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Detection of Double Compressed AMR Audio Using stacked autoencoder

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2017年第2期12卷 432-444页

作者： Luo, Da Yang, Rui Li, Bin Huang, Jiwu Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

The adaptive multi-rate (AMR) audio codec adopted by many portable recording devices is widely used in speech compression. The use of AMR speech recordings as evidence in court is growing. Nowadays, it is easy to tamper with digital speech recordings, which makes audio forensics increasingly important. The detection of double compressed audio is one of the key issues in audio forensics. In this paper, we propose a framework for detecting double compressed AMR audio based on the stacked autoencoder (SAE) network and the universal background model-Gaussian mixture model (UBM-GMM). Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms. Audio frames are used as network input and the last hidden layer's output constitutes the features of a single frame. For an audio clip with many frames, the features of all the frames are aggregated and classified by UBM-GMM. Experimental results show that our method is effective in distinguishing single/double compressed AMR audio and outperforms the existing methods by achieving a detection accuracy of 98% on the TIMIT database. Exhaustive experiments demonstrate the effectiveness and robustness of the proposed method.

关键词： Adaptive multi-mate double compressed AMR audio forensics stacked autoencoder

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Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 115卷 557-564页

作者： Adem, Kemal Kilicarslan, Serhat Comert, Onur Gaziosmanpasa Univ Dept Informat TR-60250 Tokat Turkey Gaziosmanpasa Univ Tech Sci Vocat Sch TR-60250 Tokat Turkey

Cervical cancer is one of the most common cancer types in the world, which causes many people to lose their lives. Cancer research is of importance since early diagnosis of cancer facilitates clinical applications for patients. The aim of the study is the automatic diagnosis of cervical cancer. For this purpose, a data set containing 668 samples, 30 attributes and 4 target variables (Schiller, Citology, Biopsy and Hinselmann) from the UCI database was used in the training and testing phases. Softmax classification with stacked autoencoder, one of the deep learning methods, has been used to classify data sets. At first, by applying stacked autoencoder to the raw data set, a reduced dimension dataset is obtained. This data set has been trained for classification by applying the softmax layer. In this phase, 70% (468) of the data set was allocated for training and the remaining 30% (200) for testing. In order to compare the classification performance of the softmax classification with stacked autoencoder, decision tree, kNN, SVM, Feed Forward NN, Rotation Forest models, which are machine learning methods, are used. In the study, proposed models were applied separately to 4 target variables of the dataset, and their classification successes were compared. Finally, softmax classification with stacked autoencoder model, which was applied for the first time in the cervical cancer dataset, performed better than the other machine learning methods with a correct classification rate of 97.8%. Given the interest in machine learning methods in cancer research, new methods of diagnosis are presented in this study in terms of patient diagnostic support systems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Cervical cancer Machine learning Deep learning stacked autoencoder

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Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive stacked autoencoder

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IEEE ACCESS 2020年 8卷 190431-190447页

作者： Zhang, Guoling Wang, Xiaodan Li, Rui Song, Yafei He, Jiaxing Lai, Jie Air Force Engn Univ Coll Air & Missile Def Xian 710051 Peoples R China

In the field of intrusion detection, there is often a problem of data imbalance, and more and more unknown types of attacks make detection difficult. To resolve above issues, this article proposes a network intrusion detection model called CWGAN-CSSAE, which combines improved conditional Wasserstein Generative Adversarial Network (CWGAN) and cost-sensitive stacked autoencoders (CSSAE). First of all, the CWGAN network that introduces gradient penalty and L2 regularization is used to generate specified minority attack samples to reduce the class imbalance of the training dataset. Secondly, the stacked autoencoder is used to intelligently extract the deep abstract features of the network data. Finally, a cost-sensitive loss function is constructed to give a large misclassification cost to a minority of attack samples. Thus, effective detection of network intrusion attacks can be realized. The experimental results based on KDDTest(+), KDDTest-21, and UNSW-NB15 datasets show that the CWGAN-CSSAE network intrusion detection model improves the detection accuracy of minority attacks and unknown attacks. In addition, the method in this article is compared with other existing intrusion detection methods, excellent results have been achieved in performance indicators such as accuracy and F1 score. The accuracy on the above datasets reached 90.34%, 80.78% and 93.27% respectively. The accuracy of U2R on the KDDTest(+) and KDDTest21 datasets both reached 42.50%. The accuracy of R2L on the KDDTest(+) and KDDTest-21 datasets reached 54.39% and 52.51%, respectively. And the F1 score on the above datasets reached 91.01%, 87.18% and 93.99% respectively.

