601
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Yao H, Zhang X, Zhou X, Liu S. Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers (Basel) 2019; 11:E1901. [PMID: 31795390 DOI: 10.3390/cancers11121901] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 11/10/2019] [Accepted: 11/26/2019] [Indexed: 12/16/2022] Open
Abstract
In this paper, we present a new deep learning model to classify hematoxylin-eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin-eosin-stained breast biopsy images. The results demonstrate that our model significantly outperforms state-of-the-art methods.
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602
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Liu J, Gong X. Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction. BMC Bioinformatics 2019; 20:609. [PMID: 31775612 PMCID: PMC6882172 DOI: 10.1186/s12859-019-3199-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 11/06/2019] [Indexed: 11/25/2022] Open
Abstract
Background Recurrent neural network(RNN) is a good way to process sequential data, but the capability of RNN to compute long sequence data is inefficient. As a variant of RNN, long short term memory(LSTM) solved the problem in some extent. Here we improved LSTM for big data application in protein-protein interaction interface residue pairs prediction based on the following two reasons. On the one hand, there are some deficiencies in LSTM, such as shallow layers, gradient explosion or vanishing, etc. With a dramatic data increasing, the imbalance between algorithm innovation and big data processing has been more serious and urgent. On the other hand, protein-protein interaction interface residue pairs prediction is an important problem in biology, but the low prediction accuracy compels us to propose new computational methods. Results In order to surmount aforementioned problems of LSTM, we adopt the residual architecture and add attention mechanism to LSTM. In detail, we redefine the block, and add a connection from front to back in every two layers and attention mechanism to strengthen the capability of mining information. Then we use it to predict protein-protein interaction interface residue pairs, and acquire a quite good accuracy over 72%. What’s more, we compare our method with random experiments, PPiPP, standard LSTM, and some other machine learning methods. Our method shows better performance than the methods mentioned above. Conclusion We present an attention mechanism enhanced LSTM with residual architecture, and make deeper network without gradient vanishing or explosion to a certain extent. Then we apply it to a significant problem– protein-protein interaction interface residue pairs prediction and obtain a better accuracy than other methods. Our method provides a new approach for protein-protein interaction computation, which will be helpful for related biomedical researches.
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Affiliation(s)
- Jiale Liu
- Mathematics Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, No. 59 Zhongguancun Street,Haidian District, Beijing, China
| | - Xinqi Gong
- Mathematics Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, No. 59 Zhongguancun Street,Haidian District, Beijing, China. .,Center for Mathematical Sciences and Applications,Harvard University, Boston, MA02138, USA.
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603
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Pinotsis DA, Siegel M, Miller EK. Sensory processing and categorization in cortical and deep neural networks. Neuroimage 2019; 202:116118. [PMID: 31445126 DOI: 10.1016/j.neuroimage.2019.116118] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 07/23/2019] [Accepted: 08/20/2019] [Indexed: 01/13/2023] Open
Abstract
Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results.
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Affiliation(s)
- Dimitris A Pinotsis
- Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London, EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Markus Siegel
- Center for Integrative Neuroscience and MEG Center, University of Tubingen, 72076, Tübingen, Germany
| | - Earl K Miller
- The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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604
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Abstract
We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC50 values than known Mdmx inhibitors. The source code of the project is available on https://github.com/bigchem/online-chem.
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Affiliation(s)
- Zhonghua Xia
- Institute of Structural Biology, Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Pavel Karpov
- Institute of Structural Biology, Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- BigChem GmbH, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Grzegorz Popowicz
- Institute of Structural Biology, Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München - Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- BigChem GmbH, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
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605
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Choi A, Jung H, Lee KY, Lee S, Mun JH. Machine learning approach to predict center of pressure trajectories in a complete gait cycle: a feedforward neural network vs. LSTM network. Med Biol Eng Comput 2019; 57:2693-703. [PMID: 31650342 DOI: 10.1007/s11517-019-02056-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 10/03/2019] [Indexed: 10/25/2022]
Abstract
Center of pressure (COP) trajectories of human can maintain regulation of forward progression and stability of lateral sway during walking. The insole pressure system can only detect COP trajectories of each foot during single stance. In this study, we developed artificial neural network models that could present COP trajectories in an integrated coordinate system during a complete gait cycle using pressure information of the insole system. A feed forward artificial neural network (FFANN) and a long short-term memory (LSTM) model were developed. For FFANN, among 198 pressure sensors from Pedar-X insoles, proper input variables were selected using sequential forward selection to reduce input dimension. The LSTM model used all 198 signals as inputs because of its self-learning characteristic. As results of cross-validation, the FFANN model showed correlation coefficients of 0.98-0.99 and 0.93-0.95 in anterior/posterior and medial/lateral directions, respectively. For the LSTM model, correlation coefficients were similar to those of FFANN. However, the relative root mean square error (12.5%) of the FFANN model was higher than that (9.8%) of the LSTM model in medial/lateral direction (p = 0.03). This study can be used for quantitative evaluation of clinical diagnosis and rehabilitation status for patient with various diseases through further training using varied databases. Graphical abstract Architectures of neural networks developed in this study (a feed forward artificial neural network; b LSTM network).
