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Face recognition algorithm based on stack denoising and self-encoding LBP

JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 501-510页

作者： Lu, Yanjing Khan, Mudassir Ansari, Mohd Dilshad Zhengzhou Tech Coll Zhengzhou 450121 Henan Peoples R China King Khalid Univ Coll Sci & Arts Tanumah Dept Comp Sci Abha Saudi Arabia CMR Coll Engn & Technol Hyderabad India

To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm;it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.

关键词： face recognition stacked autoencoder LBP operator denoising

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Index tracking through deep latent representation learning

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QUANTITATIVE FINANCE 2020年第4期20卷 639-652页

作者： Kim, Saejoon Kim, Soong Sogang Univ Dept Comp Sci & Engn 35 Baekbeom Ro Seoul 04107 South Korea

We consider the problem of index tracking whose goal is to construct a portfolio that minimizes the tracking error between the returns of a benchmark index and the tracking portfolio. This problem carries significant importance in financial economics as the tracking portfolio represents a parsimonious index that facilitates a practical means to trade the benchmark index. For this reason, extensive studies from various optimization and machine learning-based approaches have ensued. In this paper, we solve this problem through the latest developments from deep learning. Specifically, we associate a deep latent representation of asset returns, obtained through a stacked autoencoder, with the benchmark index's return to identify the assets for inclusion in the tracking portfolio. Empirical results indicate that to improve the performance of previously proposed deep learning-based index tracking, the deep latent representation needs to be learned in a strictly hierarchical manner and the relationship between the returns of the index and the assets should be quantified by statistical measures. Various deep learning-based strategies have been tested for the stock market indices of the S&P 500, FTSE 100 and HSI, and it is shown that our proposed methodology generates the best index tracking performance.

关键词： Index tracking Index replication Deep learning Deep autoencoder stacked autoencoder

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CapsNet-LDA: predicting IncRNA-disease associations using attention mechanism and capsule network based on multi -view data

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac531-bbac531页

作者： Zhang, Zequn Xu, Junlin Wu, Yanan Liu, Niannian Wang, Yinglong Liang, Ying Jiangxi Agr Univ Coll Comp & Informat Engn Nanchang Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha Peoples R China

Cumulative studies have shown that many long non -coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential IncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low -dimensional representations of the IncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi -view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

关键词： multi-view stacked autoencoder attention mechanism capsule network lncRNA-disease association prediction

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DIAGNOSIS OF THE PARKINSON DISEASE BY USING DEEP NEURAL NETWORK CLASSIFIER

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2017年第2期17卷 3311-3318页

作者： Caliskan, Abdullah Badem, Hasan Basturk, Alper Yuksel, Mehmet Emin Erciyes Univ Biomed Engn Kayseri Turkey Erciyes Univ Comp Engn Kayseri Turkey Sutcu Imam Univ Comp Engn Kahramanmaras Turkey

Parkinson disease occurs when certain clusters of brain cells are unable to generate dopamine which is needed to regulate the number of the motor and non-motor activity of the human body. Besides, contributing to speech, visual, movement, urinary problems, Parkinson disease also increases the risks of depression, anxiety, and panic attacks, disturbances of sleep. Parkinson disease diagnosis via proper interpretation of the vocal and speech data is an important classification problem. In this paper, a Parkinson disease diagnosis is realized by using the speech impairments, which is one of the earliest indicator for Parkinson disease. For this purpose, a deep neural network classifier, which contains a stacked autoencoder and a softmax classifier, is proposed. The several simulations are performed over two databases to demonstrate the effectiveness of the deep neural network classifier. The results of the proposed classifier are compared with the results of the state-of-art classification method. The experimental results and statistical analyses are showed that the deep neural network classifier is very efficient classifier for Parkinson disease diagnosis.

