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autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第5期22卷 e16669页

作者： D'Souza, Marcus Van Munster, Caspar E. P. Dorn, Jonas F. Dorier, Alexis Kamm, Christian P. Steinheimer, Saskia Dahlke, Frank Uitdehaag, Bernard M. J. Kappos, Ludwig Johnson, Matthew Univ Hosp Basel Dept Med Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Hosp Basel Dept Biomed Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Hosp Basel Dept Clin Res Neurol Clin & Policlin Petersgraben 4 Basel 4031 Switzerland Univ Basel Petersgraben 4 Basel 4031 Switzerland Univ Amsterdam Multiple Sclerosis Ctr Amsterdam Dept Neurol Med Ctr Amsterdam Netherlands Novartis Pharma AG Basel Switzerland Luzerner Kantonsspital Neuroctr Luzern Switzerland Univ Bern Dept Neurol Inselspital Bern Switzerland Microsoft Res Cambridge England

Background: In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non-health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. Objective: The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. Methods: In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. Results: In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe de

关键词： autoencoder video-rating machine learning algorithms deep neuronal network Neurostatus-EDSS

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autoencoder-based anomaly root cause analysis for wind turbines

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Energy and AI 2021年第2期4卷 57-65页

作者： Cyriana M.A.Roelofs Marc-Alexander Lutz Stefan Faulstich Stephan Vogt Fraunhofer IEE Konigstor 59Kassel 34119Germany Intelligent Embedded Systems Universitat KasselWilhelmshoher Allee 67Kassel 34121Germany

A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an *** models have proven to be very successful in detecting such deviations,yet cannot show the underlying cause or failure *** information is necessary for the implementation of these models in the planning of maintenance *** this paper we introduce a novel method:*** use ARCANA to identify the possible root causes of anomalies detected by an *** describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly *** reconstruction must be similar to the anomaly and thus identify only a few,but highly explanatory anomalous features,in the sense of Ockham’s *** proposed method is applied on an open data set of wind turbine sensor data,where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test *** results are compared with the reconstruction errors of the autoencoder *** ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features,whereas using the non-optimised reconstruction error does *** though the deviation in one specific input feature is very large,the reconstruction error of many other features is large as well,complicating the interpretation of the detected ***,we apply ARCANA to a set of offshore wind turbine *** case studies are discussed,demonstrating the technical relevance of ARCANA.

关键词： Anomaly detection autoencoder Root cause analysis Predictive maintenance Wind turbine Explainable artificial intelligence

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Enhancing Deep Learning-Based Channel Estimation: A Novel autoencoder-Based Approach

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第5期36卷

作者： Fola, Ephrem Luo, Chunbo Luo, Yang Jiang, Xiangyuan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao Peoples R China

Deep-learning (DL) methods have shown promising performance in pioneering studies on orthogonal frequency division multiplexing (OFDM) channel estimation challenges. Unlike typical DL-based channel estimation methods that mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, this paper proposes AE-DENet, a novel autoencoder (AE)-based data enhancement network to achieve robust channel estimation for OFDM systems. AE-DENet employs the classic least square (LS) channel estimation as input and proposes a data enhancement method to extract the interaction features from the real and imaginary parts of the complex channel estimation matrix, which are joined with the original real and imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results for a range of representative DL-based channel estimation methods demonstrate that the proposed AE-DENet-enhanced channel estimation framework achieves state-of-the-art channel estimation performance with trivial added computational complexity. Furthermore, the input dimensions of the DL-based channel estimation models can be adaptively adjusted to accommodate the dimension of the enhanced LS input. The proposed approach is also shown to be robust to channel variations and high user mobility.

