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stacked autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 470-476页

作者： Kamalakannan, K. Anandharaj, G. Adhiparasakthi Coll Arts & Sci PG & Res Dept Comp Sci Kalavai TN India

Parkinson's disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson's are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson's disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the stacked autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System - Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithm

关键词： Immune algorithms Parkinson's disease stacked autoencoder airs-parallel machine learning

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Spatiotemporal stacked autoencoder based Soft Sensor Modeling for the Dow Data Challenge Problem 9

Spatiotemporal Stacked Autoencoder based Soft Sensor Modelin...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Xiuli Damarla, Seshu Kumar Huang, Biao University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China University of Alberta Department of Chemical and Materials Engineering AB Canada

ISBN: (纸本)9798350319804

Process data with characteristics such as strong nonlinearity, high dimensionality, cross-correlations and auto correlations pose a great challenge for data-driven soft sensor modeling. Albeit the conventional stacked autoencoder (SAE) is able to learn low dimensional nonlinear features from the process data, it fails to extract spatial and temporal features. To overcome this shortcoming, in the present work, spatiotemporal SAE (STSAE) is proposed based on convolutional and LSTM layers. Lower dimensional nonlinear features (spatial and temporal) extracted from STSAE are used to build the soft sensor. The effectiveness of the STSAE-based soft sensor is demonstrated in an industrial case study, the Dow data challenge problem. For the purpose of comparison, soft sensor models based on popular machine learning algorithms are developed and applied to the industrial case study. The obtained results demonstrate that the proposed STSAE based soft sensor yields more accurate estimations for impurity level at the primary column outlet of Dow process refining system. © 2023 IEEE.

关键词： autoencoder CNN Dow data challenge problem LSTM stacked autoencoder

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Highway tollgates traffic prediction using a stacked autoencoder neural network

Highway tollgates traffic prediction using a stacked autoenc...

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作者： OSKAR KARRMAN LINNEA OTTERLIND Chalmers University of Technology

学位级别：硕士

Traffic flow prediction is an important area of research with a great number of applications such as route planning and congestion avoidance. This thesis explored artificial neural network performance as travel time and traffic volume predictors. stacked autoencoder artificial neural networks were studied in particular due to recent promising performance in traffic flow prediction, and the result was compared to multilayer perceptron networks, a type of shallow artificial neural networks. The Taguchi design of experiments method was used to decide network parameters. stacked autoencoder networks generally did not perform better than shallow networks, but the results indicated that a bigger dataset could favor stacked autoencoder networks. Using the Taguchi method did help cut down on number of experiments to test, but choosing network settings based on the Taguchi test results did not yield lower error than what was found during the Taguchi tests.

关键词： stacked autoencoder multilayer perceptron neural network traffic prediction traffic flow taguchi

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An Integrative Remote Sensing Application of stacked autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

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REMOTE SENSING 2020年第7期12卷

作者： Pyo, JongCheol Duan, Hongtao Ligaray, Mayzonee Kim, Minjeong Baek, Sangsoo Kwon, Yong Sung Lee, Hyuk Kang, Taegu Kim, Kyunghyun Cha, YoonKyung Cho, Kyung Hwa Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn Ulsan 689798 South Korea Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Natl Inst Environm Res Water Qual Assessment Res Div Environm Res Complex Incheon 22689 South Korea Natl Inst Environm Res Watershed & Total Load Management Res Div Incheon 22689 South Korea Univ Seoul Sch Environm Engn Seoul 130743 South Korea

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash-Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.

关键词： deep learning stacked autoencoder cyanobacteria hyperspectral image feature extraction dimensionality reduction

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Anomaly Detection for HTS Cable Using stacked autoencoder and Reflectometry

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2023年第5期33卷

作者： Bang, Su Sik Kwon, Gu-Young Tech Univ Korea Dept Elect Engn Shihung Gyeonggi Do South Korea Dongguk Univ Dept Smart Safety Engn Gyeongju South Korea

Since a high-temperature superconducting (HTS) cable has a cooling process and the possibility of a quench, a real-time monitoring technique is essential than other conventional power cable. Conventional reflectometry-based monitoring parameters have inherent disadvantages in that the boundary between normal and abnormal cannot be determined and multiple baselines cannot be considered during the monitoring process. In this paper, a stacked autoencoder-based anomaly detection technique for HTS cable is newly proposed. The autoencoder monitors the condition of HTS cable in real-time by calculating an anomaly score from the input data. The proposed method can automatically set the boundary between normal and abnormal and has the advantage of using multiple baselines. The performance of the proposed method is verified by emulating a local quench in a testbed containing a commercial 22.9 kV HTS cable and compared with that of the conventional monitoring parameter. Based on the advantage of being able to set multiple baselines, it is expected that the proposed technique can be used for real-time monitoring of HTS cable to enhance the reliability of the systems.

