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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Shuo Liu Shuo Liu Xuemei Ding Xuemei Ding Damien Coyle Damien Coyle Intelligent Systems Research Centre University of Ulster Londonderry

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Novelty detection (ND) has gained attention in many applications for its effectiveness in dealing with imbalanced data. Many ND algorithms have been proposed. For example, the level set boundary description (LSBD) algorithm can accurately estimate a boundary around normal data which is subsequently used to detect novelties. However, the computational complexity and the convergence time of the LSBD algorithms increases substantially when data dimensionality increases. To solve those challenges, we propose an Integrated autoencoder-Level Set Method (AE-LSM) for ND in this paper. The AE structure is employed to reduce the feature space with high dimensionality to a 3-dimensional (3D) space. The LSM algorithm is trained based on the compressed 3D data to identify the boundary of normal data. The AE-LSM has advantages of boundary control and good generalization performance. Experiments on 5 benchmark UCI datasets and an Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed AE-LSM present a 3%similar to 14% significant improvement based on the average AUC (p<0.05) over the AE and LSBD algorithms across the six datasets.

关键词： novelty detection level set methods autoencoder level set boundary description

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A novel in-situ sensor calibration method for building thermal systems based on virtual samples and autoencoder

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ENERGY 2024年 297卷

作者： Sun, Zhe Yao, Qiwei Jin, Huaqiang Xu, Yingjie Hang, Wei Chen, Hongyu Li, Kang Shi, Ling Gu, Jiangping Zhang, Qinjian Shen, Xi Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China Zhejiang Univ Technol Taizhou Inst Taizhou Key Lab Adv Mfg Technol Taizhou 318014 Peoples R China Zhejiang Agr & Forestry Univ Coll Engn Hangzhou 311300 Peoples R China Zhejiang Univ Technol Coll Educ Hangzhou 310023 Peoples R China JiaXiPera Compressor Co Ltd Jiaxing 314011 Peoples R China

Sensor networks are playing an increasingly important role in modern buildings. With the growing size of building sensor networks and the increasing use of low-cost sensors, the accuracy and reliability of these sensor networks face challenges. Therefore, in-situ calibration of sensor networks is crucial to maintain data quality. Various state-of-the-art methods typically require meeting stringent conditions, such as reference sensors or colocated sensors, accurate physical models, and a large amount of operational data, limiting their applicability in some scenarios. This paper addresses a common issue in sensor calibration: the non-differential calibration issue in uncontrolled environments. We propose an in-situ calibration method based on virtual samples and autoencoder. Virtual samples are generated through Monte Carlo sampling to ensure the completeness of sample information. autoencoder autonomously establishes relationships within the sensor network, integrating sensor fault detection and calibration into one step. Offline experiments optimize methods, and online experiments are utilized for verification and analysis. The online experiments demonstrated that the proposed method achieved a calibration accuracy above 98.9 % for single and multiple sensor faults, with a calibration error below 3 %. Compared to the SoT methods, our approach consistently delivered superior performance, confirming its outstanding efficacy.

关键词： In-situ sensor calibration Building thermal systems Virtual samples autoencoder Monte Carlo sampling

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AE-Integrated: Real-time network intrusion detection with Apache Kafka and autoencoder

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第11期36卷 e8034-e8034页

作者： Roshan, Khushnaseeb Zafar, Aasim Aligarh Muslim Univ Dept Comp Sci Aligarh India Aligarh Muslim Univ Dept Comp Sci Aligarh 202002 India

Unknown cyber-attack detection in network traffic streams is challenging but crucial to ensure network security. It is observed that new security threats occur on a daily basis and make cyberspace vulnerable. In the literature, machine learning and deep learning-based network intrusion detection systems have gained a lot of success but still face many challenges in detecting new security threats and unknown cyber-attacks in real-time. Additionally, high false alarm rates and real-time detection in constantly evolving high-dimensional network data streams are open issues for the research community. To address this issue, a DL-based solution is developed to detect real-time network anomalies in streaming data with high detection accuracy, precision, recall and low false negative and positive scenarios. The proposed novel algorithm, AE-Integrated, is developed and evaluated on the latest CICIDS-2017 dataset. The AE-Integrated is updated with the newest network traffic data stream by the human administrator after a certain period to maintain its prediction accuracy for future inference. The simulation study is conducted with the Apache Kafka and Slack API to get real-time anomaly alerts. Finally, we compared the result with recent state-of-the-art research to evaluate the significance of the proposed algorithm. It is concluded that combining multiple lightweight autoencoders into a single large architecture provides optimal results. The accuracy, recall, and AUC of AE-Integrated obtained are 99.54%, 99.53%, and 0.998, respectively.

