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第37届中国控制会议

作者： Meiling Xu Yaguo Dong Zhan Li Min Han Tong Xing School of Control Science and Engineering Dalian University of Technology

Recently,the scale of observed time series becomes bigger and bigger,and the learning ability of conventional models is limited to depict the characteristics of large-scale time *** put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by *** autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the *** this method,on the one hand,the dimension of the matrix that describes the problem is reduced,on the other hand,a large number of data can be compressed and *** we calculate the output weights by the method of pseudo *** results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method.

关键词： Time series prediction deep autoencoder feature extraction

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SHAPE RETRIEVAL USING deep autoencoder LEARNING REPRESENTATION 13

SHAPE RETRIEVAL USING DEEP AUTOENCODER LEARNING REPRESENTATI...

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Xu, Guoqing Fang, Weiwei Nanyang Inst Technol Sch Comp & Informat Engn Nanyang 473004 Peoples R China

ISBN: (纸本)9781509061266

deep learning has shown to be very effective in variety of applications including image classification and object recognition. In this paper we use deep autoencoder for compact shape representation learning and image retrieval. In this method the autoencoder is a 4-layer coding network, and the original shape images after scale normalization are used to pre-train the autoencoder in an unsupervised way. Then fine-tuning procedure is executed on the 4-layer coding network to refine the learned weight matrixes. Finally a learned 40-dimension vector for each shape image is used as its features, and the similarity between any two shapes is measured using standard cosine similarity. The image retrieval performance of the proposed method is evaluated on the Swedish leaf database using precision and recall measurement, and compared with the classical Fourier shape descriptor. The experimental results indicate that the proposed method reaches the higher precision at the same recall value among compared methods.

关键词： deep autoencoder representation learning image retrieval Swedish leaf shape database

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deep learning based retinal disease classification using an autoencoder and generative adversarial network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 108卷

作者： Jeyasri, G. Karthiyayini, R. Anjalai Ammal Mahalingam Engn Coll Dept Informat Technol Kovilvenni Tamil Nadu India Anna Univ Univ Coll Engn Bharathidasan Inst Technol Campus Dept Comp Applicat Tiruchirappalli Tamilnadu India

Human eyesight relies heavily on retinal tissue, vision loss include infections of the retina and either a delay in treatment or the disease remaining untreated. Identifying retinopathy from retinal fundus image is a vital and diagnostic system performance depends on image quality and quantity. Furthermore, the diagnosis is prone to errors when a large imbalanced database is used. Hence, a fully automated retina disease prediction system is indispensable to minimize human intervention, increase the performance of the disease diagnostic system, and support ophthalmologists in conducting speedy and accurate investigations. Advancements in deep learning have remarkable results in identifying retinopathy from retinal fundus images. However, conventional deep-learning approaches struggle to learn enough in-depth features to identify aspects of mild retinal disease. To address this, integrates a deep autoencoder-based diagnostic system with a ResNet-based generative adversarial network (RGAN) to find retinal disease. This integrated model exploits a ResNet-50 structure to generate synthetic images to handle higher FAR and class imbalance-related problems and a deep autoencoder to categorize the retinal fundus pictures into benign and malicious. The proposed RGAN engenders synthetic images to train the diagnostic and real systems. The experimental outcomes have been implemented, and the recommended RGAN model increases the accuracy ratio of 95.6%, sensitivity ratio of 96.4%, specificity ratio of 97.3%, and F1-score ratio of 93.4% compared to other popular techniques.

关键词： deep autoencoder Generative adversarial network ResNet Disease prediction Retinal fundus image Retinopathy classification

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DeMAAE: deep multiplicative attention-based autoencoder for identification of peculiarities in video sequences

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VISUAL COMPUTER 2024年第3期40卷 1729-1743页

作者： Aslam, Nazia Kolekar, Maheshkumar H. Indian Inst Technol Patna Dept Elect Engn Video Surveillance Lab Bihta 801106 India Indian Inst Technol Patna Dept Elect Engn Bihta 801106 India