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606
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Abstract
Molecular biology combined with in silico machine learning and deep learning has facilitated the broad application of gene expression profiles for gene function prediction, optimal crop breeding, disease-related gene discovery, and drug screening. Although the acquisition cost of genome-wide expression profiles has been steadily declining, the requirement generates a compendium of expression profiles using thousands of samples remains high. The Library of Integrated Network-Based Cellular Signatures (LINCS) program used approximately 1000 landmark genes to predict the expression of the remaining target genes by linear regression; however, this approach ignored the nonlinear features influencing gene expression relationships, limiting the accuracy of the experimental results. We herein propose a gene expression prediction model, L-GEPM, based on long short-term memory (LSTM) neural networks, which captures the nonlinear features affecting gene expression and uses learned features to predict the target genes. By comparing and analyzing experimental errors and fitting the effects of different prediction models, the LSTM neural network-based model, L-GEPM, can achieve low error and a superior fitting effect.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, P. R. China
| | - Chun Li
- College of Information and Computer, Taiyuan University of Technology, P. R. China
| | - Jianhui Zhang
- College of Information and Computer, Taiyuan University of Technology, P. R. China
| | - Jingjing Wang
- College of Information and Computer, Taiyuan University of Technology, P. R. China
| | - Yue Ma
- College of Information and Computer, Taiyuan University of Technology, P. R. China
| | - Yuanyuan Lian
- College of Information and Computer, Taiyuan University of Technology, P. R. China
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607
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Abstract
Changes in intraday trading volume are integral to any algorithmic trading strategy. Accordingly, forecasting the change in trading volume is paramount to better understanding the financial markets. This paper introduces a new method to forecast the log change in trading volume, leveraging the power of Long Short Term Memory (LSTM) networks in conjunction with Support Vector Regression (SVR) and Autoregressive (AR) models. We show that LSTM contributes to a more accurate forecast, particularly when constructed as part of a hybrid model with AR. The algorithm is extended to include data about the time of day, helping the model associate the log change in trading volume with the current hour, which yields the best performance of all trials.
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Affiliation(s)
- Daniel Libman
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Simi Haber
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Mary Schaps
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
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608
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Tan HX, Aung NN, Tian J, Chua MCH, Yang YO. Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection. Gait Posture 2019; 74:128-134. [PMID: 31518859 DOI: 10.1016/j.gaitpost.2019.09.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/05/2019] [Accepted: 09/04/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND Gait event detection (GED) is an important aspect in identifying and interpret a user's gait to assess gait abnormalities and design intelligent assistive devices. RESEARCH QUESTION There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments. METHODS This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user's gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made. RESULTS Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment. SIGNIFICANCE This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.
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Affiliation(s)
- Hui Xing Tan
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore
| | - Nway Nway Aung
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore
| | - Jing Tian
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore
| | - Matthew Chin Heng Chua
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore.
| | - Youheng Ou Yang
- Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, 169608, Singapore
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609
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Hssayeni MD, Jimenez-Shahed J, Burack MA, Ghoraani B. Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. Sensors (Basel) 2019; 19:s19194215. [PMID: 31569335 PMCID: PMC6806340 DOI: 10.3390/s19194215] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 09/24/2019] [Indexed: 12/14/2022]
Abstract
Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.
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Affiliation(s)
- Murtadha D Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.
| | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.
