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Supervised Learning via Unsupervised sparse autoencoder

IEEE ACCESS 2018年 6卷 73802-73814页

作者： Liu, Jianran Li, Chan Yang, Wenyuan Minnan Normal Univ Fujian Key Lab Granular Comp & Applicat Zhangzhou 363000 Peoples R China Xiamen Univ Tan KahKee Coll Xiamen 363105 Peoples R China

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly involve datasets with sufficient labels and fail to achieve effective feature vectors for datasets with insufficient labels. In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal, with the resulting low-dimensional feature being reconstructed to the original dataset as much as possible. Therefore, the reduction of high-dimensional datasets to low-dimensional datasets is effective. First, the relationship among the reconstructed data, the number of iterations, and the number of hidden variables is explored. Second, the dimensionality reduction ability of the sparse autoencoder is proven. Several classical feature representation methods are compared with the sparse autoencoder on publicly available datasets, and the corresponding low-dimensional representations are placed into different supervised classifiers and the classification performances reported. Finally, by adjusting the parameters that might influence the classification performance, the parametric sensitivity of the sparse autoencoder is shown. The extensively low-dimensional feature classification experimental results demonstrated that the sparse autoencoder is more efficient and reliable than the other selected classical dimensional reduction algorithms.

关键词： Machine learning dimensionality reduction sparse autoencoder supervised learning feature representation

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A hybrid Intrusion Detection System based on sparse autoencoder and Deep Neural Network

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COMPUTER COMMUNICATIONS 2021年 180卷 77-88页

作者： Rao, K. Narayana Rao, K. Venkata Reddy, P. V. G. D. Prasad Andhra Univ Coll Engn A Dept Comp Sci & Syst Engn Visakhapatnam AP India

A large number of attacks are launched daily in the era of the internet and with a large number of users. Nowadays, effective detection of numerous attacks using the Intrusion Detection System (IDS) is an emerging research technique. Machine learning methodologies show effective results in intrusion detection system. We proposed a two-stage hybrid methodology for intrusion detection. In the first stage, the unsupervised sparse autoencoder (SAE) with smoothed l1 regularization. We employ smoothed l1 regularization to enforce a sparsity of autoencoder. The smoothed l1 regularization is indeed able to learn sparse representations of features. In the second stage, the Deep Neural Network (DNN) was used to predict and classify attacks. The classifier classifies multi attack classification from the extracted features. Unsupervised SAE was optimized to train an efficient model. The experimental results demonstrate that proposed model better than the conventional models in terms of overall performance in detection rate and low false positive rate. The proposed model was assessed on the datasets KDDCup99, NSL-KDD and UNSW-NB15. The model attained the accuracy 99.98% , and detection rate 99.99% on UNSW-NB15 dataset.

关键词： Intrusion Detection sparse autoencoder Deep Neural Network Feature selection

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Post-fault prediction of transient instabilities using stacked sparse autoencoder

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 164卷 243-252页

作者： Mahdi, Mohammed Genc, V. M. Istemihan Istanbul Tech Univ Dept Elect Engn TR-34469 Istanbul Turkey

Post-fault prediction of transient stability of power systems has a great impact on the performance of wide area monitoring, protection and control systems. Situational awareness capabilities of a power system are improved by fast detection of instabilities after severe fault occurrences. This allows sufficient time to take necessary corrective control actions. In this paper, a novel method based on stacked sparse autoencoder is proposed to predict the post-fault transient stability status of the power system directly after clearing the fault. A dataset is generated off-line to train a stacked sparse autoencoder, and then the trained stacked sparse autoencoder is used in an online application of predicting any transient instability. The stacked sparse autoencoder is fed by the inputs, which are specific points extracted from the fault-on voltage magnitude measurements collected from the phasor measurement units. The effectiveness of the proposed method is demonstrated and compared with the conventional approaches that adopt multilayer perceptrons or post-fault measurements as it is applied to the 127-bus WSCC test system and to the Turkish power system.