In videos, anomaly detection is challenging due to its diverse nature in different application domains. Reconstruction and prediction-based methods have been widely employed to detect anomalies. Due to the generalization capability of a deep neural network, sometimes, it recreates irregular patterns along with regular ones. This paper presents a novel autoencoder-based framework called deep multiplicative attention-based autoencoder (DeMAAE) to detect anomalies in a video sequence. The global attention mechanism is used at the decoder side of DeMAAE for better feature learning during the decoding phase. An attention map is created by taking the dot product between all encoder's hidden states and the previously generated decoder's hidden state. After that, the final output of the decoder is determined by the context vector. The context vector is computed using the weighted summation of all encoder's hidden states and attention weight. DeMAAE delivers an improved runtime of 0.015 s (similar to 67 fps) for detecting anomalies during testing. Extensive experiments have been performed on the two diversified and widely used datasets (UCSD Pedestrian and CUHK Avenue) to compare the efficacy of DeMAAE with different state-of-the-art methods.

关键词： Anomaly detection Attention mechanism deep autoencoder Temporal attention

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Semantic Retrieval of Personal Photos using a deep autoencoder Fusing Visual Features with Speech Annotations Represented as Word/Paragraph Vectors 16

Semantic Retrieval of Personal Photos using a Deep Autoencod...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Lu, Hung-tsung Liou, Yuan-ming Lee, Hung-yi Lee, Lin-shan Natl Taiwan Univ Grad Inst Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781510817906

It is very attractive for the user to retrieve photos from a huge collection using high-level personal queries (e.g. "uncle Bill's house"), but technically very challenging. Previous works proposed a set of approaches toward the goal assuming only 30% of the photos are annotated by sparse spoken descriptions when the photos are taken. In this paper, to promote the interaction between different types of features, we use the continuous space word representations to train a paragraph vector model for the speech annotation, and then fuse the paragraph vector with the visual features produced by deep Convolutional Neural Network (CNN) using a deep autoencoder (DAE). The retrieval framework therefore combines the word vectors and paragraph vectors of the speech annotations, the CNN-based visual features, and the DAE-based fused visual/speech features in a three-stage process including a two-layer random walk. The retrieval performance was significantly improved in the preliminary experiments.

关键词： image retrieval speech annotation word representation paragraph vector convolutional neural network deep autoencoder random walk fused features

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Optimisation of sparse deep autoencoders for dynamic network embedding

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1361-1376页

作者： Huimei Tang Yutao Zhang Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Institute for Infocomm Research Agency for Science Technology and ResearchSingaporeSingapore Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi'anChina

Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.

关键词： deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure

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NIDS-DA: Detecting functionally preserved adversarial examples for network intrusion detection system using deep autoencoders

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep Kumar, Neeraj Natl Inst Technol Pauri Garhwal Dept Comp Sci & Engn Srinagar 246174 Uttarakhand India IGDTUW Dept Informat Technol Delhi 110006 India Doon Univ Dept Comp Sci Dehra Dun 248001 Uttarakhand India Thapar Inst Engn & Technol Patiala Punjab India

DL has revolutionized Network Intrusion Detection Systems (NIDS) in recent years. However, these models suffer from Adversarial Examples (AEs), which are maliciously crafted to cause misclassification by a target NIDS. Unlike image recognition, AEs in a network intrusion domain are crafted for evasion and to launch attacks once they have successfully evaded detection. In recent years, several adversarial attack techniques have been developed to generate adversarial examples that retain their maliciousness even after perturbations. This is achieved by only modifying the non-functional features of a malicious data sample. In this paper, we propose a deep autoencoder (DAE) mechanism that can detect adversarial examples that are crafted by only modifying non-functional attributes. The method trains a DAE to establish a latent space relationship between non-functional attributes of feature space data samples. It then uses the inconsistency in the classification result of an AE and latent space relationship between non-functional features for adversarial detection. The paper shows that a DAE trained on only non-functional features produces fewer false positives than a DAE trained on both functional and non-functional features. We evaluate our proposed method on three data sets (CICIDS2017, NSL-KDD, and UNSW-NB15) and against five state-of-the-art AE attacks. Experimentally, our method was able to detect up to 99% AEs with very few false positives.

