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Deep Attributed Network Embedding via Weisfeiler-Lehman and autoencoder

IEEE ACCESS 2022年 10卷 61342-61353页

作者： Al-Furas, Amr Thabit Alrahmawy, Mohammed F. Al-Adrousy, Waleed Mohamed Elmougy, Samir Ibb Univ Comp Sci Dept Ibb Yemen Mansoura Univ Fac Comp & Informat Comp Sci Dept Mansoura 35516 Egypt

Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications. Attributed network embedding aims to learn the low-dimensional representation of network nodes by integrating network architecture and attribute information. The network architectures of many real-world applications are complex, and the relations between network architectures and their attributed nodes are opaque. Thus, shallow models fail to capture deep nonlinear information when an attributed network is embedded, leading to unreliable embedding. In the present paper, a Deep Attributed Network Embedding via Weisfeiler-Lehman and autoencoder (DANE-WLA) is proposed in order to capture high nonlinearity and preserve the many proximities in the network attribute information of nodes and structures. Weisfeiler-Lehman proximity schema was used to capture the node dependency between both node edges and node attributes based on information sequences. Then, a deep autoencoder was applied to invest complex nonlinear information. Extensive experiments were conducted on benchmark datasets to verify that DANE-WLA is computationally efficient for various tasks requiring network embedding. The experimental results show that our model outperforms the state-of-the-art network embedding models.

关键词： Task analysis Kernel Deep learning Network architecture Time complexity Social networking (online) Neural networks Attributed network embedding autoencoder link prediction node classification visualization Weisfeiler-Lehman proximity schema

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Wafer Bin Map Recognition With autoencoder-Based Data Augmentation in Semiconductor Assembly Process

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 198-209页

作者： Shen, Po-Cheng Lee, Chia-Yen Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan 70101 Taiwan Natl Taiwan Univ Dept Informat Management Taipei 10617 Taiwan

Semiconductor manufacturers use the wafer bin map recognition (WBMR) system to identify failure modes in processing. This study proposes an WBMR system embedded with three modules: data preprocessing, region classification, and systematic pattern recognition. After using a revised Jaccard index to separate random patterns from systematic patterns, we compare three data augmentation techniques, particularly autoencoder-based, to find the best augmented method that addresses any data imbalance problems between the defect classes. We propose an adaptive algorithm to determine the amount of generated data. We describe the two tools, t-distributed stochastic neighbor embedding (t-SNE) and earth mover's distances (EMD) we use to quantify and visualize the information content of the augmented dataset. Finally, we use an inception architecture of convolutional neural network (CNN) to improve the WBMR system's recognition accuracy. An empirical study of the semiconductor assembly manufacturer and a public dataset validate that our proposed WBMR system effectively recognizes different types of defective patterns.

关键词： Systematics Costs Cost function Pattern recognition Convolutional neural networks Semiconductor device modeling Production Semiconductor manufacturing wafer bin map recognition autoencoder data imbalance deep learning

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered autoencoder

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Computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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Cognitive Memory-Guided autoencoder for Effective Intrusion Detection in Internet of Things

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第5期18卷 3358-3366页

作者： Lu, Huimin Wang, Tian Xu, Xing Wang, Ting Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8190395 Japan Univ Elect Sci & Technol China Ctr Future Multimedia Chengdu 610051 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610051 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

With the development of the Internet of Things (IoT) technology, intrusion detection has become a key technology that provides solid protection for IoT devices from network intrusion. At present, artificial intelligence technologies have been widely used in the intrusion detection task in previous methods. However, unknown attacks may also occur with the development of the network and the attack samples are difficult to collect, resulting in unbalanced sample categories. In this case, the previous intrusion detection methods have the problem of high false positive rates and low detection accuracy, which restricts the application of these methods in a real situation. In this article, we propose a novel method based on deep neural networks to tackle the intrusion detection task, which is termed Cognitive Memory-guided autoencoder (CMAE). The CMAE method leverages a memory module to enhance the ability to store normal feature patterns while inheriting the advantages of autoencoder. Therefore, it is robust to the imbalanced samples. Besides, using the reconstruction error as an evaluation criterion to detect attacks effectively detects unknown attacks. To obtain superior intrusion detection performance, we propose feature reconstruction loss and feature sparsity loss to constrain the proposed memory module, promoting the discriminative of memory items and the ability of representation for normal data. Compared to previous state-of-the-art methods, sufficient experimental results reveal that the proposed CMAE method achieves excellent performance and effectiveness for intrusion detection.

关键词： Intrusion detection Memory modules Feature extraction Data models Internet of Things Training Task analysis autoencoder cognitive memory deep neural networks (DNNs) Internet of Things (IoT) intrusion detection

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Efficient Deep-Learning-Based autoencoder Denoising Approach for Medical Image Diagnosis

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Computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： Walid El-Shafai Samy Abd El-Nabi El-Sayed MEl-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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FASSt: Filtering via Symmetric autoencoder for Spherical Superficial White Matter Tractography 14th

FASSt: Filtering via Symmetric Autoencoder for Spherical Sup...

