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14th IEEE International Conference on Signal Processing (ICSP)

作者： Wang Wei Wang Yisong Wang Lihui Sun Caixia Cheng Xinyu Li Zhi Zhang Jian Yang Feng Zhu Yuemin Guizhou Univ Key Lab Intelligent Med Image Anal & Precise Diag Sch Comp Sci & Technol Guiyang Guizhou Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Univ Lyon INSA Lyon INSERM CNRSCREATIS UMR 5220U1206 F-69621 Lyon France

ISBN: (纸本)9781538646731

Predicting accurately the lymph node metastasis is very essential for right staging and making the appropriate treatment strategies for lung cancer. Existing prediction methods are mostly based on the image features, which depend greatly on the postprocessing algorithms. To deal with this problem, we proposed to use the stacked sparse autoencoder (SSAE) network to predict the lymph node metastasis in lung cancer. To assess the performance of SSAE, the radiomics features based prediction and image based SSAE prediction were quantitatively compared in terms of ROC and AUC. In the radiomics-based prediction, more than 400 features were fed into a SSAE network, and in the image-based SSAE prediction, 100 images were inputted. The experimental results show that the image-based SSAE experienced a better performance than that radiomics-based one, even with a fewer samples to train the network. The AUC obtained by image-based SSAE was enhanced by about 50%.

关键词： Lung cancer stacked sparse autoencoder lymph node metastasis

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A stacked sparse autoencoder based Architecture for Punjabi and English Spoken Language Classification using MFCC features 3

A Stacked Sparse Autoencoder based Architecture for Punjabi ...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Arora, Vaibhav Sood, Pulkit Keshari, Kumar Utkarsh Paxcel Technol Pvt Ltd Phase 8 Mohali India

ISBN: (纸本)9789380544199

Spoken language classification is an important task in speech processing. It can serve as a preprocessing step in the pipeline of automated understanding of the semantics of human speech. The paper proposes a sparse autoencoder based architecture for Punjabi and English spoken language Classification. A number of shallow architectures namely Soft-max classifier, SVM and deep architectures namely Artificial Neural Networks, SVM with sparse Auto encoder and Softmax with sparse auto encoder (with and without fine tuning) have been compared on the same task. For this purpose noisy speech samples of 1 second using frame based MFCC coefficients of speech samples have been considered. Comparison of Principal Component Analysis and sparse autoencoder based features has also been done on the dataset.

关键词： ANN Deep learning English MFCC Spoken Language Classification PCA Punjabi stacked sparse autoencoder SVM

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stacked sparse autoencoder based Automatic Detection of Ripples and Fast Ripples in Epilepsy

Stacked Sparse Autoencoder based Automatic Detection of Ripp...

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第三十九届中国控制会议

作者： Hongzhen Qin Min Wu Xiongbo Wan Yuxiao Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Guangdong University of Technology

High frequency oscillations(HFOs) have been acknowledged as a putative biomarker of epileptic seizure onset zones(SOZs). Accurate detection of HFOs is significant for the preoperative localization of epileptic SOZs. In this paper, a new method is proposed to automatically detect ripples(Rs) and fast ripples(FRs) from intracranial electroencephalography(iEEG)in epilepsy. A moving-window technique is utilized to segment the filtered signals which are obtained by filtering the raw iEEG signals using two Chebyshev band-pass filters. Two stacked sparse autoencoder(SSAE) models are proposed to automatically detect Rs and FRs, respectively. By optimizing the parameters of the two SSAE models, our method yields higher sensitivity(88.9±2.4% for Rs and 83.2±2.5% for FRs) and higher specificity(92.3±2.8% for Rs and 86.1±2.8% for FRs) than other three methods do.

关键词： Epilepsy Intracranial electroencephalography Ripples Fast ripples stacked sparse autoencoder

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A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability

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MEASUREMENT 2022年 194卷 1页

作者： Priyatharishini, M. Devi, M. Nirmala Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India

The rate of development of technology and its associated security issues are increasing in integrated circuit industry. This provides a space for implanting dormant nature of threats, named Hardware Trojan (HT) in various process of integrated circuits (IC) design. The impact of HT's emanates the encrypted signals, privacy disruption, performance degradation or denial of service. The threat models are unimaginable and it can be intruded at any stage which complicates the HT detection process. Therefore, a deep learning-based malicious module identification method is proposed in this work by implementing stacked autoencoder and stacked sparse autoencoder model. The simulation results shows that the proposed stacked sparse autoencoder outperforms the best in detecting the malicious modifications with an average accuracy of 97.53%, true positive rate of 93% and moreover the true negative rate achieved is 98.14% which proves the effectiveness of sparsity nature in extracting suitable features in the proposed schemes.

