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DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network

MULTIMEDIA TOOLS AND APPLICATIONS 2021年第20期80卷 30399-30415页

作者： Khamparia, Aditya Gupta, Deepak Rodrigues, Joel J. P. C. de Albuquerque, Victor Hugo C. Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India Maharaja Agrasen Inst Technol New Delhi India Univ Fed Piaui Teresina PI Brazil Inst Telecommun P-1049001 Lisbon Portugal Univ Fortaleza Grad Program Appl Informat Fortaleza Ceara Brazil

Early detection, early diagnosis and classification of the cancer type facilitates faster disease management of patients. Cervical cancer is fourth most pervasive cancer type which affects life of many people worldwide. The intent of this study is to automate cancer diagnosis and classification through deep learning techniques to ensure patients health condition progress timely. For this research, Herlev dataset was utilized which contains 917 benchmarked pap smear cells of cervical with 26 attributes and two target variables for training and testing phase. We have adopted combination of convolutional network with variational autoencoder for data classification. The usage of variational autoencoder reduces the dimensionality of data for further processing with involvement of softmax layer for training. The results have been obtained over 917 cancerous image type pap smear cells, where 70% (642) allocated for training and remaining 30% (275) considered for test data set. The proposed architecture achieved variational accuracy of 99.2% with 2*2 filter size and 99.4% with 3*3 filter size using different epochs. The proposed hybrid variational convolutional autoencoder approach applied first time for cervical cancer diagnosis and performed better than traditional machine learning methods.

关键词： Variational Convolution Cervical Deep learning autoencoder

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Research on denoising sparse autoencoder

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2017年第5期8卷 1719-1729页

作者： Meng, Lingheng Ding, Shifei Xue, Yu China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Peoples R China Nanjing Univ Informat Sci & Technol Coll Comp & Software Nanjing 210044 Jiangsu Peoples R China

autoencoder can learn the structure of data adaptively and represent data efficiently. These properties make autoencoder not only suit huge volume and variety of data well but also overcome expensive designing cost and poor generalization. Moreover, using autoencoder in deep learning to implement feature extraction could draw better classification accuracy. However, there exist poor robustness and overfitting problems when utilizing autoencoder. In order to extract useful features, meanwhile improve robustness and overcome overfitting, we studied denoising sparse autoencoder through adding corrupting operation and sparsity constraint to traditional autoencoder. The results suggest that different autoencoders mentioned in this paper have some close relation and the model we researched can extract interesting features which can reconstruct original data well. In addition, all results show a promising approach to utilizing the proposed autoencoder to build deep models.

关键词： autoencoder Feature extraction Unsupervised learning Sparse coding Deep networks

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Constrained autoencoder-Based Pulse Compressed Thermal Wave Imaging for Sub-Surface Defect Detection

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IEEE SENSORS JOURNAL 2022年第18期22卷 17335-17342页

作者： Kaur, Kirandeep Mulaveesala, Ravibabu Mishra, Priyanka Indian Inst Technol Ropar Dept Elect Engn Infrared Imaging Lab Rupnagar 140001 India

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

关键词： autoencoder deep learning frequency modulated thermal wave imaging (FMTWI) non-destructive testing & evaluation (NDT&E)

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A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8335-8354页

作者： Ranjan, Pallavi Kumar, Rajeev Girdhar, Ashish Delhi Technol Univ Dept Comp Sci Delhi India Kurukshetra Univ Dept Comp Applicat Kurukshetra India

The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and more. Supervised deep learning networks have demonstrated exceptional performance in HSI classification, capitalizing on their capacity for end-to-end optimization and leveraging their strong potential for nonlinear modeling. However, labelling HSIs, on the other hand, necessitates extensive domain knowledge and is a time-consuming and labour-intensive exercise. To address this issue, the proposed work introduces a novel semi-supervised network constructed with an autoencoder, Siamese action, and attention layers that achieves excellent classification accuracy with labelled limited samples. The proposed convolutional autoencoder is trained using the mass amount of unlabelled data to learn the refinement representation referred to as 3D-CAE. The added Siamese network improves the feature separability between different categories and attention layers improve classification by focusing on discriminative information and neglecting the unimportant bands. The efficacy of the proposed model's performance was assessed by training and testing on both same-domain as well as cross-domain data and found to achieve 91.3 and 93.6 for Indian Pines and Salinas, respectively.