关键词： Parkinson disease deep learning deep neural network stacked autoencoder

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Intelligent Fault Detection Scheme for Rolling Bearing Based on Generative Adversarial Network and autoencoders Using Convolutional Neural Network

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第8期12卷 8979-8991页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India

Fault detection in early operational stages of rolling bearing is crucial for reliable and safe functioning of rotating machinery. Implementation of intelligent fault detection techniques involving deep learning methods enable automatic feature extraction and selection from raw vibration data provides accurate results. The shortage of enough historical data limits the application of deep learning. Therefore, to solve this problem, in this paper data augmentation method is implemented to generate new data that having greater similitude with the real data for better training of deep learning model for fault detection. For this purpose, WGAN (Wasserstein generative adversarial network) is implemented as imbalanced data augmentation method. Also SAE (stacked autoencoder) is implemented to obtain the latent representation of raw vibration data which is used as noise vector to train WGAN. This has greatly improved the quality of data generation from WGAN. The quality assessment of generated samples is quantified by implementing metrics such as KLD (Kullback-Leibler divergence) and NCC (normalized cross-correlation). The comparison with conventional data generation methods such as VAE, and GAN proves the superior quality of data generation by SAE-WGAN. Test rig experiments are used to gather the vibration data, and deep convolutional neural networks are used to classify the faults (DCNN). The ROC (receiver operating characteristic) curve and performance evaluation metrics like precision, recall, and F1-score amply demonstrated the excellent discriminative power of the suggested methodology for fault detection. Hence the proposed work successfully implemented as condition monitoring tool for early fault detection in rotating machinery.

关键词： Deep convolutional neural network Normalized cross-correlation stacked autoencoder Generative adversarial network ROC AUC (area under Curve)

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CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第6期17卷 3063-3071页

作者： Priyadharshini, G. Ukrit, M. Ferni SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur India

The evolution of new trends in the automobile industry is creating more comfortable and convenient means of transportation. But still there exist manifold challenges in detecting legal and illegal drivers;therefore, evaluating the behaviors of drivers needs to be addressed. Taking these into consideration his paper proposes a novel cascade region convolutional neural network-based circulatory system optimization algorithm (CRCNN++ based CSO) to attain optimal multitask framework which includes behavior evaluation, identity authentication of drivers, vehicle diagnosis as well as estimating the fault of the vehicle. In this paper, two diverse naturalistic driving behavior public datasets namely HCRL and UAH drive datasets are collected and pre-processed via normalization as well as scaling process. The preprocessed feature is then extracted and the dimensions are minimized using the stacked autoencoder technique. The CRCNN++-based CSO is employed in determining to multitask which includes identity authentication, behavioral evaluation, vehicle diagnosis, and faults estimation is performed. Finally, the efficiency of the proposed CRCNN++-based CSO method is analyzed by evaluating various metrics namely receiver operating characteristic curve, accuracy, false positive rate, precision, Cohen Kappa score, true positive rate, and F1-Score. The comparative analysis is carried out for various existing techniques and the proposed approach. From the evaluation results, it is revealed that the proposed CRCNN++-based CSO approach delivers better performance in driver identification through driving style behavior.

关键词： Cascade region Convolutional neural network stacked autoencoder Datasets Driver Authentication

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Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale

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INFORMATION SCIENCES 2022年 591卷 381-399页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is the observed input data should better be interpreted as generating from joint interaction of static and dynamic feature sources. Therefore, the traditional single static-based or dynamic-based methods will inevitably lose some of the important information features, which will result in unsatisfactory monitoring results. Motivated by this, we expect to divide the data into static and dynamic features to perform separate modeling. Hence, the kernel slow feature analysis method is proposed to achieve this purpose. The output from the KSFA model can be divided into static and dynamic sources according to the dynamic characteristics. Then, in order to extract the unsupervised dynamic features, a novel deep RNN-LSTM autoencoder model is developed, which includes three components: data conversion, deep model construction, and feature removal. Moreover, we use a stacked autoencoder for static sources to learn static features and construct high-order models for fault detection. Based on the well-built detection system formed by the joint deep models, dual-scale decision-making is integrated by the Bayesian inference method. Finally, the superiority of the proposal can be shown in two processes.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Nonlinear process monitoring Static dynamic features sources Kernel slow feature analysis stacked autoencoder RNN-LSTM autoencoder

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Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch

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MEASUREMENT 2019年 140卷 215-226页

作者： Chen, Lin Li, Rong Zhang, Hang Tian, Lili Chen, Ning Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China