关键词： Apache Kafka autoencoder network anomaly detection network streaming data real-time anomaly detection

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A Novel Data-Driven Approach with a Long Short-Term Memory autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7458页

作者： Torres-Cabrera, Joel Maldonado-Correa, Jorge Valdiviezo-Condolo, Marcelo Artigao, Estefania Martin-Martinez, Sergio Gomez-Lazaro, Emilio Natl Univ Loja Technol & Energy Res Ctr CITE Loja 110150 Ecuador Univ Castilla La Mancha Renewable Energy Res Inst IIER Albacete 02071 Spain

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs.

关键词： wind turbine SCADA data fault prediction converter LSTM autoencoder multi-head attention

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A Cross-Modal Adaptive Masked autoencoder for Decoding Emotions With Multimodal Data

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年 1-14页

作者： Cheng, Cheng Liu, Wenzhe Zhang, Yong Feng, Lin Jia, Ziyu Dalian Univ Technol Sch Comp Sci & Technol Dalian 116081 Peoples R China Huzhou Univ Sch Informat & Engn Huzhou 313000 Peoples R China Liaoning Normal Univ Coll Comp & Artificial Intelligence Dalian 116029 Peoples R China Chinese Acad Sci Inst Automat Brainnetome Ctr Beijing 100190 Peoples R China

Multimodal emotion recognition (MER) has recently gained much attention since it can leverage information over multiple modalities. However, in real life, we often encounter the problem of missing modalities, as well as modeling the heterogeneity and correlation among multimodal data are challenges. To this end, we propose a unified model called cross-modal adaptive masked autoencoder (CMA-MAE) for incomplete multimodal learning. Our CMA-MAE model comprises a cross-modal adaptive fusion encoder (CMAFE) and a multiview adaptive encoder (MVAE) to capture and fuse the heterogeneity and correlation among multimodal features. Additionally, we design a convolutional decoder that progressive upsampling and fusion with the modality-invariant features to generate robust emotional features from partially observable data. To effectively utilize both data with complete and incomplete modalities for feature learning, we adopt an end-to-end approach that simultaneously optimizes classification and reconstruction tasks. Extensive testing on the DEAP and SEED-IV datasets is conducted to assess our model, with the findings demonstrating that our CMA-MAE model outperforms current leading approaches in both incomplete and complete multimodal learning scenarios.

关键词： autoencoder correlation heterogeneity missing modalities multimodal emotion recognition (MER)

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Single Channel Speech Enhancement System using Convolutional Neural Network based autoencoder for Noisy Environments 19

Single Channel Speech Enhancement System using Convolutional...

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19th IEEE-India-Council International Conference (INDICON)

作者： Buragohain, Rantu Ashishkumar, Gudmalwar Rao, Ch V. Rama NIT Meghalaya Dept ECE Shillong 793003 Meghalaya India

ISBN: (纸本)9781665473507

The proposed work involves forming a Convolutional Denoising autoencoder (CDAE) for speech enhancement (SE). Compared to different information exchange methods, like through visible data, speech signals are more complicated. Noise in the environment causes a reduction in intelligibility and quality of speech signals during the conversation. Therefore, there is a need to enhance distorted speech signals. Over the years, many SE techniques have been developed to restore clean speech by reducing background noise. Denoising speech afflicted with noise is a complex process due to the variable characteristics of noise environments. Researchers also apply deep learning algorithms to enhance noisy signals. In this work, the architecture for CDAE is developed to enhance the noisy speech signal. A 2D input feature vector is fed to this architecture, which is obtained by estimating the magnitude spectrum of speech signals. The CDAE training set includes clean speech as the intended target and the magnitude of spectral values of a speech signal affected by noise as input. In this context, a performance metric is the perceptual evaluation of speech quality (PESQ). The suggested system gives higher PESQ values for considered noise environments than the existing approaches.

关键词： Speech Enhancement Spectrum autoencoder Convolutional Neural Networks

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An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing

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JOURNAL OF THE ACADEMY OF MARKETING SCIENCE 2022年第6期50卷 1324-1350页

作者： Shankar, Venkatesh Parsana, Sohil Mays Business Sch Ctr Retailing Studies College Stn TX 77843 USA Oracle College Stn TX USA

With artificial intelligence permeating conversations and marketing interactions through digital technologies and media, machine learning models, in particular, natural language processing (NLP) models, have surged in popularity for analyzing unstructured data in marketing. Yet, we do not fully understand which NLP models are appropriate for which marketing applications and what insights can be best derived from them. We review different NLP models and their applications in marketing. We layout the advantages and disadvantages of these models and highlight the conditions under which different models are appropriate in the marketing context. We introduce the latest neural autoencoder NLP models, demonstrate these models to analyze new product announcements and news articles, and provide an empirical comparison of the different autoencoder models along with the statistical NLP models. We discuss the insights from the comparison and offer guidelines for researchers. We outline future extensions of NLP models in marketing.

