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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Radha, S. Jeyalaksshmi, S. School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9798350373110

With the development of several services, traffic signal preemption is carried out for emergency tasks. For performing workflow intent, cloud systems offer unlimited virtual resources for designing the traffic signal preemption in emergency tasks. While some efforts have been made to implement traffic signal preemption for emergencies, more attention must be given to improving response times. A hybrid method called Graph Attention Preemptive Traffic-aware Sparse Neural autoencoder (GAPT-SNA) is proposed for secured data transmission in the cloud platform. The GAPT-SNA method is split into the preemptive traffic-aware relocation model and secure data transmission in the CC environment. Initially, the Graph Attention Recurrent Preemptive Relocation model is designed with an Enhanced Healthcare Monitoring System (EHMS) testbed dataset. The decision is forwarded to the discriminator network for scheduling. The Sparse Gradient Neural Auto-encoder is designed to ensure secure data transmission by utilizing preemptive traffic-aware relocated results. The sparse gradient neural function ensures sparsity on prediction tasks for boosting data integrity. Java Software was used to simulate and validate performance metrics, including response time, Throughput, data confidentiality, and data integrity, using the EHMS testbed dataset. © 2024 IEEE.

关键词： autoencoder cloud computing gated recurrent graph attention preemptive relocation sparse gradient neural

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Representation learning of 3D meshes using an autoencoder in the spectral domain

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COMPUTERS & GRAPHICS-UK 2022年 107卷 131-143页

作者： Lemeunier, Clement Denis, Florence Lavoue, Guillaume Dupont, Florent Univ Lyon CNRS INSA Lyon UCBLLIRISUMR5205 F-69622 Villeurbanne France Univ Lyon UCBL CNRS INSA LyonLIRISUMR5205 F-69622 Villeurbanne France Univ Lyon Cent Lyon CNRS INSA LyonUCBLLIRISUMR5205ENISE F-42023 Saint Etienne France

Learning on surfaces is a difficult task: the data being non-Euclidean makes the transfer of known techniques such as convolutions and pooling non trivial. Common methods deploy processes to apply deep learning operations to triangular meshes either in the spatial domain by defining weights between nodes, or in the spectral domain using first order Chebyshev polynomials followed by a return in the spatial domain. In this study, we present a Spectral autoencoder (SAE) enabling the application of deep learning techniques to 3D meshes by directly giving spectral coefficients obtained with a spectral transform as inputs. With a dataset composed of surfaces having the same connectivity, it is possible with the Graph Laplacian to express the geometry of all samples in the frequency domain. Then, by using an autoencoder architecture, we are able to extract important features from spectral coefficients without going back to the spatial domain. Finally, a latent space is built from which reconstruction and interpolation is possible. This method allows the treatment of meshes with more vertices by keeping the same architecture, and allows to learn on big datasets with short computation times. Through experiments, we demonstrate that this architecture is able to give better results than state of the art methods in a faster way. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Geometric Deep Learning Spectral analysis autoencoder Human body triangular meshes

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Spectral-Spatial Feature Extraction With Dual Graph autoencoder for Hyperspectral Image Clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8500-8511页

作者： Zhang, Yongshan Wang, Yang Chen, Xiaohong Jiang, Xinwei Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China

autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative representations with the assumption that connected samples share the same label and they cannot directly embed the geometric structure into feature extraction. To address these issues, in this paper, we propose a dual graph autoencoder (DGAE) to learn discriminative representations for HSIs. Utilizing the relationships of pair-wise pixels within homogenous regions and pair-wise spectral bands, DGAE first constructs the superpixel-based similarity graph with spatial information and band-based similarity graph to characterize the geometric structures of HSIs. With the developed dual graph convolution, more discriminative feature representations are learnt from the hidden layer via the encoder-decoder structure of DGAE. The main advantage of DGAE is that it fully exploits both the geometric structures of pixels with spatial information and spectral bands to promote nonlinear feature extraction of HSIs. Experiments on HSI datasets show the superiority of the proposed DGAE over the state-of-the-art methods.