关键词： AE-DENet autoencoder channel estimation least square estimation

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autoencoder Model Using Edge Enhancement to Detect Communities in Complex Networks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 1303-1314页

作者： Chaudhary, Laxmi Singh, Buddha Jawaharlal Nehru Univ New Delhi 110067 India

Community structure is the utmost significant characteristics in complex networks. Numerous algorithms of community detection have been developed so far. Some methods consider only the lower-order framework, i.e. nodes and edges, and neglect the higher-order framework, whereas other recent methods capture higher-order framework, i.e. motifs (a small dense subgraph), yet they mainly put emphasis on the connected motif hypergraph. These methods help to encounter only fragmentation issue. Moreover, the other major issue in complex network is to reduce the dimension and extract the significant characteristics. In addition to this, the existing methods have sparsity and computational issues as well. Hence, we have developed an autoencoder model using edge enhancement (AMEE) to tackle these issues and uncover the hidden communities in complex networks. It begins by emphasizing edge enhancement to redesign the network connectivity of input network and creates a rewired graph. Embedding of a rewired network is obtained by applying the autoencoder model. Finally, a community detection technique is applied to reveal the communities. Hence, the proposed method (AMEE) deals with the above-mentioned issues efficiently. Furthermore, a comprehensive analysis of the proposed method is carried out on eight different real-world network datasets, i.e. Polbooks, Email, Polblogs, Cora, Facebook, Com-Orkut, Com-Amazon and Com-Youtube. The Modularity score, F-score and normalized mutual information are used as evaluation parameters to measure the performance of the proposed method. The higher value of these measures clearly indicates the efficacy and quality of the communities achieved using the proposed approach. It shows a significant improvement in comparison with other existing community detection techniques.

关键词： Communities Community detection Social networks Complex networks autoencoder Deep learning

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A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

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Digital Communications and Networks 2023年第1期9卷 14-21页

作者： Pengcheng Wei Bo Wang Xiaojun Dai Li Li Fangcheng He School of Mathematics and Information Engineering Chongqing University of EducationChongqingChina School of Automation Chongqing University of Posts and TelecommunicationsChongqingChina Department of Modern Service Chongqing Energy Industry Technician CollegeChongqingChina College of Foreign Languages Literature Chongqing University of EducationChongqingChina

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between ***,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful *** machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent ***,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and autoencoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better *** the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper ***,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or *** experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

关键词： Controller area network bus packet In-vehicle network Attention mechanism autoencoder

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autoencoder-based defect detection in PVC profile manufacturing

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INTERNATIONAL JOURNAL OF MANUFACTURING RESEARCH 2024年第2期19卷 119-144页

作者： Aslan, Ahmet Zahit Onal, Sinan Southern Illinois Univ Dept Ind Engn 61 Harping Dr Box 1805 Edwardsville IL 62026 USA

This study develops an automatic defect detection system for polyvinyl chloride (PVC) profile manufacturing, addressing inefficiencies in manual inspection. It compares the proposed autoencoder model with other well-known unsupervised deep-learning methods, including GANomaly, f-AnoGAN, and the student-teacher network, for defect detection during extrusion. Utilising a defective PVC profile dataset, the study generates anomaly heat maps through reconstruction errors and assesses model performance using the area under the receiver operating characteristic (ROC) curve. The proposed autoencoder model is found to be optimal for this dataset, offering a balance between efficiency and accuracy. These findings have significant implications for enhancing quality control and reducing defects in PVC manufacturing, with potential applicability in other industrial settings.

关键词： automated defect detection polyvinyl chloride PVC profiles unsupervised deep learning models autoencoder quality control in manufacturing

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An autoencoder-based feature level fusion for speech emotion recognition

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Digital Communications and Networks 2024年第5期10卷 1341-1351页

作者： Peng Shixin Chen Kai Tian Tian Chen Jingying National Engineering Research Center for E-Learning National Engineering Laboratory for Educational Big DataCentral China Normal UniversityHubei430079China