关键词： Adversarial example Functional/non-functional features DL Machine learning deep autoencoder

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Network intrusion detection system for DDoS attacks in ICS using deep autoencoders

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WIRELESS NETWORKS 2024年第6期30卷 5059-5075页

作者： Ortega-Fernandez, Ines Sestelo, Marta Burguillo, Juan C. Pinon-Blanco, Camilo Galician Res & Dev Ctr Adv Telecommun GRADIANT Carretera Vilar 56-58 Vigo 36214 Spain Univ Vigo Dept Stat & OR SiDOR Res Grp Vigo 36310 Spain CITMAga Santiago De Compostela 15782 Spain Univ Vigo Escola Enxenaria Telecomunicac Vigo 36310 Spain Univ Vigo atlanTTic Res Ctr Vigo 36310 Spain

Anomaly detection in industrial control and cyber-physical systems has gained much attention over the past years due to the increasing modernisation and exposure of industrial environments. Current dangers to the connected industry include the theft of industrial intellectual property, denial of service, or the compromise of cloud components;all of which might result in a cyber-attack across the operational network. However, most scientific work employs device logs, which necessitate substantial understanding and preprocessing before they can be used in anomaly detection. In this paper, we propose a network intrusion detection system (NIDS) architecture based on a deep autoencoder trained on network flow data, which has the advantage of not requiring prior knowledge of the network topology or its underlying architecture. Experimental results show that the proposed model can detect anomalies, caused by distributed denial of service attacks, providing a high detection rate and low false alarms, outperforming the state-of-the-art and a baseline model in an unsupervised learning environment. Furthermore, the deep autoencoder model can detect abnormal behaviour in legitimate devices after an attack. We also demonstrate the suitability of the proposed NIDS in a real industrial plant from the alimentary sector, analysing the false positive rate and the viability of the data generation, filtering and preprocessing procedure for a near real time scenario. The suggested NIDS architecture is a low-cost solution that uses only fifteen network-based features, requires minimal processing, operates in unsupervised mode, and is straightforward to deploy in real-world scenarios.

关键词： Network intrusion detection system Anomaly detection Industrial control systems Cyber-physical systems Industrial cybersecurity deep autoencoder

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Learning binary codes for fast image retrieval with sparse discriminant analysis and deep autoencoders

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INTELLIGENT DATA ANALYSIS 2023年第3期27卷 809-831页

作者： Hong, Son An Huu, Quynh Nguyen Viet, Dung Cu Thuy, Quynh Dao Thi Quoc, Tao Ngo Viet Hung Univ Hanoi Vietnam Thuyloi Univ Hanoi Vietnam Posts & Telecommun Inst Technol Hanoi Vietnam Vietnam Acad Sci & Technol Inst Informat Technol Hanoi Vietnam

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.

关键词： Content-based image retrieval (CBIR) sparse discriminant analysis deep autoencoder binary code

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Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac104-bbac104页

作者： Liu, Wei Lin, Hui Huang, Li Peng, Li Tang, Ting Zhao, Qi Yang, Li Xiangtan Univ Sch Comp Sci Sch Cyberspace Sci Xiangtan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China Xiangtan Univ Sch Publ Adm Xiangtan Peoples R China

Increasing evidences show that the occurrence of human complex diseases is closely related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease-related miRNAs is essential for the diagnosis and treatment of complex human diseases. Although some current computational methods can effectively predict potential disease-related miRNAs, the accuracy of prediction should be further improved. In our study, a new computational method via deep forest ensemble learning based on autoencoder (DFELMDA) is proposed to predict miRNA-disease associations. Specifically, a new feature representation strategy is proposed to obtain different types of feature representations (from miRNA and disease) for each miRNA-disease association. Then, two types of low-dimensional feature representations are extracted by two deep autoencoders for predicting miRNA-disease associations. Finally, two prediction scores of the miRNA-disease associations are obtained by the deep random forest and combined to determine the final results. DFELMDA is compared with several classical methods on the The Human microRNA Disease Database (HMDD) dataset. Results reveal that the performance of this method is superior. The area under receiver operating characteristic curve (AUC) values obtained by DFELMDA through 5-fold and 10-fold cross-validation are 0.9552 and 0.9560, respectively. In addition, case studies on colon, breast and lung tumors of different disease types further demonstrate the excellent ability of DFELMDA to predict disease-associated miRNA-disease. Performance analysis shows that DFELMDA can be used as an effective computational tool for predicting miRNA-disease associations.

关键词： miRNA-disease association prediction deep forest ensemble learning deep autoencoder feature representation

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