关键词： Feature extraction Data mining Principal component analysis Hyperspectral imaging Convolution Task analysis Decoding Hyperspectral imagery dimensionality reduction feature extraction autoencoder graph convolution

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Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第6期20卷 1827-1840页

作者： Xu, Xinyao Qin, Fangbo Zhao, Wenjun Xu, De Wang, Xingang Yang, Xihao Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China State Key Lab Smart Mfg Special Vehicles & Transm Baotou City 014000 Inner Mongolia Peoples R China

The anomaly detection for multimode industrial process is a challenging problem, because the multiple operation modes present various main distributions of monitored variables, and the dynamic sequential characteristics exist within each operation mode. This paper proposes an anomaly detection method based on sequence-to-sequence gated recurrent units (SGRU). First, to better model both the cross-mode trends and mode-specific sequential characteristics, a main reconstruction module and residual reconstruction module are integrated to improve the ability to represent complex process. Both modules are implemented by SGRUs. Second, a reconstruction error prediction module is designed to estimate the mean values of mode-specific reconstruction errors, which helps to determine the more reliable alarm thresholds. Third, the two anomaly indicators are utilized to represent the deviation degree of monitored variables against the normal conditions, according to the statistical errors and biases of reconstructions, respectively. The effectiveness of the proposed method is validated on simulations with multimode process, and on the practical data set collected from the Cleaning-in-Place multimode process of an aseptic beverage filling line in a real factory.

关键词： Anomaly detection autoencoder gated recurrent unit multimode process

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Residual autoencoder Deep Neural Network for Electrical Capacitance Tomography

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Computers, Materials & Continua 2022年第12期73卷 6307-6326页

作者： Wael Deabes Kheir Eddine Bouazza Department of Computer Science in Jamoum Umm Al-Qura UniversityMakkah25371Saudi Arabia Computers and Systems Engineering Department Mansoura UniversityMansoura35516Egypt Laboratoire D’Informatique et des Technologies de L’Information D’Oran(LITIO) University of Oran31000OranAlgeria

Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography(ECT)image ***,there is still a need to make these image reconstruction results faster and of better ***,Deep Learning(DL)is flourishing and is adopted in many *** DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks(ANNs).An ECT image reconstruction model using DNN is proposed in this *** proposed model mainly uses Residual autoencoder called(ECT_ResAE).Alarge-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE *** instance contains two vectors;a distinct permittivity distribution and its corresponding capacitance *** capacitance vector has been modulated to generate a 66×66 image,and represented to the ECT_ResAE as an *** scalability and practicability of the ECT_ResAE network are tested using noisy data,new samples,and experimental *** experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed *** achieved an average image Correlation Coefficient(CC)of more than 99%and an averageRelative ImageError(IE)around 8.5%.

关键词： ECT image reconstruction deep learning residual autoencoder IE CC

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Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 5761-5774页

作者： Chen, Li Saykin, Andrew J. Yao, Bing Zhao, Fengdi Univ Florida Dept Biostat Gainesville FL 32603 USA Indiana Univ Sch Med Dept Radiol & Imaging Sci Indianapolis IN 46202 USA Emory Univ Dept Human Genet Atlanta GA 30322 USA

Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomark-ers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the histor-ical DNA methylation data and the performance is further improved by including all temporal DNA methylation data. Availability:: https://***/lichen-lab/MTAE. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning Alzheimer?s disease Longitudinal data autoencoder DNA methylation

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Spatial-Aware Hyperspectral Nonlinear Unmixing autoencoder With Endmember Number Estimation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 20-41页

作者： Shahid, Kazi Tanzeem Schizas, Ioannis D. Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

In this article, we develop a novel fully unsupervised autoencoder-based scheme for nonlinear hyperspectral pixel unmixing. A unique approach is derived where high noise and unresponsive pixels are accounted for, by a unique averaging approach based on spatially aware filters built using radial basis function (RBF) kernels. A novel technique is implemented via calculating rank-equivalent kernel covariance matrices in order to estimate the unknown number of endmembers contributing to the data. Utilization of spatial information is done via RBF-based weighted averaging, which is then followed by endmember estimation via K-means clustering. The RBF distances from the cluster centers are determined to measure the position of the mixed pixels in relation to the centers, which is utilized as a preliminary estimation of the abundances. The proposed framework is robust in the presence of unresponsive pixels, while highly versatile working with different nonlinear unmixing models. Extensive numerical tests establish the superiority of the novel approach with respect to state-of-the-art methods.