关键词： Intrusion detection conditional Wasserstein GAN stacked autoencoder imbalanced classification cost-sensitive regularization deep learning

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SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

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BMC BIOINFORMATICS 2021年第1期22卷 1-18页

作者： Liu, Dayun Huang, Yibiao Nie, Wenjuan Zhang, Jiaxuan Deng, Lei Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA

Background Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. Results In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. Conclusion The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

关键词： miRNA-disease associations stacked autoencoder Latent feature XGBoost

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Mutual stacked autoencoder for unsupervised fault detection under complex multi-residual correlations

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Yu, Jianbo Lv, Zhaomin Ruan, Hang Hu, Shijie Jiang, Qingchao Yan, Xuefeng Liu, Yuping Yang, Xiaofeng Fudan Univ Sch Microelect Shanghai 200433 Peoples R China Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai 201620 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Due to the increasing complexity of variable relationships, fault detection has garnered significant attention, as it is crucial for ensuring industrial safety and engineering reliability. Traditional detection methods can be classified as twofold: global-based and local-based strategies, which respectively focus on mining macro- and micro-level information. However, our theoretical derivation and experiment results reveal that some spurious assumptions, such as local groups and their provided information are mutually independent are implicitly adhered to but are hardly satisfied in unsupervised fault detection under real industrial scenarios. Hence, this study introduces a novel mutual stacked autoencoder (M-SAE) which can be divided into three subnetworks: L-Net, R-Net, and M-Net. L-Net enriches local information learning through multiple local backbones by incorporating the unsupervised clustering algorithm. R-Net, employing a multi-scale attention mechanism, leverages complete local information for residual strength calculation and utilizes local features to capture residual information within the latent feature space. M-Net fuses the multi-scale local feature information to perform a reconstruction for each local. A multitask entropy-aided loss function is introduced to enrich local details, the global structure, and the residual associations. Finally, results on eleven datasets validate the high-performance of the proposed M-SAE and the ablation experiments demonstrate the efficacy of each component in M-SAE, confirming that this research effectively and accurately addresses multivariable industrial fault detection tasks, thereby enabling timely interventions that are crucial for maintaining operational safety in real-world scenarios.

关键词： Fault detection Global-based strategy Local-based strategy stacked autoencoder

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Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

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Petroleum Science 2021年第5期18卷 1465-1482页

作者： Jaejun Kim Changhyup Park Seongin Ahn Byeongcheol Kang Hyungsik Jung Ilsik Jang Department of Energy Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Energy and Resources Engineering Kangwon National UniversityChuncheonKangwon24341Republic of Korea Geo-ICT Convergence Research Team Korea Institute of Geoscience and Mineral ResourcesDaejeon34132Republic of Korea Department of Energy and Resources Engineering Chosun UniversityGwangju61452Republic of Korea

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history *** proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering *** functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs *** iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration *** proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population *** learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata *** confirms the proposed workflow constructs more plausible *** workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

关键词： Deep neural network stacked autoencoder History matching Iterative learning Clustering Many-objective

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Deep stacked autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 534-548页

作者： Pan, Guangliang Wu, Qihui Ding, Guoru Wang, Wei Li, Jie Xu, Fuyuan Zhou, Bo Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China Nanjing Elect Equipment Res Inst Nanjing 210000 Peoples R China

Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifically, a SAE is designed to extract the hidden features (semantic coding) of spectrum data in an unsupervised manner. Then, the output of SAE is connected to a predictor (Bi-LSTM), which is used for long-term prediction by learning hidden features. The main advantage of SAEL-SP is that the underlying features of spectrum data can be retained automatically, layer by layer, rather than designing them manually. To further improve the prediction accuracy of SAEL-SP and achieve a wider bandwidth prediction, we propose a SAE-based spectrum prediction method using temporal-spectral-spatial features of data (SAE-TSS). Different from SAEL-SP, the input of SAE-TSS is the image format. SAE-TSS achieves higher prediction accuracy than SAEL-SP using the features extracted from time, frequency, and space dimensions. We use a real-world spectrum dataset to validate the effectiveness of two prediction frameworks. Experiment results show that both SAEL-SP and SAE-TSS outperform existing spectrum prediction approaches.

