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autoencoder Constrained Clustering With Adaptive Neighbors

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第1期32卷 443-449页

作者： Li, Xuelong Zhang, Rui Wang, Qi Zhang, Hongyuan Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously.

关键词： Kernel Adaptive systems Clustering methods Clustering algorithms Sparse matrices Learning systems Neural networks Adaptive neighbors autoencoder deep clustering parameter-free similarity structured graph

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autoencoder-based deep metric learning for network intrusion detection

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INFORMATION SCIENCES 2021年 569卷 706-727页

作者： Andresini, Giuseppina Appice, Annalisa Malerba, Donato Univ Bari Aldo Moro Dept Informat Via Orabona 4 I-70125 Bari Italy Consorzio Interuniv Nazl Infomat CINI Rome Italy

Nowadays intrusion detection systems are a mandatory weapon in the war against the ever-increasing amount of network cyber attacks. In this study we illustrate a new intrusion detection method that analyses the flow-based characteristics of the network traffic data. It learns an intrusion detection model by leveraging a deep metric learning methodology that originally combines autoencoders and Triplet networks. In the training stage, two separate autoencoders are trained on historical normal network flows and attacks, respectively. Then a Triplet network is trained to learn the embedding of the feature vector representation of network flows. This embedding moves each flow close to its reconstruction, restored with the autoencoder associated with the same class as the flow, and away from its reconstruction, restored with the autoencoder of the opposite class. The predictive stage assigns each new flow to the class associated with the autoencoder that restores the closest reconstruction of the flow in the embedding space. In this way, the predictive stage takes advantage of the embedding learned in the training stage, achieving a good prediction performance in the detection of new signs of malicious activities in the network traffic. In fact, the proposed methodology leads to better predictive accuracy when compared to competitive intrusion detection architectures on benchmark datasets. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Network intrusion detection Deep metric learning Triplet network autoencoder

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autoencoder-CatBoost Model for Accurate Hyperspectral Quality Assessment of Yunling Snowflake Beef

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IEEE ACCESS 2024年 12卷 184701-184713页

作者： Gao, Lutao Zhang, Lilian Chen, Jian Peng, Lin Guo, Lujiale Yang, Linnan Yunnan Agr Univ Coll Food Sci & Technol Kunming 650201 Yunnan Peoples R China Yunnan Agr Univ Coll Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Technol Res Ctr Agr Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Res Ctr Big Data Intelligent Informat Kunming 650201 Yunnan Peoples R China Fudan Univ Zhongshan Hosp Shanghai 200032 Peoples R China

Snowflake beef is highly valued for its nutritional benefits and exquisite taste, yet the inconsistent quality in the market poses challenges for consumers in distinguishing genuine products, leading to economic losses and trust issues. This study aims to develop a rapid, accurate, and non-destructive method for evaluating the quality of snowflake beef to enhance consumer satisfaction and confidence. We propose a novel autoencoder-Catboost model based on an autoencoder and CatBoost classifier, utilizing hyperspectral imaging (HSI) technology to assess the quality grades of Yunling Cattle snowflake beef. A total of 250 beef samples, scanned in the 900-2500 nm wavelength range, were processed using seven data preprocessing techniques and two feature extraction methods. The autoencoder, comprising three layers of Transformer units, effectively extracted features from the hyperspectral data, which were then classified by the CatBoost classifier. The model demonstrated superior accuracy, precision, recall, and F1-score compared to traditional machine learning methods, achieving an average accuracy of 82.42%. This research introduces an innovative approach by integrating Transformer-based autoencoders with CatBoost for hyperspectral data analysis, providing a new methodology for non-destructive snowflake beef quality evaluation and offering valuable insights for future research in food quality assessment.

关键词： Hyperspectral imaging Feature extraction Accuracy Spectroscopy Cows Transformers Noise Information filters Data models Data mining Snowflake beef quality hyperspectral imaging autoencoder CatBoost Yunling cattle

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autoencoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第8期131卷 1995-2014页

作者： Cao, Bing Bi, Zhiwei Hu, Qinghua Zhang, Han Wang, Nannan Gao, Xinbo Shen, Dinggang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Chongqing Univ Posts & Telecommun Chongqing Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China Shanghai Clin Res & Trial Ctr Shanghai Peoples R China

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods.

关键词： Multimodal collaborative learning Medical image synthesis autoencoder Self-representation

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autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Li, Fan Chen, Chun-Hsien Liu, Yisi Chang, Danni Cui, Jian Sourina, Olga Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Fraunhofer Singapore Singapore Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

关键词： autoencoder Eye -tracking Robot appearance design User preference

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autoencoder-based drug synergy framework for malignant diseases

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 113卷 108273页

作者： Rani, Pooja Dutta, Kamlesh Kumar, Vijay Natl Inst Technol Comp Sci & Engn Dept Hamirpur 177005 HP India Dr BR Ambedkar Natl Inst Technol Jalandhar Informat Technol Dept Jalandhar 144027 Punjab India

Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O'Neil datasets. autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.