关键词： Hardware Trojan Feature Generative Adversarial Network stacked sparse autoencoder

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Identification of maize seed varieties based on stacked sparse autoencoder and near-infrared hyperspectral imaging technology

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JOURNAL OF FOOD PROCESS ENGINEERING 2022年第9期45卷 e14120-e14120页

作者： Fu, Lvhui Sun, Jun Wang, Simin Xu, Min Yao, Kunshan Cao, Yan Tang, Ningqiu Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

Maize seed variety identification is the key to improving the quality and yield of maize. The study aimed to investigate a stacked sparse autoencoder combined with a cuckoo search (CS) optimized support vector machine (SSAE-CS-SVM) to meet the identification requirements of accurate detection. First, the near-infrared (NIR) (871.61-1766.32 nm) hyperspectral data of maize seeds were processed using Savitzky-Golay (SG) combined with standard normal variables (SNV). Subsequently, SSAE, SAE, principal component analysis (PCA), and competitive adaptive reweighted sampling were employed for feature extraction. Finally, the recognition model was constructed using the SoftMax regression model, SVM, and CS optimized SVM (CS-SVM). The results indicated that the SSAE-CS-SVM model achieved satisfactory performance (the training set and testing set accuracies were 99.45% and 95.81%, respectively). This study confirmed the great potential of combining NIR hyperspectral imaging technology with deep learning algorithms for maize seed variety identification. Practical applications The identification of maize seed varieties is extremely important for ensuring seed purity and improving maize quality and yield. A method based on SSAE and NIR hyperspectral imaging technology was studied to identify maize seed varieties. The method proposed in this paper could identify maize seed varieties non-destructively and accurately, which provided a new way for the online detection of seed varieties.

关键词： cuckoo search-support vector machine identification mode maize seed varieties near-infrared hyperspectral image stacked sparse autoencoder

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Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction

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APPLIED ENERGY 2019年 254卷 113686-000页

作者： Liu, Hui Chen, Chao Cent South Univ Sch Traff & Transportat Engn IAIR Key Lab Traff Safety TrackMinist Educ Changsha 410075 Hunan Peoples R China

Accurate wind speed prediction is essential for proper use of wind energy resource. In this paper, a novel hybrid multi-step wind speed forecasting model is developed, which consists of sparse feature extraction, bidirectional deep learning, multi-objective optimization, and adaptive decomposition-based error correction. Apart from the traditional average-based resolution transformation method, a two-layer stacked sparse autoencoder (SSAE) is proposed to extract the hidden representation of original 3 s high-resolution wind speed data. Trained by the data generated from different resolution transformation methods, two bidirectional long short-term memory (BiLSTM) networks serve as base predictors and provide 10-step forecasting results. The results of base predictors are reasonably ensembled by multi-objective multi-universe optimization (MOMVO). Moreover, to reduce the predictable components in error series further, a correction model based on empirical wavelet transform (EWT) and outlier robust extreme learning machine model (ORELM) is constructed to reduce the forecasting error further. The effectiveness of the proposed hybrid model is comprehensively evaluated by a series of experiments. The experimental results demonstrate that: (a) the proposed model is well trained, with great convergence, and an average RMSE of 0.2618 m/s in 10-step forecasting;(b) the proposed model outperforms other existing models in all experimental sites and forecasting steps;(c) the multi-objective optimization algorithm can rationally integrate base predictors to obtain better performance in each step;(d) the proposed residual error correction model can generate more than 78% improvement of RMSE, significantly better than compared correction methods.

关键词： Wind speed forecasting stacked sparse autoencoder Bidirectional long short-term memory Multi-objective multi-universe optimization Residual error correction

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Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder

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COMPUTER NETWORKS 2022年第0期207卷 108742-108742页

作者： Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti Univ Nantes LS2N 2 Chemin Houssiniere Nantes France IIT Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era where the implementation of this concept can be done easily with network function virtualization. Currently, many approaches have been proposed to improve the performance of traffic classification. However, as new types of traffic emerge every day (and they are generally not labeled), this opens a new challenge to be handled. Moreover, the question of how to accurately classify the traffic using a limited amount of labeled data or partially labeled data is also another important concern. In fact, labeling data is often difficult and time-consuming. In order to solve the previously described issues, we reformulate traffic classification into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined. To do so, this paper presents a stacked sparse autoencoder (SSAE) based semi-supervised deep-learning model for traffic classification. The main motivations of this approach are: (i) unlabeled data is often abundant and easily available;(ii) classification performance of the whole model can be greatly improved when a large amount of unlabeled traffic is included in the training process;(iii) there is a limit to how much human effort can be thrown at the labeling problem. To investigate the performance of our approach, an empirical study has been conducted on a real dataset and results indicate that using a large amount of unlabeled data in the SSAE pre-trained phase can improve significantly the classification performance of the whole model. Furthermore, the proposed approach is compared against other representative machine-learning and deep-learning models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), and autoencoder.