关键词： Hyperspectral imaging Estimation Kernel Spatial resolution Earth Recurrent neural networks Meteorology autoencoder hyperspectral imaging neural networks nonlinear unmixing radial basis function (RBF) kernel unsupervised

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Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with autoencoder Classifier

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NEURAL PROCESSING LETTERS 2022年第4期54卷 3489-3505页

作者： Sheela, S., V Radhika, K. R. BMS Coll Engn Dept Informat Sci & Engn Bangalore Karnataka India

In recent decades, iris recognition is a trustworthy and important biometric model for human recognition. Criminal to commercial products, citizen confirmation and border control are few application areas. The research work is a deep learning based integrated model for accurate iris detection and recognition. Initially, eye images are considered from two datasets, the Chinese Academy of Sciences Institute of Automation (CASIA) and the Indian Institute of Technology (IIT) Delhi v1.0. Iris region is accurately segmented using Daugman's algorithm and Circular Hough Transform (CHT). Feature extraction is hybrid that is performed using Dual Tree Complex Wavelet Transform (DTCWT), Gabor filter, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) from the segmented iris regions. A Multiobjective Artificial Bee Colony (MABC) algorithm is proposed to eliminate noisy and redundant feature vectors by estimating consistent information. In MABC algorithm, two multi-objective functions are formulated as reduction in number of features and classification error rate. The selected active feature vectors are given as input to autoencoder classification for iris recognition. The experimental outcome shows that MABC-autoencoder model obtained 99.67% and 98.73% accuracy on CASIA-Iris, and IIT Delhi v1.0 iris datasets. Performance evaluation is based on accuracy, specificity, Critical Success Index (CSI), sensitivity, Fowlkes Mallows (FM) index, and Mathews Correlation Coefficient (MCC).

关键词： autoencoder Circular Hough transform Daugman's algorithm Iris recognition Local binary pattern Multi objective artificial bee colony

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Contrastive autoencoder for anomaly detection in multivariate time series

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INFORMATION SCIENCES 2022年 610卷 266-280页

作者： Zhou, Hao Yu, Ke Zhang, Xuan Wu, Guanlin Yazidi, Anis Beijing Univ Posts & Telecommun Beijing Peoples R China Norwegian Res Ctr AS Grimstad Norway Oslo Metropolitan Univ Oslo Norway Norwegian Univ Sci & Technol Trondheim Norway Oslo Univ Hosp Oslo Norway

With the proliferation of the Internet of Things, a large amount of multivariate time series (MTS) data is being produced daily by industrial systems, corresponding in many cases to life-critical tasks. The recent anomaly detection researches focus on using deep learning methods to construct a normal profile for MTS. However, without proper constraints, these methods cannot capture the dependencies and dynamics of MTS and thus fail to model the normal pattern, resulting in unsatisfactory performance. This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in MTS, by introducing multi -grained contrasting methods to extract normal data pattern. First, to capture the temporal dependency of series, a projection layer is employed and a novel contextual contrasting method is applied to learn the robust temporal representation. Second, the projected series is transformed into two different views by using time-domain and frequency-domain data augmentation. Last, an instance contrasting method is proposed to learn local invariant characteristics. The experimental results show that CAE-AD achieves an F1-score ranging from 0.9119 to 0.9376 on the three public datasets, outperforming the baseline methods.(c) 2022 Published by Elsevier Inc.

关键词： Anomaly detection Multivariate time series autoencoder Contrastive learning Data augmentation

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A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期81卷 695-714页

作者： Patel, Heena Upla, Kishor P. Sardar Vallabhbhai Natl Inst Technol Surat 395007 India

This paper addresses an approach for the classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with narrow and continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Such nature of data acquisition is very useful in the classification and/or the identification of different objects present in the HSI data. However, the low-spatial resolution and large volume of HS images make it more challenging. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for the classification of HS images. Pre-processing with autoencoder enhances the features in the HS images which helps to obtain optimized weights in the initial layers of the CNN model. Hence, shallow CNN architecture can be utilized to extract features from the pre-processed HSI data which are used further for the classification of the same. The potential of the proposed approach has been verified by conducting many experiments on various datasets. The classification results obtained using the proposed method are compared with many state-of-the-art deep learning based methods including the winner of the geoscience and remote sensing society (GRSS) Image Fusion Contest-2018 on HSI classification held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and it shows superiority over those methods.

关键词： autoencoder CNN ERDAS Imagine Feature extraction Hyperspectral images Classification

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