关键词： Embeddings Drug synergy autoencoder Deep learning Encoding Neural network Malignant diseases

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autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第3期34卷 1427-1444页

作者： Yun, Huitaek Kim, Hanjun Jeong, Young Hun Jun, Martin B. G. Purdue Univ Sch Mech Engn W Lafayette IN 47906 USA Purdue Univ Indiana Mfg Competitiveness Ctr MaC W Lafayette IN 47906 USA Kyungpook Natl Univ Sch Mech Engn Daegu 41566 South Korea

Sound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the machine status to detect machine faults. However, it is difficult to collect enough amount of fault data as much as normal status data for training general NN models. To resolve the issue, this paper proposes the autoencoder-based anomaly detection framework for industrial robot arms using an internal sound sensor. The autoencoder uses signals in the normal state of the robots for training the model. It reconstructs the input signals as output, and anomalous states are found from high reconstruction error. Two stethoscopes were attached to the surface of the robot joint as sensors, and the sounds were recorded by USB microphone attached to the outlet of the stethoscopes. Features were extracted from STFT spectrogram images of the gathered sound, then used to train and test an autoencoder model. The reconstruction errors of the autoencoder were compared to distinguish the abnormal status from normal one. The experimental results suggest that the stethoscopes prevent the interference of noise, and the collected sound signals can be utilized for detecting machine anomalies.

关键词： Sound spectrogram autoencoder Neural network Stethoscope Industrial robot arm

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autoencoder-Based Feature Extraction for Identifying Hate Speech Spreaders in Social Media

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 4819-4827页

作者： Kumar, Gunjan Singh, Jyoti Prakash Singh, Amit Kumar Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 India

Hate speech on social media has become a big problem, making regular users very upset and giving victims depression and suicidal thoughts. Early identification of the user spreading this type of hate speech may be a better solution, allowing hate speech to be stopped at source. In this article, we attempt to identify these hate speech spreaders by finding a representation for each user. Each user's comments are aggregated and fed to an auto-encoder to train it. The encoder part of the auto-encoder is used to get an encoded vector for each user. The encoded vector is used with different machine learning (ML) classifiers to determine if a user is spreading hate speech. The proposed model was tested using the dataset released by PAN 2021 (https://***/***) hate speech spreader profiling competition in English and Spanish. The experimental results show that support vector machine (SVM) with encoded vectors as features outperforms existing models with an accuracy of 92% for both English and Spanish dataset. The proposed features extraction technique is found to be equally effective at identifying fake news spreaders on fake news datasets provided by PAN 2020 yielding accuracy values of 95% and 83% for English and Spanish, respectively.

关键词： autoencoder hate speech hate speech spreader long short-term memory (LSTM)

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autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

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IEEE ACCESS 2023年 11卷 124150-124162页

作者： Smialkowski, Tomasz Czyzewski, Andrzej Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning method using the autoencoder model with LSTM and 1D Convolutional networks for various configurations and training periods. The evaluation of the algorithms was carried out based on real data from an extensive lighting control system. A practical approach was proposed using real-time, unsupervised algorithms employing limited computing resources that can be implemented in industrial devices designed to control intelligent city lighting. An anomaly detection algorithm based on classic LSTM networks, single-layer and multi-layer, was used for comparison purposes. Error matrix calculus was used to assess the quality of the models. It was shown that based on the autoencoder method, it is possible to construct an algorithm that correctly detects anomalies in power measurements of lighting systems, and it is possible to build a model so that the algorithm works correctly regardless of the season of the year.

关键词： Anomaly detection autoencoder machine learning road lighting systems smart city smartmeters

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autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development

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ISA TRANSACTIONS 2020年 103卷 143-155页

作者： Liu, Kang Shao, Weiming Chen, Guoming China Univ Petr Ctr Offshore Engn & Safety Technol Qingdao 266580 Peoples R China China Univ Petr Coll New Energy Qingdao 266580 Peoples R China

The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high performance is to construct accurate localized models. To this end, in this paper a nonlinear local model training algorithm called nonlinear Bayesian weighted regression (NBWR) is proposed. In the NBWR, the nonlinear features of process data are first extracted by the autoencoder;then, given a query sample a local dataset is selected on the feature space and a fully Bayesian regression model with differentiated sample weights is developed. The benefits of this approach, which include better consistency of correlation, stronger abilities to deal with process nonlinearities and uncertainties, overfitting and numerical issues, lead to superior performance. The NBWR is used for developing a soft sensor under the LWL framework, and a real-world industrial process is used to evaluate the performance of the NBWR-based soft sensor. The experimental results demonstrate that the proposed method outperforms several benchmarking soft sensing approaches. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Soft sensor Locally weighted learning Bayesian weighted regression autoencoder Nonlinear feature extraction

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