operation of power systems but also to the efficient use of energy resources. In order to …
operation of power systems but also to the efficient use of energy resources. In order to …
Detecting dynamic behavior of brain fatigue through 3-d-CNN-LSTM
EQ Wu, P Xiong, ZR Tang, GJ Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This article proposes a four-dimensional brain mapping method, which can represent the
continuous process of a person's fatigue state in the form of image frames in a space-time …
continuous process of a person's fatigue state in the form of image frames in a space-time …
Virtual reality-based measurement of ocular deviation in strabismus
Background and objective Strabismus is an eye movement disorder in which shows the
abnormal ocular deviation. Cover tests have mainly been used in the clinical diagnosis of …
abnormal ocular deviation. Cover tests have mainly been used in the clinical diagnosis of …
Real-time intrusion detection in wireless network: A deep learning-based intelligent mechanism
L Yang, J Li, L Yin, Z Sun, Y Zhao, Z Li - Ieee Access, 2020 - ieeexplore.ieee.org
With the development of the wireless network techniques, the number of cyber-attack
increases significantly, which has seriously threat the security of Wireless Local Area …
increases significantly, which has seriously threat the security of Wireless Local Area …
On adaptive learning framework for deep weighted sparse autoencoder: A multiobjective evolutionary algorithm
In this article, an adaptive learning framework is established for a deep weighted sparse
autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The …
autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The …