关键词： Natural language processing (NLP) Topic modeling Machine learning Text analysis Text mining Unstructured data Artificial intelligence autoencoder Marketing

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Telecom Churn Prediction Using CNN with Variational autoencoder 3rd

Telecom Churn Prediction Using CNN with Variational Autoenco...

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3rd International Conference on Smart Systems - Innovations in Computing (SSIC)

作者： Jain, Hemlata Khunteta, Ajay Srivastava, Sumit Poornima Univ Jaipur Comp Sci Sch Basic & Appl Sci Jaipur 303905 Rajasthan India Poornima Grp Coll Jaipur 303905 Rajasthan India Manipal Univ Jaipur Dept Informat Technol Jaipur 303007 Rajasthan India

ISBN: (纸本)9789811628771;9789811628764

Customer churn is a major problem across traditional and new age companies like telecom, digital services providers, online marketplace, payment banking and social media companies. Customer churn means customer is moving out from the company, hence impacting the top line of financial sheets. This is particularly more relevant for companies having subscription-based plans. Telecom industry which has sizable customer base and cut-throat competition is at higher risk of impact due to churn. With the customer database size running into petabyte and terabytes, it is a tedious and time-taking task to accurately predict customer churn based on some random logic. While machine learning appears to be an easier alternative, often such database contains large number of string attributes that are tough to use in machine learning algorithms. Various traditional machine learning and data mining techniques have been applied for handling such big data. All such techniques have leveraged different techniques for data engineering. In this study, we have demonstrated how a new and powerful technique convolutional neural network with variational autoencoder can help predict churn with higher accuracy. CNN automatically has the ability of good feature selection and representation of input data, and this study has come with a new aspect, i.e. integrating all string attributes in dataset (ISAD) for using all existing string variable of database helping in enhancing model performance. Experiment is done on three telecom companies Cell2Cell, Telco and Orange datasets of size 51,048, 7048 and 3333, respectively. Model outperformed on all three datasets and achieved a good accuracy level. ISAD model helped to enhance the performance by giving more feature option for predicting the customer behaviour correctly.

关键词： autoencoder Categorical features CNN Deep learning Machine learning Telecommunications Variational

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A Convolutional autoencoder based Keyword Spotting in Historical Handwritten Devanagari Documents 5

A Convolutional Autoencoder based Keyword Spotting in Histor...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Sushma, S. N. Sharada, B. Univ Mysore Comp Sci Dept Mysore Karnataka India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

There exist huge amount of valuable historical Devanagari documents archived in many national libraries that need to be preserved in digital form. Additionally, there is a growing requirement in the area of document image processing for automation and information extraction from old handwritten documents. Retrieval of relevant information from handwritten historical document images would ideally necessitate an efficient An alternate approach to accurate document transcription is to use a keyword spotting method. The keyword spotting system's primary application is to create digital libraries that facilitate quick searching and browsing of old manuscripts in order to preserve the world's cultural heritage. For word spotting in handwritten manuscripts, we propose a convolution neural network (CNN) architecture based on autoencoder representation. The proposed technique was demonstrated using historical handwritten Devanagari manuscript which is collected from Oriental Research Institute of University of Mysore, Mysore. As a result, the proposed convolution neural network (CNN) method exhibits superior accuracy with favorable results.

关键词： Keyword spotting Convolutional neural network autoencoder

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Astral: An autoencoder-Based Model for Pedestrian Trajectory Prediction of Variable-Length

Astral: An Autoencoder-Based Model for Pedestrian Trajectory...

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Diao, Yupeng Su, Yiteng Zeng, Ximu Chen, Xu Liu, Shuncheng Su, Han Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Chengdu Peoples R China

ISBN: (纸本)9783031112171;9783031112164

Predicting pedestrian's future behavior in a crowd plays an important role in many fields. Such as autonomous driving, machine navigation, video surveillance, and intelligent security systems. This is very challenging because pedestrian motion can be easily influenced by surrounding pedestrians' interactions. In previous works, researchers use these interactions to make prediction more effective. However, the previous work set fixed-length input in their models. In this way, they ignore shorter pedestrian trajectories. This approach leads to insufficient feature information and inaccurately prediction in some scenarios. In this paper, we propose an autoencoder-based model for pedestrian trajectory prediction of variable length (ASTRAL). At first, we use the autoencoder to process pedestrian data with variable-length trajectories. And then, we use the optimized multi-head attention mechanism to extract the interactions between neighbors. Finally, we use LSTM to decode vectors and make predictions. In particular, we fine-tune the model to make its performance better. We test our model and the state-of-the-art methods on the public benchmark datasets. Compared with others, our model improves ADE (average displacement error) and FDE (final displacement error) by 9% and 33% respectively. Therefore, our model is better than previous works, and we can predict the future trajectory of pedestrians more effectively.

关键词： Variable length trajectory autoencoder Multi-head attention

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