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Deep Learning Enabled autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

Computers, Materials & Continua 2021年第7期68卷 487-503页

作者： Thavavel Vaiyapuri College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate ***,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of *** of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality *** by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF *** proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in ***,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item ***,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP *** conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation *** experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation *** analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rat

关键词： Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations

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Inherent-attribute-aware dual-graph autoencoder for rating prediction

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Journal of Information and Intelligence 2024年第1期2卷 82-97页

作者： Yangtao Zhou Qingshan Li Hua Chu Jianan Li Lejia Yang Biaobiao Wei Luqiao Wang Wanqiang Yang School of Computer Science and Technology Xidian UniversityXi'an 710071China Intelligent Financial Software Engineering New Technology Joint Laboratory Xidian UniversityXi'an 710071China Shanghai Fairyland Software Corp. Ltd.Shanghai 200233China

autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'***,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding *** meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating *** address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item *** exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among ***,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence *** experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.

关键词： Rating prediction Graph convolutional network autoencoder Inherent attribute aware

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Two-Dimensional Principal Component Analysis-Based Convolutional autoencoder for Wafer Map Defect Detection

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第9期68卷 8789-8797页

作者： Yu, Jianbo Liu, Jiatong Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in the semiconductor manufacturing process can help operators to troubleshoot root causes of the out-of-control process and then accelerate the process adjustment. This article proposes a novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition. First, a new convolution kernel based on conditional two-dimensional principal component analysis is developed to construct the first convolutional block of PCACAE. Second, a convolutional autoencoder is cascaded by considering the nonlinearity of data representation. The second convolutional block of PCACAE is constructed based on the encoding part. Finally, the pretrained PCACAE is fine-tuned to obtain the final classifier. PCACAE is successfully applied for feature learning and recognition of wafer map defects. The experimental results on a real-world case demonstrate that PCACAE is superior to other well-known convolutional neural networks (e.g., GoogLeNet, PCANet) on WMPR.

关键词： Kernel Feature extraction Covariance matrices Convolution Principal component analysis Machine learning Training autoencoder convolutional neural network (CNN) deep learning semiconductor manufacturing wafer map pattern recognition (WMPR)

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Comparison of Training Strategies for autoencoder-Based Monochromatic Image Denoising

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SENSORS 2023年第12期23卷 5538页

作者： Jozwik-Wabik, Piotr Bernacki, Krzysztof Popowicz, Adam Silesian Tech Univ Fac Automat Control Elect & Comp Sci Akad 16 PL-44100 Gliwice Poland

Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our article focuses on the utilization of machine learning (ML) for the denoising of monochromatic images in multiple data availability scenarios, including those with no access to noise-free data. For this purpose, a simple autoencoder architecture was chosen and checked for various training approaches on two large and widely used image datasets: MNIST and CIFAR-10. The results show that the method of training as well as architecture and the similarity of images within the image dataset significantly affect the ML-based denoising. However, even without access to any clear data, the performance of such algorithms is frequently well above the current state-of-the-art;therefore, they should be considered for monochromatic image denoising.

关键词： image denoising Gaussian noise autoencoder

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A Shallow autoencoder Framework for Epileptic Seizure Detection in EEG Signals

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SENSORS 2023年第8期23卷 4112页

作者： Khan, Gul Hameed Khan, Nadeem Ahmad Bin Altaf, Muhammad Awais Abbasi, Qammer Lahore Univ Management Sci LUMS Dept Elect Engn Lahore 54792 Pakistan Western Washington Univ Engn & Design Dept Bellingham WA 98225 USA Univ Glasgow James Watt Sch Engn Commun Sensing & Imaging Res Grp Glasgow G12 8QQ Scotland

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

关键词： autoencoder EEG classification epilepsy seizure detection

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autoencoder-based IDS for cloud and mobile devices 21

Autoencoder-based IDS for cloud and mobile devices

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21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Faber, Kamil Faber, Lukasz Sniezynski, Bartlomiej AGH Univ Sci & Technol Inst Comp Sci Krakow Poland

ISBN: (纸本)9781728195865

Along with the popularization of cloud computing and the increase in responsibilities of mobile devices, there is a need for intrusion detection systems available for working in these two new areas. At the same time, the increase in computational power of mobile devices gives us the possibility to use them to do a part of data preprocessing. Similarly, more complex operations can be executed in the cloud - this concept is known as mobile cloud computing. In this paper, we propose an autoencoder-based intrusion detection system applicable to cloud and mobile environments. The system provides multiple data gathering points, allowing to monitor either fully controlled networks, like virtual networks in the cloud, or mobile devices scattered in different networks. The monitoring process uses both mobile devices and cloud computational power. Gathered network traffic records are sent to a proper intrusion detection node, which executes the detection process. In case of suspicious behavior, an alert of a possible intrusion can be sent to the device owner. The detection process is based on an autoencoder neural network, which brings significant advantages: an anomaly-based approach, training only on benign samples, and a good performance. To improve detection results, we created time-window-based features, and there is also a possibility to share computed statistics between intrusion detection nodes. In the experiments, we construct three models using pure network flows data and time-window-based features. The results show that the autoencoder-based approach can detect with a high performance attacks not known during the training process. We also prove that created derived features have a significant impact on detection results.