关键词： Predictive models Feature extraction Hidden Markov models Time-frequency analysis Frequency measurement Autoregressive processes Data models Spectrum prediction temporal-spectral-spatial correlations deep learning stacked autoencoder

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A Noise Estimation Method for Hyperspectral Image Based on stacked autoencoder

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IEEE ACCESS 2023年 11卷 89835-89843页

作者： Deng, Lei Zhou, Bing Ying, Jiaju Zhao, Runze Army Engn Univ PLA Elect & Opt Engn Dept Shijiazhuang 050003 Peoples R China Army Engn Univ PLA Equipment Command & Management Dept Shijiazhuang 050003 Peoples R China

The imaging spectrometer is limited by a short response time and narrow channel, resulting in a low signal-to-noise ratio of hyperspectral images. The accurate estimation of noise has a significant impact on some preprocessing and downstream tasks. The existing noise estimation methods for hyperspectral images are all focused on satellite and aviation data, and there is little research on hyperspectral images with high spatial resolution. For this type of image, This article proposes a noise estimation method based on a stacked autoencoder. Firstly, the image is divided into multiple uniform regions using the K-means algorithm, and then a stacked automatic encoder is set for each uniform region. Reconstruct the spectral signal on each pixel through the corresponding stacked automatic encoder. Calculate the residual between the reconstructed image and the original image to achieve signal-to-noise separation. Finally, the image is divided into a large number of subblocks, and the subblocks containing edges are removed. The remaining subblocks are used for noise estimation in this band. The applicability of some classic noise estimation methods was experimentally tested, and the effectiveness and stability of the proposed method were verified through simulation and real data experiments.

关键词： Hyperspectral imaging Estimation Image reconstruction Image edge detection Training Spatial resolution Noise measurement Encoding Hyperspectral images noise estimation stacked autoencoder K-means algorithm

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Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第7期14卷 1242-1250页

作者： Wang, Ying Lu, Hong Xiao, Xianyong Yang, Xiaomei Zhang, Wenhai Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China

Cable incipient fault is an intermittent arc fault, and may evolve into a permanent fault. Due to the short duration of the fault, the conventional overcurrent protection device cannot detect it. A cable incipient fault identification method is proposed in this study, using restricted Boltzmann machine (RBM) and stacked autoencoder (SAE). Firstly, disturbance current waveforms data is effectively compressed by RBM, which can improve analysis efficiency and obtain the shallow features of the data. Then, the compressed data is used as the input of SAE, and the optimal network parameters are obtained through layer-by-layer pre-training and fine-tuning. Finally, a well-trained SAE network is used to learn deep features from the input data to identify cable incipient fault, and softmax outputs identification result. In addition, the performance of the proposed method is compared with other methods. The accuracy of the proposed method is 98.33/95.62% for simulated data/measured data, and is 1.66/1.09%, 3.33/1.76%, 17.31/28.48% and 40.17/46.1% higher than the accuracies of convolutional neural network, deep belief network, random forest and back propagation neural network, respectively. The results show that the proposed method has high identification accuracy and feasibility.

关键词： Boltzmann machines backpropagation neural nets overcurrent protection fault diagnosis feature extraction belief networks arcs (electric) learning (artificial intelligence) power distribution faults restricted Boltzmann machine stacked autoencoder intermittent arc fault permanent fault conventional overcurrent protection device cable incipient fault identification method RBM disturbance current waveforms data compressed data layer-by-layer pre-training SAE network identification result high identification accuracy

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Multi-label classification using a cascade of stacked autoencoder and extreme learning machines

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NEUROCOMPUTING 2019年 358卷 222-234页

作者： Law, Anwesha Ghosh, Ashish Indian Stat Inst Machine Intelligence Unit 203 BT Rd Kolkata 700108 India

This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhance the performance of a stand-alone multi-label extreme learning machine (MLELM) by collaborating it with other networks. There are three basic phases in the proposed method: feature encoding, soft classification and class score approximation. In the first step, an SAE network is employed to generate a discriminating and reduced input representation of the multi-label data. This makes the data compact and more manageable for the successive stages. This data in turn is used by an MLELM in the next phase for the prediction of soft labels. In the final step, to improve the prediction capability of the previous network, a novel approach of approximating the class score is proposed using an additional MLELM. Comprehensive experimental evaluation of the proposed approach has been performed on seven datasets against eleven relevant algorithms, and overall it displays a promising performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-label classification Extreme learning machine stacked autoencoder Multi-label extreme learning machine

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