关键词： Classification Deep learning Siamese Hyperspectral images autoencoder Attention

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Wind turbine anomaly detection based on SCADA: A deep autoencoder enhanced by fault instances

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ISA TRANSACTIONS 2023年第1期139卷 586-605页

作者： Liu, Jiarui Yang, Guotian Li, Xinli Wang, Qianming He, Yuchen Yang, Xiyun North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

An increasing number of deep autoencoder-based algorithms for intelligent condition monitoring and anomaly detection have been reported in recent years to improve wind turbine reliability. However, most existing studies have only focused on the precise modeling of normal data in an unsupervised manner;few studies have utilized the information of fault instances in the learning process, which results in suboptimal detection performance and low robustness. To this end, we first developed a deep autoencoder enhanced by fault instances, that is, a triplet-convolutional deep autoencoder (triplet-Conv DAE), jointly integrating a convolutional autoencoder and deep metric learning. Aided by fault instances, triplet-Conv DAE can not only capture normal operation data patterns but also acquire discriminative deep embedding features. Moreover, to overcome the difficulty of scarce fault instances, we adopted an improved generative adversarial network-based data augmentation method to generate high-quality synthetic fault instances. Finally, we validated the performance of the proposed anomaly detection method using a multitude of performance measures. The experimental results show that our method is superior to three other state-of-the-art methods. In addition, the proposed augmentation method can efficiently improve the performance of the triplet-Conv DAE when fault instances are insufficient. & COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Wind turbine Anomaly detection autoencoder Deep metric learning Data augmentation SCADA data

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Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks

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CHEMICAL ENGINEERING SCIENCE 2018年 181卷 68-78页

作者： Na, Jonggeol Jeon, Kyeongwoo Lee, Won Bo Seoul Natl Univ Sch Chem & Biol Engn Gwanak Ro 1 Seoul 08826 South Korea

High-accuracy gas dispersion models are necessary for predicting toxic gas movement, and for reducing the damage caused by toxic gas release accidents in chemical processes. In urban areas, where obstacles are large and abundant, computational fluid dynamics (CFD) would be the best choice for simulating and analyzing scenarios of accidental release of toxic chemicals. However, owing to the large computation time required for CFD simulation, it is inappropriate in emergency situations and in real-time alarm systems. In this study, a non-linear surrogate model based on deep learning is proposed using a variational autoencoder with deep convolutional layers and a deep neural network with batch normalization (VAEDC-DNN) for real-time analysis of the probability of death (P-death). VAEDC can extract representation features of the Pdeath contour with complicated urban geometry in the latent space, and DNN maps the variable space into the latent space for the Pdeath image data. The chlorine gas leak accident in the Mipo complex (city of Ulsan, Republic of Korea) is used for verification of the model. The proposed model predicts the Pdeath image within a mean squared error of 0.00246, and compared with other models, it exhibits superior performance. Furthermore, through the smoothness of image transition in the variable space, it is confirmed that image generation is not overfitting by data memorization. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： autoencoder Deep learning Surrogate model Toxic gas release Convolutional neural network Computational fluid dynamics

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Quality-relevant feature extraction method based on teacher-student uncertainty autoencoder and its application to soft sensors

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INFORMATION SCIENCES 2022年 592卷 320-339页

作者： Lu, Yusheng Jiang, Chao Yang, Dan Peng, Xin Zhong, Weimin East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Supervised representation learning based on the teacher-student framework can extract quality-related features for soft sensors, in which the teacher network extracts representation information for the student network as supervision information. In traditional applications, the teacher network is heavy and is difficult to train, so the teacher network is conventionally pre-trained. However, the pre-training of the teacher network is unnecessary if the training process is not complicated so that it is meaningful to joint optimize the teacher-student network. In our application, the teacher-student framework is used to extract quality-related representation information for soft sensors. The objective is to maximize the mutual information of representation information and supervision information, in which the inconsistency of distributions between observed information and supervisory information is modeled as isotropic Gaussian noise. The objective is decoupled through analysis under some approximate assumptions so that the alternative iteration method can be used to update the parameters of the model. The proposed quality-related feature extraction method is applied to soft sensors combined with a traditional just-in-time learning method. Our experiments show that the prediction performance of our representation extraction method is better than other existing representation extraction algorithms. (C) 2022 Published by Elsevier Inc.