关键词： Superconducting cables High-temperature superconductors Monitoring Power cables Training Reflectometry Anomaly detection high-temperature superconducting (HTS) cable quench reflectometry stacked autoencoder

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A stacked autoencoder based gene selection and cancer classification framework

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年 78卷

作者： Gokhale, Madhuri Mohanty, Sraban Kumar Ojha, Aparajita Jabalpur Engn Coll Dept Comp Sci & Engn Jabalpur 482001 India PDPM Indian Inst Informat Technol Design & Mfg Comp Sci & Engn Jabalpur 482005 India

Cancer is one of the most common causes of death worldwide and is, therefore, a prominent area of biomedical research. Cancer is a genetic disease in which improperly functioning genes tend to change expressions. Thus, gene expression analysis is utilized for early diagnosis of cancer prognosis, and therapy prediction in a clinical environment. Usually, some dominant genes among thousands of them play an important role in the diagnosis of cancer. But designing a suitable framework to find out the key set of genes is a challenging task. Numerous gene selection approaches have been introduced by researchers for cancer classification, using statistical, or traditional feature selection methods. In recent years, deep learning methods have also been applied for gene selection using autoencoder networks. However, improving the accuracy of cancer classification still remains a challenging task. In the present paper, a stacked autoencoder-based framework is proposed for gene selection and cancer classification. Nine different classifiers are employed to evaluate the performance of the gene selection model. Then the best performing combination of gene selection and cancer classification models are chosen to finally select the genes. Random Forest and Support Vector Machine show better performance on ten different benchmark datasets, when the gene selection is done using the stacked autoencoder. The classifier with the highest accuracy is selected to build the cancer classification model. The proposed model outperforms seven existing methods on all the ten datasets.

关键词： Gene selection Cancer classification stacked autoencoder

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Quantum stacked autoencoder fault diagnosis model for bearing faults

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INSIGHT 2023年第11期65卷 631-638页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut NUAA Coll Energy & Power Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

The use of neural network models to monitor bearing vibration signals can easily be affected by noise, which leads to a decrease in the model test accuracy. Therefore, the existence of noise problems increases the requirements for non-linear mapping capability and robustness of deep neural network (DNN) models. In order to deal with the noise problem, the concept of qubit neurons is introduced into a deep learning stacked autoencoder (SAE) model, and a quantum stacked autoencoder (QSAE) model based on qubits and quantum gates is proposed. The properties of SAE layer-by-layer coding and the arithmetic of qubit neurons are combined in the QSAE. The quantum state signal is taken as the input signal to the encoder and the coding activation function and coding weight matrix are redefined by quantum-controlled non-gates and quantum revolving gates, so that the quantum state signal can be coded layer by layer. Experimental results show that the QSAE can train and diagnose noisy experimental data and maintain high test accuracy in an anti-attack test. This shows that the QSAE has non-linear mapping capability and robustness.

关键词： stacked autoencoder quantum neural network non-linear mapping robustness bearing fault

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A hybrid model of stacked autoencoder and modified particle swarm optimization for multivariate chaotic time series forecasting

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APPLIED SOFT COMPUTING 2022年 116卷

作者： Xu, Xinghan Ren, Weijie Kyoto Univ Dept Environm Engn Kyoto 6158540 Japan Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Heilongjiang Peoples R China

Phase space reconstruction (PSR) is an effective method for chaotic system modeling, which can reveal the implicit evolution information in a complex system. However, the reconstructed time series tend to have a high dimension and contain some redundant information. It is difficult for a traditional simple model to directly forecast the reconstructed time series. In this paper, we propose a hybrid model of stacked autoencoder (SAE) and modified particle swarm optimization (MPSO) for multivariate chaotic time series forecasting. We utilize SAE to extract the reconstructed time series and adopt feedforward neural network (FNN) to forecast time series. In the proposed hybrid model, the SAE is followed by FNN, and we make the MPSO to train the output weights of the model, which is a large-scale optimization problem. To enhance the generalization ability and prevent over-fitting, we add a regularization item to the objective function when MPSO trains the weights of the model. Experimental results show that MPSO algorithm has advantages in the exploration and exploitation in large-scale optimization problems. Then, experiments on Lorenz time series and two real-world time series datasets verify the effectiveness of the hybrid model in multivariate chaotic time series forecasting. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multivariate chaotic time series Particle swarm optimization stacked autoencoder

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Nonlocal, local and global preserving stacked autoencoder based fault detection method for nonlinear process monitoring

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年 235卷

作者： Yang, Jingchao Wang, Li Shanghai Inst Technol Sch Elect & Elect Engn Shanghai 200230 Peoples R China

In recent years, the application of deep learning methods has improved the ability of nonlinear feature extraction in industrial processes. However, most of the deep learning methods cannot consider the manifold structure-related information (i.e., nonlocal, local and global manifold structure) of the data. To extract the vital structure-related features, a novel local, nonlocal and global preserving stacked autoencoder (NLGPSAE) for nonlinear process monitoring is proposed. NLGPSAE has an ability to extract crucial structure-related features by introducing a new regularized objective function with local, nonlocal and global structural information. For the preservation of local structure, NLGPSAE can preserve the original neighbor data points to be neighbor data points in the reconstructed space;for the preservation of nonlocal structures, NLGPSAE projects the nonlocal data points to be far apart in the reconstructed space;for the preservation of the global structure, the distance relationship between the original data points and the center of the data points is preserved in the reconstructed space. Two statistics Hotelling's T-squared (T2) and squared prediction error (SPE) based on features extracted by NLGPSAE are established for fault detection. The effectiveness of the proposed algorithm is verified in a complex numerical process and Tennessee Eastman process.

关键词： Nonlinear feature extraction Fault detection stacked autoencoder Structure preserving projection

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Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with stacked autoencoder

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REMOTE SENSING 2021年第9期13卷

作者： Madani, Hadis McIsaac, Kenneth Western Univ Elect & Comp Engn Dept London ON N6A 3K7 Canada

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

关键词： hyperspectral image classification spectral-spatial feature vector distance transform stacked autoencoder

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