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Printed Odia Numeral Recognition Using stacked autoencoder

Procedia Computer Science 2019年 154卷 790-797页

作者： Subhashree Satpathy Ajit Ku. Nayak Mamata Nayak Srikanta Patnaik

Automatic recognition of both printed and handwritten characters is the most progressive research area since last few periods. The printed character recognition rate still desires concentration of researchers because of variations in shape, size and design of characters. Odia is the native language of Odisha. Less works have been accounted in Odia script classification. Complex printed character recognition constantly demands a better feature extraction method for such diversified characters. Purpose of this manuscript is to apply autoencoder technique considering a suitable classifier for Odia printed numeral recognition. autoencoder proved to be a better method in performance for dimensionality reduction as well as classification of Odia numerals.

关键词： - Odia numerals character recognition stacked autoencoder deep learning dimensionality reduction

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Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression

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JOURNAL OF ENERGY STORAGE 2022年 47卷

作者： Wei, Meng Ye, Min Wang, Qiao Xinxin-Xu Twajamahoro, Jean Pierre Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710064 Shaanxi Peoples R China Univ Rwanda Kigali 3900 Rwanda Coll Sci & Technol Kigali 3900 Rwanda

Lithium-ion batteries have been widely applied in energy storage systems and electric vehicles (EVs), the remaining useful life (RUL) prediction is one of the critical technologies for prognostics and health management. However, high accuracy RUL prediction with reliability is the biggest bottleneck. To improve RUL prediction and adaptively extract indirect health indicators (HIs), the RUL prediction framework based on the stacked autoencoder and Gaussian mixture regression (SAE-GMR) is proposed. Firstly, the indirect HIs are extracted from charging and discharging data, and the gray relation analysis (GRA) is adopted to analyze the relation with capacity. In this paper, the SAE neural network is proposed to reduce the dimensions and noise of battery and obtain a syncretic HI. Then, the GMR model is estiblished not only to improve accuracy of RUL prediction, but also describe the reliability. Finally, the proposed method is compared with esixting methods,which shows that the proposed model has superiority for other methods.

关键词： Energy storage systems Lithium-ion batteries Remaining useful life stacked autoencoder Gaussian mixture regression

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Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization

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AIN SHAMS ENGINEERING JOURNAL 2024年第6期15卷

作者： Manimurugan, S. Karthikeyan, P. Aborokbah, Majed Narmatha, C. Ganesan, Subramaniam Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia RV Univ Sch Comp Sci & Engn Bengaluru India Oakland Univ Sch Engn & Comp Sci Dept Elect & Comp Engn Rochester MI USA

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including preprocessing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrastlimited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inceptionv3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%.

关键词： Breast cancer CAD INBreast Res-SegNet VGG-19 stacked autoencoder

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Multi-DoF continuous estimation for wrist torques using stacked autoencoder

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期57卷 101733-101733页

作者： Yu, Yang Chen, Chen Sheng, Xinjun Zhu, Xiangyang Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Human machine interface (HMI) based on surface electromyography (sEMG) promises to provide an intuitive and noninvasive way to interact with peripheral equipments, such as prostheses, exoskeletons, and robots. Most recently, advances in machine learning, especially in deep learning algorithms, present the capabilities in constructing complicated mapping functions. In this study, we construct a stacked autoencoder-based deep neural network (SAE-DNN) to continuously estimate multiple degrees-of-freedom (DoFs) kinetics of wrist from sEMG signals. During the experiments, high-density sEMG signals and multi-DoF wrist torques were simultaneously acquired under the guidance of a visual feedback system, with eight healthy subjects and an amputee recruited. Moreover, the estimation performance of SAE-DNN was compared with two of commonly used conventional regressors, linear regression (LR) and support vector regression (SVR). As a consequence, the results demonstrate the feasibility of this scheme and significant superiority of SAE-DNN over LR and SVR with higher R-2 values across all DoFs (SAE-DNN: 0.829 +/- 0.050, LR: 0.757 +/- 0.075, SVR: 0.751 +/- 0.079). The outcomes of this study provide us with a perspective and a feasible scheme for simultaneous and proportional control. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Human machine interface Simultaneous and proportional control stacked autoencoder

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Predictor Discovery for Early-late Indian Summer Monsoon Using stacked autoencoder

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Procedia Computer Science 2016年 80卷 565-576页

作者： Moumita Saha Pabitra Mitra Ravi S. Nanjundiah Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Centre for Atmospheric and Oceanic Sciences Divecha Centre for Climate Change Indian Institute of Science Bangalore India

Indian summer monsoon has distinct behaviors in its early and late phase. The influencing climatic factors are also different. In this work we aim to predict the national rainfall in these phases. The predictors used by the forecast models are discovered using a stacked autoencoder deep neural network. A fitted regression tree is used as the forecast model. A superior accuracy to state of art method is achieved. We also observe that the late monsoon can be predicted with higher accuracy than early monsoon rainfall.

