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Pseudo Zernike Moment and Deep stacked sparse autoencoder for COVID-19 Diagnosis

Computers, Materials & Continua 2021年第12期69卷 3145-3162页

作者： Yu-Dong Zhang Muhammad Attique Khan Ziquan Zhu Shui-Hua Wang School of Informatics University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Science in Civil Engineering University of FloridaGainesvilleFloridaFL 32608GainesvilleUSA School of Mathematics and Actuarial Science University of LeicesterLE17RHUK

(Aim)COVID-19 is an ongoing infectious *** has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/*** computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better *** use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted *** settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit ***,we use a deep-stacked sparse autoencoder(DSSAE)as the ***,multiple-way data augmentation is chosen to overcome *** multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,*** AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

关键词： Pseudo Zernike moment stacked sparse autoencoder deep learning COVID-19 multiple-way data augmentation medical image analysis

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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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stacked sparse autoencoder based Automatic Detection of Ripples and Fast Ripples in Epilepsy 39

Stacked Sparse Autoencoder based Automatic Detection of Ripp...

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39th Chinese Control Conference (CCC)

作者： Qin, Hongzhen Wu, Min Wan, Xiongbo Du, Yuxiao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9789881563903

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 ERs) 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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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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Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network

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APPLIED ENERGY 2020年第0期259卷 114159-000页

作者： Han, Zhezhe Hossain, Md Moinul Wang, Yuwei Li, Jian Xu, Chuanlong Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Univ Kent Sch Engn & Digital Arts Canterbury CT2 7NT Kent England China Energy Jianbi Power Plant Zhenjiang 212006 Jiangsu Peoples R China

Combustion instability is a well-known problem in the combustion processes and closely linked to lower combustion efficiency and higher pollutant emissions. Therefore, it is important to monitor combustion stability for optimizing efficiency and maintaining furnace safety. However, it is difficult to establish a robust monitoring model with high precision through traditional data-driven methods, where prior knowledge of labeled data is required. This study proposes a novel approach for combustion stability monitoring through stacked sparse autoencoder based deep neural network. The proposed stacked sparse autoencoder is firstly utilized to extract flame representative features from the unlabeled images, and an improved loss function is used to enhance the training efficiency. The extracted features are then used to identify the classification label and stability index through clustering and statistical analysis. Classification and regression models incorporating the stacked sparse autoencoder are established for the qualitative and quantitative characterization of combustion stability. Experiments were carried out on a gas combustor to establish and evaluate the proposed models. It has been found that the classification model provides an F-1-score of 0.99, whilst the R-squared of 0.98 is achieved through the regression model. Results obtained from the experiments demonstrated that the stacked sparse autoencoder model is capable of extracting flame representative features automatically without having manual interference. The results also show that the proposed model provides a higher prediction accuracy in comparison to the traditional data-driven methods and also demonstrates as a promising tool for monitoring the combustion stability accurately.

关键词： Combustion stability Flame imaging stacked sparse autoencoder Innovative loss function Gaussian mixture model

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stacked sparse autoencoder and case-based postprocessing method for nucleus detection

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NEUROCOMPUTING 2019年 359卷 494-508页

作者： Li, Siqi Jiang, Huiyan Bai, Jie Liu, Ye Yao, Yu-dong Northeastern Univ Dept Software Coll Shenyang 110819 Liaoning Peoples R China Northeastern Univ Dept Sino Dutch Biomed & Informat Engn Sch Shenyang 110819 Liaoning Peoples R China Sun Yat Sen Univ Affiliated Hosp 5 Dept Pathol 52 Meihua Dong Rd Zhuhai 519000 Guangdong Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Accurate nucleus detection is of great importance in pathological image analyses and diagnoses, which is a critical prerequisite for tasks such as automated grading hepatocellular carcinoma (HCC) nuclei. This paper proposes an automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) in a coarse-to-fine manner. SSAE, an unsupervised learning model, is first trained using image patches of breast cancer. Then, the transfer learning and sliding window techniques are applied to other cancers' pathological images (HCC and colon cancer) to extract the high-level features of image patches via the trained SSAE. Subsequently, these high-level features are fed to a logistic regression classifier (LRC) to classify whether each image patch contains a complete nucleus in a coarse detection process. Finally, CPM is developed for refining the coarse detection results which removes false positive nuclei and locates adhesive or overlapped nuclei effectively. SSAE-CPM achieves an average nucleus detection accuracy of 0.8748 on HCC pathological images, which can accurately locate almost all nuclei on the pathological images with serious differentiation. In addition, our proposed detection framework is also evaluated on a public dataset of colon cancer, with a mean F-1 score of 0.8355. Experimental results demonstrate the performance advantages of our proposed SSAE-CPM detection framework as compared with related work. While our detection framework is trained on the pathological images of breast cancer, it can be easily and effectively applied to nucleus detection tasks on other cancers without re-training. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Automated nucleus detection stacked sparse autoencoder Case-based postprocessing method Transfer learning Coarse-to-fine manner

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Remaining Useful Life Prediction Based on stacked sparse autoencoder and Echo State Network 39

Remaining Useful Life Prediction Based on Stacked Sparse Aut...

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39th Chinese Control Conference (CCC)

作者： Yang, Yinghua Yao, Dandan Liu, Xiaozhi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9789881563903

Nowadays, the prediction of industry components' remaining useful life (RUL) has already become a hot topic. In this paper, a RUL prediction method based on stacked sparse autoencoder (SAE) and echo state network (ESN) is proposed. autoencoder (AE) is an unsupervised learning method that can be used for feature extraction to obtain a health index (HI). To enhance the performance of the HI, a sparse constraint is added and a stacked structure is used to increase depth. The RUL of the industry components is then predicted using a method that maps directly to the RUL value based on the health factor curve. With the capability of encapsulating dynamic time behavior and saving historical information of input data, ESN is selected as the prediction network. The proposed method is verified using the C-MPASS dataset. The experimental results show that the method has better performance than the stacked AE based method.

关键词： Remaining useful life stacked sparse autoencoder echo state network

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A NONINVASIVE METHOD TO DETECT DIABETES MELLITUS AND LUNG CANCER USING THE stacked sparse autoencoder

A NONINVASIVE METHOD TO DETECT DIABETES MELLITUS AND LUNG CA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Qi Zhou, Jianhang Zhang, Bob Univ Macau Dept Comp & Informat Sci PAMI Res Grp Taipa Macau Peoples R China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently-needing professional medical instruments such as a CT and a qualified individual to perform the Fasting Plasma Glucose test. Considering the risks and various inconveniences with conventional diagnosis methods, noninvasive approaches based on computerized analysis are desired. The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Experimental results on a dataset containing 450 healthy samples, 284 diabetes and 175 lung cancer patients produced the F1-score of 93.57%, 97.54%, 81.56% for detecting healthy, diabetes and lung cancer, respectively, validating the effectiveness of our proposed method.

关键词： Diabetes mellitus lung cancer facial image medical biometrics stacked sparse autoencoder

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