关键词： Deep learning Phasor measurement units Prediction sparse autoencoder Transient stability Wide area measurements

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Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked sparse autoencoder

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JOURNAL OF GRID COMPUTING 2022年第1期20卷 1-16页

作者： Wang, Shui-Hua Satapathy, Suresh Chandra Zhou, Qinghua Zhang, Xin Zhang, Yu-Dong Univ Leicester Sch Math & Actuarial Sci Leicester LE1 7RH Leics England KIIT Deemed Univ Sch Comp Eng Bhubaneswar India Univ Leicester Sch Informat Leicester LE1 7RH Leics England Fourth Peoples Hosp Huaian Dept Med Imaging Huaian 223002 Jiangsu Peoples R China

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% +/- 1.47%, a specificity of 93.13% +/- 0.95%, a precision of 93.15% +/- 0.89%, an accuracy of 93.23% +/- 0.81%, and an F1 score of 93.23% +/- 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

关键词： Secondary pulmonary tuberculosis pseudo-Zernike moment sparse autoencoder Machine learning Deep learning

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Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第13期236卷 7529-7545页

作者： Zhang, Ziqin Wu, Xing Liu, Tao Liu, Xiaoqin Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China Yunnan Vocat Coll Mech & Elect Technol Kunming Yunnan Peoples R China

Planetary gear reducer is widely applied in various transmission equipment, and its performance highly affects the operation of a machine. The appearance of unreasonable backlash in planetary gear reducer may lead to undesirable vibration, which may accelerate the degradation of equipment and eventually cause premature failure. In traditional condition-based monitoring (CBM), sensors such as accelerometers have been utilized to detect the fault of planetary gear. However, the complexity and integration of planetary gear limit the installation and application of vibration sensors in practice. In this case, the current signal from motor, as a convenient real-time monitoring approach, is introduced into the CBM of the planetary gear. In this paper, a fault diagnosis method based on drive motor current signal analysis (MCSA) is presented to identify the backlash fault in a planetary gear reducer. In this method, frequency domain data of the original current signal is found and used to automatically extract fault features. A deep sparse autoencoder (DSAE) extracts the required features. In particular, the Fisher criterion is introduced to evaluate the sensitivity of these features. It then selects the most effective ones for improving diagnostic accuracy as well as diagnostic efficiency. Experimental test data shows that under different load conditions, this method outperforms other typical fault diagnosis methods and exhibits the best performance.

关键词： fault diagnosis gear backlash planetary gear reducer motor current signal analysis sparse autoencoder Fisher criterion feature selection

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SARS-CoV-2 virus classification based on stacked sparse autoencoder

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2023年 21卷 284-298页

作者： Coutinho, Maria G. F. Camara, Gabriel B. M. Barbosa, Raquel de M. Fernandes, Marcelo A. C. Univ Fed Rio Grande do Norte Lab Machine Learning & Intelligent Instrumentat IMD nPITI Natal Brazil Univ Granada Dept Pharm & Pharmaceut Technol Granada 18071 Spain Univ Fed Rio Grande do Norte Dept Comp & Automat Engn Natal Brazil Univ Fed Rio Grande do Norte Dept Comp & Automat Engn Natal Brazil

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. Considering that motivation, the authors proposed an efficient viral genome classifier for the SARS-CoV-2 using the deep neural net-work based on the stacked sparse autoencoder (SSAE). For the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation was applied. We performed four experiments to provide different levels of taxonomic classification of the SARS-CoV-2. The SSAE technique provided great performance results in all experiments, achieving classification accuracy between 92% and 100% for the validation set and between 98.9% and 100% when the SARS-CoV-2 samples were applied for the test set. In this work, samples of the SARS-CoV-2 were not used during the training process, only during subsequent tests, in which the model was able to infer the correct classification of the samples in the vast majority of cases. This indicates that our model can be adapted to classify other emerging viruses. Finally, the results indicated the applicability of this deep learning technique in genome classification problems.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

关键词： COVID-19 Deep learning SARS-CoV-2 sparse autoencoder Viral classification

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Hierarchical fusion network for periocular and iris by neural network approximation and sparse autoencoder

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MACHINE VISION AND APPLICATIONS 2020年第1期32卷 1-10页

作者： Algashaam, Faisal Kien Nguyen Banks, Jasmine Chandran, Vinod Tuan-Anh Do Alkanhal, Mohamed Queensland Univ Technol Brisbane Qld Australia King Abdulaziz City Sci & Technol Riyadh Saudi Arabia Hanoi Univ Sci & Technol Hanoi Vietnam

The eye region is one of the most attractive sources for identification and verification due to the representative availability of such biometric modalities as periocular and iris. Many score-level fusion approaches have been proposed to combine these two modalities targeting to improve the robustness. The score-level approaches can be grouped into three categories: transformation-based, classification-based and density-based. Each category has its own benefits, if combined can lead to a robust fusion mechanism. In this paper, we propose a hierarchical fusion network to fuse multiple fusion approaches from transformation-based and classification-based categories into a unified framework for classification. The proposed hierarchical approach relies on the universal approximation theorem for neural networks to approximate each fusion approach as one child neural network and then ensemble them into a unified parent network. This mechanism takes advantage of both categories to improve the fusion performance, illustrated by an improved equal error rate of the multimodal biometric system. We subsequently force the parent network to learn the representation and interaction strategy between the child networks from the training data through a sparse autoencoder layer, leading to further improvements. Experiments on two public datasets (MBGC version 2 and CASIA-Iris-Thousand) and our own dataset validate the effectiveness of the proposed hierarchical fusion approach for periocular and iris modalities.

