检索结果-内蒙古大学图书馆

Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2022年第4期127卷 e2021JB023830-e2021JB023830页

作者： Ding, Wen Li, Tianjue Yang, Xu Ren, Kui Tong, Ping Columbia Univ Dept Appl Phys & Appl Math New York NY USA Nanyang Technol Univ Div Math Sci Sch Phys & Math Sci Singapore Singapore Nanyang Technol Univ Earth Observ Singapore Singapore Singapore Univ Calif Santa Barbara Dept Math Santa Barbara CA 93106 USA Nanyang Technol Univ Asian Sch Environm Singapore Singapore

Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P- and S-wave arrivals, auto-identification of later seismic phases such as the Moho-reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine-identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high-quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep-neural-network-based algorithm to automatically identify PmP waves efficiently;by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)-autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre-trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.

关键词： PmP database deep neural networks unbalanced data set ResNet autoencoder southern California

来源：评论

学校读者我要写书评

暂无评论

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

引用

ALGORITHMS 2023年第1期16卷 52-52页

作者： Narkhede, Gaurav Hiwale, Anil Tidke, Bharat Khadse, Chetan MIT World Peace Univ Sch Elect & Commun Engn Pune 411038 India MIT World Peace Univ Sch Comp Engn & Technol Pune 411038 India MIT World Peace Univ Sch Elect Engn Pune 411038 India

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world's urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

关键词： MIA-LSTM data preprocessing iterative imputation autoencoder LSTM

来源：评论

学校读者我要写书评

暂无评论

Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

引用

FOOD CONTROL 2022年 131卷 108442-108442页

作者： Vasafi, Pegah Sadeghi Hinrichs, Joerg Hitzmann, Bernd Univ Hohenheim Inst Food Sci & Biotechnol Garbenstr 23 D-70599 Stuttgart Germany

Controlling milk processing steps is a crucial task as it affects the quality and safety of the final product. Using Raman spectrometer in combination with various evaluation techniques such as principal component analysis and regression, Gaussian process regression, and the autoencoder were checked to define an accurate method for detection of deviations from standard procedures. For this purpose, milk with 5% fat measured at 10 degrees C was considered as the reference milk. A temperature-controlled flow cell was used in a by-pass for online measurements. While the principal component regression was not able to predict the deviations, results demonstrate the capability of Gaussian process regression and the autoencoder to detect 5% added water and cleaning solution, 0.1% difference in fat content and variation of 5 degrees C in measurement temperature. It can be concluded that both procedures display promising results, however, the autoencoder can be trained once and used immediately for online supervision. Therefore, changes can be detected promptly, enabling companies to react instantly.

关键词： autoencoder Milk processing Deviations detection Gaussian process regression Raman spectroscopy

来源：评论

学校读者我要写书评

暂无评论

More evidence for prediction model of radiosensitivity

引用

BIOSCIENCE REPORTS 2021年第4期41卷 BSR20210034页

作者： Du, Zixuan Zhang, Xinyan Tang, Zaixiang Soochow Univ Sch Publ Hlth Dept Biostat Med Coll Suzhou 215123 Peoples R China Soochow Univ Jiangsu Key Lab Prevent & Translat Med Geriatr Di Med Coll Suzhou 215123 Peoples R China Kennesaw State Univ Sch Data Sci & Analyt Kennesaw GA 30144 USA

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high-and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.

关键词： Breast cancer LASSO Cox regression autoencoder radiotherapy sensitivity

来源：评论

学校读者我要写书评

暂无评论

Time Series Classification for Portable Medical Devices

引用

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第4期10卷

作者： Zhong, Zhaoyi Sun, Le Subramani, Sudha Peng, Dandan Wang, Yilin Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Dept Jiangsu Collaborat Innovat Ctr Atmospher Env Nanjing Peoples R China Victoria Univ Footscray Vic Australia Guangzhou Univ Sch Comp Sci & Network Engn Guangzhou Guangdong Peoples R China

INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module;(2) Training module;(3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% ACC and 99.32% AUC respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.