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610
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Zhang H, Hung CL, Liu M, Hu X, Lin YY. Corrigendum: NCNet: Deep Learning Network Models for Predicting Function of Non-Coding DNA. Front Genet 2019; 10:923. [PMID: 31543905 PMCID: PMC6753644 DOI: 10.3389/fgene.2019.00923] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 09/04/2019] [Indexed: 11/13/2022] Open
Abstract
[This corrects the article DOI: 10.3389/fgene.2019.00432.].
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Affiliation(s)
- Hanyu Zhang
- College of Computing and Informatics, Providence University, Taichung City, Taiwan.,Labo MICS, École CentraleSup élec, Université Paris Saclay, Gif-sur-Yvette, France
| | - Che-Lun Hung
- Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan City, Taiwan.,Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan City, Taiwan.,AI Innovation Research Center, Chang Gung University, Taoyuan City, Taiwan.,Department of Computer Science and Communication Engineering, Providence University, Taichung City, Taiwan
| | - Meiyuan Liu
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Xiaoye Hu
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Yi-Yang Lin
- Department of Computer Science and Communication Engineering, Providence University, Taichung City, Taiwan
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611
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Abstract
As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.
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Affiliation(s)
- Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Shangbin Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. .,Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, 100124, China. .,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China.
| | - Sinuo Deng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
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612
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Richter-Pechanski P, Amr A, Katus HA, Dieterich C. Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports. Stud Health Technol Inform 2019; 267:101-109. [PMID: 31483261 DOI: 10.3233/shti190813] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identification methods on German medical texts. Because of remarkable advancements in natural language processing using supervised machine learning methods on limited training data, we evaluated them for the first time on German medical reports using our annotated data set consisting of 113 medical reports from the cardiology domain. We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports. We performed an extensive evaluation for de-identification and multiclass named entity recognition. Using rule based and out of domain machine learning methods as a baseline, the conditional random field improved F2-score from 70 to 93% for de-identification, the neural approach reached 96% in F2-score while keeping balanced precision and recall rates. These results show, that state-of-the-art machine learning methods can play a crucial role in de-identification of German medical reports.
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Affiliation(s)
- Phillip Richter-Pechanski
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg.,Department of Internal Medicine III, University Hospital Heidelberg.,German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim
| | - Ali Amr
- Department of Internal Medicine III, University Hospital Heidelberg.,German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim
| | - Hugo A Katus
- Department of Internal Medicine III, University Hospital Heidelberg.,German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim
| | - Christoph Dieterich
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Heidelberg.,Department of Internal Medicine III, University Hospital Heidelberg.,German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim
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613
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Zhao X, Li P, Xiao K, Meng X, Han L, Yu C. Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models. Sensors (Basel) 2019; 19:E3844. [PMID: 31492034 DOI: 10.3390/s19183844] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/26/2019] [Accepted: 09/02/2019] [Indexed: 12/01/2022]
Abstract
Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.
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614
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Liu S, Zheng D, Li R. Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network. Sensors (Basel) 2019; 19:s19173740. [PMID: 31470577 PMCID: PMC6749196 DOI: 10.3390/s19173740] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 08/20/2019] [Accepted: 08/23/2019] [Indexed: 11/16/2022]
Abstract
The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.
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Affiliation(s)
- Shucong Liu
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Institute of Disaster Prevention, Sanhe 065201, China
| | - Dezhi Zheng
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
| | - Rui Li
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
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615
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Wouda FJ, Giuberti M, Rudigkeit N, van Beijnum BF, Poel M, Veltink PH. Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning. Sensors (Basel) 2019; 19:E3716. [PMID: 31461958 DOI: 10.3390/s19173716] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 07/30/2019] [Accepted: 08/21/2019] [Indexed: 11/16/2022]
Abstract
Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and/or sufficient computational resources. Therefore, we investigate the following research question: "What is the performance of a shallow approach, compared to a deep learning one, for estimating time coherent full-body poses using only five inertial sensors?". We propose to incorporate past/future inertial sensor information into a stacked input vector, which is fed to a shallow neural network for estimating full-body poses. Shallow and deep learning approaches are compared using the same input vector configurations. Additionally, the inclusion of acceleration input is evaluated. The results show that a shallow learning approach can estimate full-body poses with a similar accuracy (~6 cm) to that of a deep learning approach (~7 cm). However, the jerk errors are smaller using the deep learning approach, which can be the effect of explicit recurrent modelling. Furthermore, it is shown that the delay using a shallow learning approach (72 ms) is smaller than that of a deep learning approach (117 ms).