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14th International Workshop on Computational Diffusion MRI (CDMRI)

作者： Li, Yuan Nie, Xinyu Fu, Yao Shi, Yonggang Univ Southern Calif USC Keck Sch Med Stevens Neuroimaging & Informat Inst Los Angeles CA 90033 USA Univ Southern Calif USC Viterbi Sch Engn Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA Case Western Reserve Univ CWRU Dept Comp & Data Sci Cleveland OH 44106 USA

ISBN: (纸本)9783031472916;9783031472923

Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.

关键词： Tractography filtering Superficial white matter Spherical representation autoencoder

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Latent adversarial regularized autoencoder for high-dimensional probabilistic time series prediction

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NEURAL NETWORKS 2022年第0期155卷 383-397页

作者： Zhang, Jing Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

Many practical applications require probabilistic prediction of time series to model the distribution on future horizons. With ever-increasing dimensions, much effort has been invested into developing methods that often make an assumption about the independence between time series. Consequently, the probabilistic prediction in high-dimensional environments has become an essential topic with significant challenges. In this paper, we propose a novel probabilistic model called latent adversarial regularized autoencoder, abbreviated as TimeLAR, specifically for high-dimensional multivariate Time Series Prediction (TSP). It integrates the flexibility of Generative Adversarial Networks (GANs) and the capability of autoencoders in extracting higher-level non-linear features. Through flexible autoencoder mapping, TimeLAR learns cross-series relationships and encodes this global information into several latent variables. We design a modified Transformer for these latent variables to capture global temporal patterns and infer latent space prediction distributions, where only one step is required to output multi-step predictions. Furthermore, we employ the GAN to further refine the performance of latent space predictions, by using a discriminator to guide the training of the autoencoder and the Transformer in an adversarial process. Finally, complex distributions of multivariate time series data can be modeled by the non-linear decoder of the autoencoder. The effectiveness of TimeLAR is empirically underpinned by extensive experiments conducted on five real-world high-dimensional time series datasets in the fields of transportation, electricity, and web page views. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Probabilistic prediction High -dimensional time series Generative adversarial network autoencoder

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autoencoder-based intra prediction with auxiliary feature

Autoencoder-based intra prediction with auxiliary feature

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Xu, Luhang Yu, Yue Yu, Haoping Wang, Dong OPPO Dept Stand Res Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781665475921

A set of autoencoders is trained to perform intra prediction for block-based video coding. Each autoencoder consists of an encoding network and a decoding network. Both encoding network and decoding networks are jointly optimized and integrated into the state-of-the-art VVC reference software VTM-11.0 as an additional intra prediction mode. The simulation is conducted under common test conditions with all intra configurations and the test results show 1.55%, 1.04%, and 0.99% of Y, U, V components Bjontegaard-Delta bit rate saving compared to VTM-11.0 anchor, respectively. The overall relative decoding running time of proposed autoencoder-based intra prediction mode on top of VTM-11.0 are 408% compared to VTM-11.0.

关键词： Intra prediction video coding autoencoder neural networks

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autoencoder-based self-supervised hashing for cross-modal retrieval

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第11期80卷 17257-17274页

作者： Li, Yifan Wang, Xuan Cui, Lei Zhang, Jiajia Huang, Chengkai Luo, Xuan Qi, Shuhan Harbin Inst Technol Shenzhen Comp Sci & Technol Shenzhen Peoples R China

Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability.

关键词： Cross-modal retrieval Hash learning autoencoder Self-supervised

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autoencoder performance analysis with adaptive and trainable activation function to compress images 8

Autoencoder performance analysis with adaptive and trainable...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： De Medeiros Delgado, Joao Henrique Ferreira, Tiago A. E. Univ Fed Rural Pernambuco UFRPE Dept Estatistia & Informat Recife PE Brazil

ISBN: (数字)9781665488587

ISBN: (纸本)9781665488587

Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and trainable activation functions have been studied lately, which are used to increase network performance. This study intends to evaluate the performance of an artificial neuron that uses adaptive and trainable functions in an autoencoder network for image compression problems. The tested neuron, known as Global-Local Neuron, comprises two complementary components, one with global characteristics and the other with local characteristics. The global component is given by a sine function and the local component by the hyperbolic tangent function. The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type autoencoder neural network model. Different compression ratios were considered when varying the size of the autoencoder bottleneck layer, and 48 samples were obtained for each value of this layer. The metrics used for the evaluation were the loss value obtained in the test set and the number of epochs necessary to reach a stopping criterion. In the second step, the classification accuracy of the images compressed by the encoder block of the previous model was evaluated, using a Wide Residual Networks (WRN) network and the Support Vector Machines (SVM) method. The results obtained indicated that the use of Global-Local Neuron improved the network training speed, obtained better classification accuracy for compression up to 50% in a WRN network, and proved the adaptability in image classification problems.

关键词： activation functions autoencoder neural network

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