关键词： time series classification autoencoder attention mechanism Medical mobile information systems

来源：评论

学校读者我要写书评

暂无评论

Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma

引用

OPHTHALMOLOGY 2024年第2期4卷 100423-100423页

作者： Mohammadzadeh, Vahid Li, Leyan Fei, Zhe Davis, Tyler Morales, Esteban Wu, Kara Ma, Elise Lee Fifi, Abdelmonem A. Nouri-Mahdavi, Kouros Caprioli, Joseph David Geffen Sch Med Jules Stein Eye Inst Glaucoma Div Los Angeles CA USA Univ Calif Los Angeles Jonathan & Karin Fielding Sch Publ Hlth Los Angeles CA USA Univ Calif Los Angeles Biostat Los Angeles CA USA Univ Calif Riverside Dept Stat Riverside CA USA Univ Calif Los Angeles Comp Sci Los Angeles CA USA 100 Stein Plaza Los Angeles CA 90095 USA

Purpose: To evaluate and compare the effectiveness of nearest neighbor (NN)-and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression ***: Longitudinal cohort ***: 7150 eyes (4232 patients), with > 5 years of follow-up and > 6 ***: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed *** Outcome Measures: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing ***: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. Conclusions: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting.

关键词： Artificial intelligence Glaucoma progression Variability Variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning

引用

DIGITAL HEALTH 2024年 10卷

作者： Choi, Hyoungshin Cho, Yesol Min, Choongki Kim, Kyungnam Kim, Eunji Lee, Seungmin Kim, Jae-Jin Waycen Inc AI Medtech R&D Seoul South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea Yonsei Univ Inst Behav Sci Med Coll Med Seoul South Korea Yonsei Univ Gangnam Severance Hosp Coll Med Dept Psychiat 211 Eonju-ro Seoul 06273 South Korea

Objective Social anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom *** We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive *** Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of *** Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.

关键词： Digital phenotype social anxiety disorder autoencoder representation learning integrated gradient multiclassification

来源：评论

学校读者我要写书评

暂无评论

Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

引用

SENSORS 2019年第24期19卷 5501页

作者： Chun, Chanjun Ryu, Seung-Ki Korea Inst Civil Engn & Bldg Technol KICT Future Infrastruct Res Ctr Goyang 10223 South Korea

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

关键词： road surface damage semantic segmentation autoencoder convolutional neural network semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

The application of unsupervised deep learning in predictive models using electronic health records

引用

BMC MEDICAL RESEARCH METHODOLOGY 2020年第1期20卷 37-37页

作者： Wang, Lei Tong, Liping Davis, Darcy Arnold, Tim Esposito, Tina Renmin Univ China Sch Stat 59 Zhong Guan Cun Ave Beijing Peoples R China Univ Illinois Dept Math Stat & Comp Sci 851 S Morgan St Chicago IL 60607 USA Advocate Aurora Hlth 3075 Highland Pkwy Downers Grove IL 60515 USA Cerner Corp 2800 Rockcreek Pkwy North Kansas City MO 64117 USA

Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks. Methods We compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals. Results On simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper. Conclusions We conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and

关键词： autoencoder LASSO Enhanced Reg Predictive model Predictive performance Important response-specific predictors

来源：评论

学校读者我要写书评

暂无评论

Dimensionality reduction for protein secondary structure and solvent accesibility prediction

引用

JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2018年第5期16卷 1850020页

作者： Aydin, Zafer Kaynar, Oguz Gormez, Yasin Abdullah Gul Univ Dept Comp Engn TR-38080 Kayseri Turkey Cumhuriyet Univ Dept Management Informat Syst TR-58000 Sivas Turkey

Secondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction. The reduced feature set is used to train a support vector machine at the second stage of a hybrid classifier. Cross-validation experiments on two difficult benchmarks demonstrate that the dimension of the input space can be reduced substantially while maintaining the prediction accuracy. This will enable the incorporation of additional informative features derived for predicting the structural properties of proteins without reducing the accuracy due to overfitting.

关键词： Secondary structure prediction solvent accessibility prediction feature selection dimension reduction autoencoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：