关键词： Periocular recognition Iris recognition Hierarchical multimodal fusion Neural network approximation sparse autoencoder

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Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network

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NEUROCOMPUTING 2018年 311卷 1-10页

作者： Xu, Lin Cao, Maoyong Song, Baoye Zhang, Jiansheng Liu, Yurong Alsaadi, Fuad E. Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Yangzhou Univ Dept Math Yangzhou 225002 Jiangsu Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia

This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Fault diagnosis Power rectifier sparse autoencoder Deep neural network Feature extraction

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sparse autoencoder for Facial Expression Recognition 12

Sparse Autoencoder for Facial Expression Recognition

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12th IEEE Int Conf Ubiquitous Intelligence & Comp/12th IEEE Int Conf Autonom & Trusted Comp/15th IEEE Int Conf Scalable Comp & Commun & Associated Workshops/IEEE Int Conf Cloud & Big Data Comp/IEEE Int Conf Internet People

作者： Huang, Binbin Ying, Zilu Wuyi Univ Sch Informat Engn Jiangmen Peoples R China

ISBN: (纸本)9781467372114

Facial expression recognition has become one of the most interesting topics in human computer interaction. A lot of methods have been proposed and studied for facial expression recognition. Among some of these methods, feature extraction is very important. However, feature extraction in these methods involves human intervention more or less which may make the recognition task unstable and not suitable for practical application. To overcome these problems, a deep learning method for facial expression recognition is proposed. First, it divides the training images to 7 groups correspond to 7 expressions to train 7 sparse autoencoder networks. Then, it lets each training group gets thought the network that have been trained by this group, and each training image gets 150 outputs as the training features. In testing step, a testing image is input into the 7 trained networks and gets 150 outputs as the testing features of each network. Comparing the square of error between testing features with the training features in each network, and classifying the testing image into the class with the minimum error. Experiments on JAFFE database has proved the effectiveness of the proposed method.

关键词： Facial expression sparse autoencoder

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Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 4412-4435页

作者： Huang, Jianying Yang, Seunghyeok Li, Jinhui Oh, Jeill Kang, Hoon Chung Ang Univ Sch Elect & Elect Engn 84 Heukseok Ro Seoul 06974 South Korea Chung Ang Univ Dept Civil & Environm Engn 84 Heukseok Ro Seoul 06974 South Korea

Sanitary sewer overflows caused by excessive rainfall derived infiltration and inflow is the major challenge currently faced by municipal administrations, and therefore, the ability to correctly predict the wastewater state of the sanitary sewage system in advance is especially significant. In this paper, we present the design of the sparse autoencoder-based Bidirectional long short-term memory (SAE-BLSTM) network model, a model built on sparse autoencoder (SAE) and Bidirectional long short-term memory (BLSTM) networks to predict the wastewater flow rate in a sanitary sewer system. This network model consists of a data preprocessing segment, the SAE network segment, and the BLSTM network segment. The SAE is capable of performing data dimensionality reduction on high-dimensional original input feature data from which it can extract sparse potential features from the aforementioned high-dimensional original input feature data. The potential features extracted by the SAE hidden layer are concatenated with the smooth historical wastewater flow rate features to create an augmented previous feature vector that more accurately predicts the wastewater flow rate. These augmented previous features are applied to the BLSTM network to predict the future wastewater flow rate. Thus, this network model combines two kinds of abilities, SAE's low-dimensional nonlinear representation for original input feature data and BLSTM's time series prediction for wastewater flow rate. Then, we conducted extensive experiments on the SAE-BLSTM network model utilizing the real-world hydrological time series datasets and employing advanced SVM, FCN, GRU, LSTM, and BLSTM models as comparison algorithms. The experimental results show that our proposed SAE-BLSTM model consistently outperforms the advanced comparison models. Specifically, we selected a 3 months period training dataset in our dataset to train and test the SAE-BLSTM network model. The SAE-BLSTM network model yielded the lowest RMSE, MAE

关键词： Time series prediction sparse autoencoder Bidirectional long short-term memory Data dimensionality reduction Sanitary sewer overflows (SSOs) Rainfall derived infiltration and inflow (RDII)

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