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616
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Liu X, Liu Y, Zhang M, Chen X, Li J. Improving Stockline Detection of Radar Sensor Array Systems in Blast Furnaces Using a Novel Encoder-Decoder Architecture. Sensors (Basel) 2019; 19:s19163470. [PMID: 31398946 PMCID: PMC6719009 DOI: 10.3390/s19163470] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 11/24/2022]
Abstract
The stockline, which describes the measured depth of the blast furnace (BF) burden surface with time, is significant to the operator executing an optimized charging operation. For the harsh BF environment, noise interferences and aberrant measurements are the main challenges of stockline detection. In this paper, a novel encoder–decoder architecture that consists of a convolution neural network (CNN) and a long short-term memory (LSTM) network is proposed, which suppresses the noise interferences, classifies the distorted signals, and regresses the stockline in a learning way. By leveraging the LSTM, we are able to model the longer historical measurements for robust stockline tracking. Compared to traditional hand-crafted denoising processing, the time and efforts could be greatly saved. Experiments are conducted on an actual eight-radar array system in a blast furnace, and the effectiveness of the proposed method is demonstrated on the real recorded data.
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Affiliation(s)
- Xiaopeng Liu
- School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
| | - Yan Liu
- School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
| | - Meng Zhang
- School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Instrument Science & Technology, Beijing 100083, China
| | - Xianzhong Chen
- School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
| | - Jiangyun Li
- School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
- Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China.
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617
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Huang Z, Xia J, Li F, Li Z, Li Q. A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy (Basel) 2019; 21:E709. [PMID: 33267423 DOI: 10.3390/e21070709] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/09/2019] [Accepted: 07/17/2019] [Indexed: 11/17/2022]
Abstract
Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error (MAPE¯) and root mean square error (RMSE¯) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.
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618
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Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput Methods Programs Biomed 2019; 176:121-133. [PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 05/03/2019] [Accepted: 05/09/2019] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues. METHODS A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network. RESULTS Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed. CONCLUSIONS One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
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Affiliation(s)
- Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli, Turkey.
| | | | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia
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619
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Oksuz I, Ruijsink B, Puyol-Antón E, Clough JR, Cruz G, Bustin A, Prieto C, Botnar R, Rueckert D, Schnabel JA, King AP. Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. Med Image Anal 2019; 55:136-147. [PMID: 31055126 PMCID: PMC6688894 DOI: 10.1016/j.media.2019.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 02/13/2019] [Accepted: 04/17/2019] [Indexed: 11/17/2022]
Abstract
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.
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Affiliation(s)
- Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK; Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - James R Clough
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Gastao Cruz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Rene Botnar
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College, London, UK
| | - Julia A Schnabel
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
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620
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Qian F, Chen L, Li J, Ding C, Chen X, Wang J. Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM. Int J Environ Res Public Health 2019; 16:ijerph16122133. [PMID: 31212880 PMCID: PMC6617190 DOI: 10.3390/ijerph16122133] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 06/04/2019] [Accepted: 06/05/2019] [Indexed: 01/13/2023]
Abstract
Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.
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Affiliation(s)
- Fei Qian
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China.
| | - Li Chen
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China.
| | - Jun Li
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China.
| | - Chao Ding
- State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China.
| | - Xianfu Chen
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China.
| | - Jian Wang
- State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China.
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621
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Zhang H, Hung CL, Liu M, Hu X, Lin YY. NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA. Front Genet 2019; 10:432. [PMID: 31191597 PMCID: PMC6549219 DOI: 10.3389/fgene.2019.00432] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 04/24/2019] [Indexed: 11/13/2022] Open
Abstract
The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep residual learning and sequence-to-sequence learning networks, to predict the transcription factor (TF) binding sites, which can then be used to predict non-coding functions. In NCNet, deep residual learning networks are used to enhance the identification rate of regulatory patterns of motifs, so that the sequence-to-sequence learning network may make the most out of the sequential dependency between the patterns. With the identity shortcut technique and deep architectures of the networks, NCNet achieves significant improvement compared to the original hybrid model in identifying regulatory markers.