关键词： intrusion detection system autoencoder machine learning security mobile cloud computing

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Convolutional Long Short-Term Memory autoencoder-Based Feature Learning for Fault Detection in Industrial Processes

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-15页

作者： Yu, Jianbo Liu, Xing Ye, Lyujiangnan Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Rutgers State Univ Stat Dept New Brunswick NJ 08854 USA

Process signals show the characteristics of large scale, high dimension, and strong correlation in modern industrial processes, which brings a big challenge for process fault detection and diagnosis. Due to the powerful feature learning ability, deep learning has been widely used in image and visual processing. This article proposes a new deep neural network (DNN), convolutional long short-term memory autoencoder (CLSTM-AE) for feature learning from process signals. The convolutional LSTM (ConvLSTM) is proposed to describe the distribution of the process data and learn effective features on time series data for fault detection. A selective residual block is embedded in the deep network to improve the training accuracy and perform feature selection from process signals. Two statistics, the T-2 and the squared prediction error (SPE), are generated in the feature space and residual space of CLSTM-AE, respectively. Finally, the feasibility and advantages of CLSTM-AE are shown on a simulated process, the Tennessee-Eastman process (TEP), and the continuous stirred tank reactor (CSTR). CLSTM-AE has good fault detection performance in these cases, which shows that it is capable of learning effective features from complex process signals. The hybrid learning technique with convolutional LSTM and autoencoder provides a new way for feature learning and fault detection for complex industrial processes.

关键词： autoencoder deep learning fault detection industrial process long short-term memory

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An autoencoder Based Background Subtraction for Public Surveillance

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2021年第10期E104A卷 1445-1449页

作者： Li, Yue Yu, Xiaosheng Cao, Haijun Xu, Ming Northeastern Univ Sch Humanities & Law Shenyang 110819 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.

关键词： surveillance image background image autoencoder multi-scale features foreground detection

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A BiGRU autoencoder Remaining Useful Life Prediction Scheme With Attention Mechanism and Skip Connection

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IEEE SENSORS JOURNAL 2021年第9期21卷 10905-10914页

作者： Duan, Yuhang Li, Honghui He, Mengqi Zhao, Dongdong Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

Remaining Useful Life (RUL) prediction is one of the most common activities to ensure the reliability of a degradation system. In previous RUL prediction schemes based on RNN autoencoder, the multi-dimensional sensor data for each timestep made an equal contribution to the generation of the embedding vector during the encoding process. Besides, the single embedding vector carries the burden of decoding the entire multi-timestep information. To overcome the above shortcomings, two improvements are proposed: (1) For the embedding vectors to highlight critical timestep information, weights are assigned to each timestep information through an attention mechanism. (2) To reduce the decoding burden on a single embedding vector, a skip connection is introduced at each step of the decoding process to improve BiGRU decoding capabilities. The prognostic performance of the proposed method BiGRU-AS is evaluated on two publicly available datasets: the C-MAPSS dataset (simulation dataset) and the milling dataset (experimental dataset). Compared to the latest prediction methods, the experimental results show that the proposed method is competitive in RUL prediction for mechanical systems.

关键词： Degradation Sensors Decoding Hidden Markov models Training Predictive models Machine learning Remaining useful life bidirectional recurrent neural network autoencoder attention mechanism health indicator

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SL2E-AFRE : Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation

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APPLIED INTELLIGENCE 2021年第4期51卷 2253-2268页

作者： Devi, P. R. Suganya Baskaran, R. Anna Univ Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

3D face reconstruction from single face image has received much attention in the past decade, as it has been used widely in many applications in the field of computer vision. Despite more accurate solutions by 3D scanners and several commercial systems, they have drawbacks such as the need for manual initialization, time and economy constraints. In this paper, a novel framework for 3D face reconstruction is presented. Firstly, landmarks are localized on the database faces with the proposed landmark-mapping strategy employing a model template. Then, an autoencoder assisted by the proposed energy function to simultaneously learn the facial patch subspace and the keypoints positions is employed to predict the landmarks. Finally, an unique 3D reconstruction is obtained with the proposed predicted landmark based deformation. Meta-parameters are incorporated into the energy function during the training phase to enhance the performance of the autoencoder network in reconstructing the face model. The experiments are carried out on two databases namely the USF Human ID 3-D Database and the Bosphorus 3D face database. The experimental results show that the autoencoder based Face REconstruction with Simultaneous patch Learning and Landmark Estimation method (SL2E-AFRE) is efficient and the performance of the same is significantly upgraded in each iteration.

关键词： 3D Face reconstruction autoencoder Landmark estimation Shape deformation

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