关键词： Feature learning Monsoon predictors stacked autoencoder Early and Late Indian summer monsoon

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Radar HRRP Target Recognition Based on stacked autoencoder and Extreme Learning Machine

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SENSORS 2018年第1期18卷 173-173页

作者： Zhao, Feixiang Liu, Yongxiang Huo, Kai Zhang, Shuanghui Zhang, Zhongshuai Natl Univ Def Technol Coll Elect Sci Changsha 410073 Hunan Peoples R China

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

关键词： radar target recognition high-resolution range profile deep learning extreme learning machine stacked autoencoder

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Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a stacked autoencoder

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SENSORS 2017年第1期17卷 192-192页

作者： Kang, Miao Ji, Kefeng Leng, Xiangguang Xing, Xiangwei Zou, Huanxin Natl Univ Def Technol Sch Elect Sci & Engn Sanyi Ave Changsha 410073 Hunan Peoples R China Beijing Inst Remote Sensing Informat Beijing 100192 Peoples R China

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.

关键词： SAR target recognition feature fusion stacked autoencoder MSTAR

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Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using stacked autoencoder

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2017年第2期4卷 124-128页

作者： Tomonaga, Kenta Hayakawa, Takuya Kobayashi, Jun Kyushu Inst Technol Dept Syst Design & Informat Kawazu 680-4 Iizuka Fukuoka 8208502 Japan

This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction measured by a portable EEG headset. In the authors' previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a stacked autoencoder (SAE) for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.

关键词： electroencephalography stacked autoencoder neural network portable EEG headset imagination of direction

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A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 102卷

作者： Zhao, Jinling Hu, Lei Dong, Yingying Huang, Linsheng Weng, Shizhuang Zhang, Dongyan Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applic 111 Jiulong Rd Hefei 230601 Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China

In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network (3DCNN) was then designed and the residual network module was introduced to build a 3DDRN. The dimension-reduced 3D HSI cubes were input into the 3DDRN to extract identifiable joint spectral-spatial features. Finally, the deep features continuously identified by the 3DDRN were input to Softmax classification layer to realize the classification. In addition, Batch Normalization (BN) and Dropout were used during the learning process to avoid overfitting on training data. The training and test sets of Indian Pines (IP), Pavia University (PU) and Salinas (SA) hyperspectral data sets were selected as the modeling and verification data sources. Six classical classification algorithms were adopted for comparing our proposed method, specifically including conventional machine learning algorithms of Radial Basis FunctionSupport Vector Machine (RBF-SVM), Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP) and Local Binary Pattern-K-Nearest Neighbor (LBP-KNN), and mainstream deep learning algorithms of Variational autoencoder (VAE), Convolutional Neural Network (CNN) and Spectral-Spatial Residual Network (SSRN). The results showed that the overall accuracy (OA) reached 98.97%, 99.69% and 99.24%, respectively, only based on 10%, 5% and 1% of training samples for IP, PU and SA. Consequently, the proposed method shows a better classification performance, even in the case of limited samples.

关键词： Deep learning Hyperspectral remote sensing Residual network 3D convolutional neural network Spectral-spatial features stacked autoencoder

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Android Malware Detection Approach Using stacked autoencoder and Convolutional Neural Networks

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2023年第1期19卷 1页

作者： Menaouer, Brahami Islem, Abdallah El Hadj Mohamed Nada, Matta Natl Polytech Sch Oran LABAB Lab Comp Sci M Audin Algeria Natl Polytech Sch Oran Comp Syst Engn Dept Oran Algeria Univ Technol Troyes TechCICO Lab Comp Sci Troyes France Univ Technol Troyes Dept Comp Sci Troyes France

In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works.

关键词： Android Security Classification CNN Dimensionality Reduction Features Extraction Knowledge Management Malware Detection Mobile Malware Smartphone Security stacked autoencoder

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