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Affiliation(s)
- Hanyu Zhang
- College of Computing and Informatics, Providence University, Taichung City, Taiwan.,Labo MICS, École CentraleSup élec, Université Paris Saclay, Gif-sur-Yvette, France
| | - Che-Lun Hung
- Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan City, Taiwan.,Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan City, Taiwan.,AI Innovation Research Center, Chang Gung University, Taoyuan City, Taiwan.,Department of Computer Science and Communication Engineering, Providence University, Taichung City, Taiwan
| | - Meiyuan Liu
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Xiaoye Hu
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Yi-Yang Lin
- Department of Computer Science and Communication Engineering, Providence University, Taichung City, Taiwan
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622
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Xing X, Li Z, Xu T, Shu L, Hu B, Xu X. SAE+ LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG. Front Neurorobot 2019; 13:37. [PMID: 31244638 PMCID: PMC6581731 DOI: 10.3389/fnbot.2019.00037] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 05/24/2019] [Indexed: 11/29/2022] Open
Abstract
EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.
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Affiliation(s)
- Xiaofen Xing
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Zhenqi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Tianyuan Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Lin Shu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
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623
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Coto-Jiménez M. Improving Post-Filtering of Artificial Speech Using Pre-Trained LSTM Neural Networks. Biomimetics (Basel) 2019; 4:biomimetics4020039. [PMID: 31141924 PMCID: PMC6630405 DOI: 10.3390/biomimetics4020039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/16/2019] [Accepted: 05/22/2019] [Indexed: 11/16/2022] Open
Abstract
Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.
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Affiliation(s)
- Marvin Coto-Jiménez
- Escuela de Ingeniería Eléctrica, Universidad de Costa Rica, San José 11501-2060, Costa Rica.
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624
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Kutlu H, Avcı E. A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks. Sensors (Basel) 2019; 19:s19091992. [PMID: 31035406 PMCID: PMC6540219 DOI: 10.3390/s19091992] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/23/2019] [Accepted: 04/24/2019] [Indexed: 12/13/2022]
Abstract
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN-DWT-LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN-DWT-LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN-DWT-LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.
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Affiliation(s)
- Hüseyin Kutlu
- Computer Using Department, Besni Vocational School, Adıyaman University, Adıyaman 02300, Turkey.
| | - Engin Avcı
- Software Engineering Department, Technology Faculty, Fırat University, Elazığ 23000, Turkey.
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625
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Ye C, Li X, Chen J. A deep network for tissue microstructure estimation using modified LSTM units. Med Image Anal 2019; 55:49-64. [PMID: 31022640 DOI: 10.1016/j.media.2019.04.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 03/15/2019] [Accepted: 04/17/2019] [Indexed: 11/18/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) offers a unique tool for noninvasively assessing tissue microstructure. However, accurate estimation of tissue microstructure described by complicated signal models can be challenging when a reduced number of diffusion gradients are used. Deep learning based microstructure estimation has recently been developed and achieved promising results. In particular, optimization-based learning, where deep network structures are constructed by unfolding the iterative processes performed for solving optimization problems, has demonstrated great potential in accurate microstructure estimation with a reduced number of diffusion gradients. In this work, using the optimization-based learning strategy, we propose a deep network structure that is motivated by the use of historical information in iterative optimization for tissue microstructure estimation, and such incorporation of historical information has not been previously explored in the design of deep networks for microstructure estimation. We assume that (1) diffusion signals can be sparsely represented by a dictionary and its coefficients jointly in the spatial and angular domain, and (2) tissue microstructure can be computed from the sparse representation. Following these assumptions, our network comprises two cascaded stages. The first stage takes image patches as input and computes the spatial-angular sparse representation of the input with learned weights. Specifically, the network structure in the first stage is constructed by unfolding an iterative process for solving sparse reconstruction problems, where historical information is incorporated. The components in this network can be shown to correspond to modified long short-term memory (LSTM) units. In the second stage, fully connected layers are added to compute the mapping from the sparse representation to tissue microstructure. The weights in the two stages are learned jointly by minimizing the mean squared error of microstructure estimation. Experiments were performed on dMRI scans with a reduced number of diffusion gradients. For demonstration, we evaluated the estimation of tissue microstructure described by three signal models: the neurite orientation dispersion and density imaging (NODDI) model, the spherical mean technique (SMT) model, and the ensemble average propagator (EAP) model. The results indicate that the proposed approach outperforms competing methods.