关键词： autoencoder Representation learning Soft sensor Teacher-student network

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Stock market network based on bi-dimensional histogram and autoencoder

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INTELLIGENT DATA ANALYSIS 2022年第3期26卷 723-750页

作者： Choi, Sungyoon Gwak, Dongkyu Song, Jae Wook Chang, Woojin Seoul Natl Univ Dept Ind Engn Seoul 08826 South Korea Hanyang Univ Dept Ind Engn Seoul South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul South Korea Seoul Natl Univ SNU Inst Res Finance & Econ Seoul South Korea

In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix. The constructed stock market network represents the latent space of bi-dimensional histogram, and network analysis is performed to investigate the structural properties of the stock market. we discover that the structural properties of stock market network are related to the dispersion of bi-dimensional histogram. Also, we confirm that the autoencoder is effective in extracting the latent feature of the bi-dimensional histogram. Portfolios using the features of bi-dimensional histogram network are constructed and their investment performance is evaluated in comparison with other benchmark portfolios. We observe that the portfolio consisting of stocks corresponding to the peripheral nodes of bi-dimensional histogram network shows better investment performance than other benchmark stock portfolios.

关键词： autoencoder complex network dimensionality reduction latent space visualization histogram stock portfolio

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Multi-label learning with kernel extreme learning machine autoencoder

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KNOWLEDGE-BASED SYSTEMS 2019年第0期178卷 1-10页

作者： Cheng, Yusheng Zhao, Dawei Wang, Yibin Pei, Gensheng Anqing Normal Univ Sch Comp & Informat Anhui Anqing 246011 Peoples R China Univ Key Lab Intelligent Percept & Comp Anhui Pro Anqing 246011 Peoples R China

In multi-label learning, in order to improve the accuracy of classification, many scholars have considered the relationship between features and features, features and labels or labels and labels, but how to combine the correlation among them is rarely studied. Based on this, this paper proposes a multi-label learning algorithm with kernel extreme learning machine autoencoder. Firstly, the label space is reconstructed by using the non-equilibrium labels completion method in the label space. Then, the non-equilibrium labels space information is added to the input node of the kernel extreme learning machine autoencoder network, and the input features are output as the target. Finally, the kernel extreme learning machine is used for classification. Our method implements the information fusion between features and features, between labels and features, and between labels and labels. Compared with the traditional autoencoder network, the extreme learning machine autoencoder has no iterative process, which reduces the network training time and improves the classification accuracy. The experimental results of the proposed algorithm in the opening benchmark multi-label data sets show that the KELM-AE algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis testing and stability analysis further illustrate the effectiveness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-label learning Extreme learning machine autoencoder Non-equilibrium labels completion Information entropy Labels correlations

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Deep autoencoder Learning for Relay-Assisted Cooperative Communication Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第9期68卷 5471-5488页

作者： Lu, Yuxin Cheng, Peng Chen, Zhuo Li, Yonghui Mow, Wai Ho Vucetic, Branka Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia CSIRO DATA61 Marsfield NSW 2122 Australia Univ Sydney Sch Elect & Informat Sydney NSW 2006 Australia

Emerging recently as a novel concept in communication system design, end-to-end learning introduces deep neural networks (NNs) to represent the transmitter and receiver functions. Consequently, the whole system can be interpreted as an autoencoder (AE), which can be optimized from a holistic approach through a data-driven training method. Until now, the AE technique is mainly developed for point-to-point communication scenarios. In this paper, we aim to develop a novel NN-based AE scheme for relay-assisted cooperative communication systems. Specifically, three NN components are constructed to learn the behavior of the transmitter, relay node, and receiver, respectively. As the conventional end-to-end training is inapplicable, a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem. The implicit approximations involved are analytically expressed based on information theory, offering insights on the achievable performance with the proposed training method. The proposed AE model eliminates the need for channel state information and noise variance of any link, and is adaptive to the variation in the input block length. Simulation results verify its advantages over the conventional decode-and-forward (DF) and amplify-and-forward (AF) schemes in various scenarios.

关键词： Training Relays Artificial neural networks Receivers Adaptation models Transmitters Fading channels autoencoder channel state information deep learning neural network relay network

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