关键词： Machine learning Deep learning stacked sparse autoencoder Feature extraction Traffic classification Semi-supervised learning Partial information

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Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2018年第12期29卷 125002-125002页

作者： Saufi, Syahril Ramadhan bin Ahmad, Zair Asrar Leong, Mohd Salman Lim, Meng Hee Univ Teknol Malaysia Fac Engn Sch Mech Engn Skudai 81310 Malaysia Univ Teknol Malaysia Inst Noise & Vibrat Kuala Lumpur 54100 Malaysia

The rolling clement bearing is an important component in most rotating machinery. The unexpected failure of a bearing may cause the whole mechanism to break down. Hence, research has focused on developing effective intelligent fault diagnosis to generate more accurate and robust diagnostic results. Bearing fault diagnosis based on stacked sparse autoencoder (SSAE) architecture is proposed in this study. SSAE is capable of providing a featureless methodology for bearing fault diagnosis. However, the architecture of SSAE is greatly influenced by its hyperparameter settings and there is no standard method of determining the optimal hyperparameter values. In addition, the standard learning algorithm used in SSAE architecture is time-intensive. In this paper, a method that combines differential evolution and a resilient back-propagation approach is proposed to improve the performance of SSAE networks in bearing fault classification. The differential evolution approach optimised SSAEs hyperparameters such as the hidden nodes number, weight decay parameter, sparsity parameter, and weight of the sparsity penalty term, that are associated with each hidden layer of SSAE networks. An increase in the hidden layers of SSAE will further complicate the hyperparameter selection process. The resilient back-propagation training algorithm is used to train the SSAE network due to its low computation cost. Results from analysis of three databases demonstrate that the proposed model achieved 99% performance accuracy in bearing fault diagnosis. The proposed model is found to be more user-friendly and effective in handling multi-condition of bearing faults compared to the original autoencoder.

关键词： bearing fault diagnosis stacked sparse autoencoder resilient back-propagation differential evolution scalogram

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Building Extraction Based on an Optimized stacked sparse autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images

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SENSORS 2017年第9期17卷 1957-1957页

作者： Yan, Yiming Tan, Zhichao Su, Nan Zhao, Chunhui Harbin Engn Univ Dept Informat Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors, as light detection and ranging (LIDAR), optical sensor etc., are used to improve the extraction. Using digital surface model (DSM) obtained from LIDAR data and optical images, traditional method can improve the extraction effect to a certain extent, but there are some defects in feature extraction. Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. A better setting strategy of SSAE network structure is given, and an idea of setting the number and proportion of training samples for better training of SSAE was presented. The optical data and DSM were combined as input of the optimized SSAE, and after training by an optimized samples, the appropriate network structure can extract buildings with great accuracy and has good robustness.

关键词： stacked sparse autoencoder LIDAR DSM remote sensing image building extraction

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Cost Prediction of Tunnel Construction Based on Interpretative Structural Model and stacked sparse autoencoder

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ENGINEERING LETTERS 2024年第10期32卷 1966-1980页

作者： Zhou, Jing-Qun Liu, Qi-Ming Ma, Chang-Xi Li, Dong Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Gansu Rd & Bridge Construct Grp Lanzhou 730070 Peoples R China

Cost management plays a vital role in ensuring the successful execution of different engineering projects, with precise costing serving as the cornerstone of effective cost management strategies. Recently, the machine learning technique offers an accurate and efficient method for forecasting construction expenses, introducing a novel approach to cost accounting other than conventional calculation techniques. This paper provides an overview of the current research landscape in the realm of cost prediction utilizing machine learning, also addresses some new research focuses and limitations. By utilizing highway tunnel engineering as a case study, this study employs an Interpretative Structural Model (ISM) to analyze the primary factors influencing construction costs. Subsequently, a construction cost prediction model is developed, leveraging a stacked sparse autoencoder (SSAE) network within a deep learning framework. Last, the proposed model is trained using real construction projects as samples. Results show that there are some good prediction outputs with a remarkably low mean absolute percentage error of 0.71%. Thereby, they verified the identifying precision of key influencing factors and the reliability of the cost prediction model.

关键词： construction cost highway tunnel engineering Interpretative Structural Model prediction stacked sparse autoencoder

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