Accurate and reliable automatic fall detection based on wearable devices enables elderly people to receive instant treatment and can alleviate the severe consequences of falls. Falls are abnormal activities that occur rarely compared with normal daily activities;therefore, fall detection can be considered a one-class classification problem. However, it is difficult and dangerous to collect sufficient fall data in practice, making it difficult to use supervised learning methods to detect falls automatically. Among wearable devices, wrist-worn devices for fall detection are more likely to be accepted because of comfort;however, high accuracy cannot typically be realized due to sensitivity to interference from the diverse activities of the hand and wrist. Combining ensemble stacked autoencoders (ESAEs) with one-class classification based on the convex hull (OCCCH), this paper proposes a novel intelligent fall detection method, namely, ESAEs-OCCCH, which is based on accelerometer data from a wrist-worn smart watch. In the proposed method, ESAEs are first adopted for unsupervised feature extraction to overcome the disadvantages of artificial feature extraction, namely, the requirements in terms of experience and time. Then, OCCCH is used for pattern recognition. Finally, the majority voting strategy and weight adaptive adjustment strategy are combined to improve the performance and stability of fall detection. According to the behavioral characteristics of the elderly, the uncertainty of activities of daily living (ADLs) and fall activities (FAs), and the influence of the intense activities of the hand on the accelerometer signal, thirteen FAs and sixteen ADLs, including intense hand and wrist activities, are simulated by young volunteers of various genders, ages, heights and weights in two experiments. The experimental results demonstrate the performance and stability of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Fall detection Wearable device stacked autoencoder One-class classification based on the convex hull Ensemble strategy

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Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering

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NEURAL PROCESSING LETTERS 2021年第1期53卷 1-16页

作者： Zhu, Xiaofei Do, Khoi Duy Guo, Jiafeng Xu, Jun Dietze, Stefan Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China Leibniz Univ Hannover Res Ctr L3S D-30167 Hannover Germany Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China Leibniz Inst Social Sci Knowledge Technol Social Sci D-50667 Cologne Germany

Clustering is an essential data analysis technique and has been studied extensively over the last decades. Previous studies have shown that data representation and data structure information are two critical factors for improving clustering performance, and it forms two important lines of research. The first line of research attempts to learn representative features, especially utilizing the deep neural networks, for handling clustering problems. The second concerns exploiting the geometric structure information within data for clustering. Although both of them have achieved promising performance in lots of clustering tasks, few efforts have been dedicated to combine them in a unified deep clustering framework, which is the research gap we aim to bridge in this work. In this paper, we propose a novel approach, Manifold regularized Deep Embedded Clustering (MDEC), to deal with the aforementioned challenge. It simultaneously models data generating distribution, cluster assignment consistency, as well as geometric structure of data in a unified framework. The proposed method can be optimized by performing mini-batch stochastic gradient descent and back-propagation. We evaluate MDEC on three real-world datasets (USPS, REUTERS-10K, and MNIST), where experimental results demonstrate that our model outperforms baseline models and obtains the state-of-the-art performance.

关键词： Deep neural networks stacked autoencoder Manifold constraint Clustering

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Deep relevant representation learning for soft sensing

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INFORMATION SCIENCES 2020年 514卷 263-274页

作者： Yan, Xuefeng Wang, Jie Jiang, Qingchao East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China

Soft sensing provides a reliable estimation of difficult-to-measure variables and is important for process control, optimization, and monitoring. The extraction of beneficial information from the abundance of available data in modern industrial processes and the development of data-driven soft sensors are becoming areas of increasing interest. In addition, the use of deep neural networks (DNNs) has become a popular data processing and feature extraction technique owing to its superiority in generating high-level abstract representations from massive amounts of data. A deep relevant representation learning (DRRL) approach based on a stacked autoencoder is proposed for the development of an efficient soft sensor. Representations from conventional DNN methods are not extracted for an output prediction, and thus a mutual information analysis is conducted between the representations and the output variable in each layer. Analysis results indicate that irrelevant representations are eliminated during the training of the subsequent layer. Hence, relevant information is highlighted in a layer-by-layer manner. Deep relevant representations are then extracted, and a soft sensor model is established. The results of a numerical example and an industrial oil refining process show that the prediction performance of the proposed DRRL-based soft sensing approach is better than that of other state-of-the-art methods. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Deep neural network stacked autoencoder Deep relevant representation Soft sensing Mutual information

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