Although speech emotion recognition is challenging,it has broad application prospects in human-computer *** a system that can accurately and stably recognize emotions from human languages can provide a better user ***,the current unimodal emotion feature representations are not distinctive enough to accomplish the recognition,and they do not effectively simulate the inter-modality dynamics in speech emotion recognition *** paper proposes a multimodal method that utilizes both audio and semantic content for speech emotion *** proposed method consists of three parts:two high-level feature extractors for text and audio modalities,and an autoencoder-based feature *** audio modality,we propose a structure called Temporal Global Feature Extractor(TGFE)to extract the high-level features of the timefrequency domain relationship from the original speech *** that text lacks frequency information,we use only a Bidirectional Long Short-Term Memory network(BLSTM)and attention mechanism to simulate an intra-modal *** these steps have been accomplished,the high-level text and audio features are sent to the autoencoder in parallel to learn their shared representation for final emotion *** conducted extensive experiments on three public benchmark datasets to evaluate our *** results on Interactive Emotional Motion Capture(IEMOCAP)and Multimodal EmotionLines Dataset(MELD)outperform the existing ***,the results of CMU Multi-modal Opinion-level Sentiment Intensity(CMU-MOSI)are ***,experimental results show that compared to unimodal information and autoencoderbased feature level fusion,the joint multimodal information(audio and text)improves the overall performance and can achieve greater accuracy than simple feature concatenation.

关键词： Attention mechanism autoencoder Bimodal fusion Emotion recognition

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Direction-aware convolutional autoencoder based on positional encoding for one-dimensional anomaly detection

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Information Sciences 2025年 716卷

作者： Qien Yu Qiong Chang Tinghui Ouyang Takio Kurita Ran Dong School of Information Science and Engineering Chongqing Jiaotong University Chongqing 400074 China School of Computing Institute of Science Tokyo Meguro Tokyo 152-8552 Japan School of Automation Southeast University Nanjing Jiangsu 211189 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima Hiroshima 739-8521 Japan School of Engineering Chukyo University Toyota Aichi 470-0393 Japan

One-dimensional anomaly detection remains challenging owing to existing methods inadequately modeling interactions between widely separated elements and neglecting local interaction patterns. To address these limitations, this study proposes a space transformation-based direction-aware convolutional autoencoder framework using positional encoding (SDP-CAE) for one-dimensional anomaly detection. Unlike previous approaches, SDP-CAE uniquely applies multiple space-filling curves to transform one-dimensional data into two-dimensional data, effectively capturing rich local feature interactions and overcoming the limitations posed by traditional one-dimensional modeling. Space-filling curves can enhance the interactions among different elements of one-dimensional data to enrich local feature patterns in a two-dimensional space. Furthermore, we introduce an asymmetric two-stream convolutional autoencoder architecture that employs horizontal and vertical convolution operations to explicitly capture direction-specific interactions within the transformed data. This architecture significantly improves anomaly detection by modeling interactions that are sensitive to the local spatial context. Positional encoding (PE), employed as an auxiliary mechanism, enhances the representation of high-frequency details to further prompt the sensitivity of the model to subtle anomalies. Extensive experiments conducted on benchmark datasets demonstrate that our method significantly outperforms state-of-the-art methods, clearly validating the effectiveness and necessity of the proposed space transformation and direction-aware modeling mechanisms.

关键词： Anomaly detection autoencoder Position encoding Space-filling curve

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A Bi-population Cooperative Optimization Algorithm Assisted by an autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第8期10卷 3035-3043页

作者： Wang, Wei Zhao, Mengxue Wang, Jigang Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Sci & Technol Elect Informat Control Lab Chengdu Sichuan Peoples R China ZTE Corp Chengdu Sichuan Peoples R China

Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN). First, to improve the accuracy of malware detection, we reconstruct the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware. In the serial convolutional neural network architecture (CNN-S), we use Relu, a non-linear function, as the activation function to increase sparseness and dropout to prevent over-fitting. The convolutional layer and pooling layer are combined with the full-connection layer to enhance feature extraction capability. Under these conditions, CNN-S shows powerful ability in feature extraction and malware detection. Second, to reduce the training time, we use deep autoencoder as a pre-training method of CNN. With the combination, deep autoencoder and CNN model (DAE-CNN) can learn more flexible patterns in a short time. We conduct experiments on 10,000 benign apps and 13,000 malicious apps. CNN-S demonstrates a significant improvement compared with traditional machine learning methods in Android malware detection. In details, compared with SVM, the accuracy with the CNN-S model is improved by 5%, while the training time using DAE-CNN model is reduced by 83% compared with CNN-S model.

关键词： Deep learning Convolutional neural network autoencoder Malware detection Android applications

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