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Affiliation(s)
- Chuyang Ye
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Xiuli Li
- Deepwise AI Lab, Beijing, China; Peng Cheng Laboratory, Shenzhen, China
| | - Jingnan Chen
- School of Economics and Management, Beihang University, Beijing, 37 Xueyuan Road, 100191, China.
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626
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Hernandez-Suarez A, Sanchez-Perez G, Toscano-Medina K, Perez-Meana H, Portillo-Portillo J, And Luis VS, Javier García Villalba L. Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation. Sensors (Basel) 2019; 19:s19071746. [PMID: 30979067 PMCID: PMC6484392 DOI: 10.3390/s19071746] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 03/29/2019] [Accepted: 03/29/2019] [Indexed: 11/16/2022]
Abstract
In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico's 2017 Earthquake is presented, and the data extracted during and after the event are reported.
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Affiliation(s)
| | | | | | - Hector Perez-Meana
- Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico.
| | | | - Victor Sanchez And Luis
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
- Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain.
| | - Luis Javier García Villalba
- Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain.
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627
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Zhang J, Wu Z, Li F, Xie C, Ren T, Chen J, Liu L. A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data. Sensors (Basel) 2019; 19:s19061356. [PMID: 30889917 PMCID: PMC6471704 DOI: 10.3390/s19061356] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 03/04/2019] [Accepted: 03/13/2019] [Indexed: 11/16/2022]
Abstract
Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods.
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Affiliation(s)
- Jun Zhang
- High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
- University of Science and Technology of China, Hefei 230026, China.
| | - ZhongCheng Wu
- High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
- University of Science and Technology of China, Hefei 230026, China.
| | - Fang Li
- High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Chengjun Xie
- Institute of Intelligent Machines, and Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Tingting Ren
- High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Jie Chen
- High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
- University of Science and Technology of China, Hefei 230026, China.
| | - Liu Liu
- Institute of Intelligent Machines, and Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
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628
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Shi X, Shao X, Guo Z, Wu G, Zhang H, Shibasaki R. Pedestrian Trajectory Prediction in Extremely Crowded Scenarios. Sensors (Basel) 2019; 19:E1223. [PMID: 30862018 DOI: 10.3390/s19051223] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 03/03/2019] [Accepted: 03/04/2019] [Indexed: 11/17/2022]
Abstract
Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians' trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.
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629
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Mei M, Chang J, Li Y, Li Z, Li X, Lv W. Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers. Sensors (Basel) 2019; 19:E1137. [PMID: 30845726 DOI: 10.3390/s19051137] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 02/28/2019] [Accepted: 03/01/2019] [Indexed: 11/16/2022]
Abstract
Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.
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630
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Chen Z, He N, Huang Y, Qin WT, Liu X, Li L. Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites. Genomics Proteomics Bioinformatics 2019; 16:451-459. [PMID: 30639696 PMCID: PMC6411950 DOI: 10.1016/j.gpb.2018.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 06/20/2018] [Accepted: 08/08/2018] [Indexed: 12/27/2022]
Abstract
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTMWE) for the prediction of mammalian malonylation sites. LSTMWE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTMWE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.
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Affiliation(s)
- Zhen Chen
- School of Basic Medicine, Qingdao University, Qingdao 266021, China
| | - Ningning He
- School of Basic Medicine, Qingdao University, Qingdao 266021, China
| | - Yu Huang
- School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China
| | - Wen Tao Qin
- Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario N6A 5C1, Canada
| | - Xuhan Liu
- Department of Information Technology, Beijing Oriental Yamei Gene Technology Institute Co. Ltd., Beijing 100078, China.
| | - Lei Li
- School of Basic Medicine, Qingdao University, Qingdao 266021, China; School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China; Qingdao Cancer Institute, Qingdao University, Qingdao 266021, China.
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631
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Khokhlova M, Migniot C, Morozov A, Sushkova O, Dipanda A. Normal and pathological gait classification LSTM model. Artif Intell Med 2019; 94:54-66. [PMID: 30871683 DOI: 10.1016/j.artmed.2018.12.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 12/27/2018] [Indexed: 10/27/2022]
Abstract
Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis. Second, we adapt to use the skeleton joint orientation data to calculate kinematic gait parameters to match golden-standard MOCAP systems. We propose a new set of features based on 3D low-limbs flexion dynamics to analyze the symmetry of a gait. Third, we design a Long-Short Term Memory (LSTM) ensemble model to create an unsupervised gait classification tool. The results show that joint orientation data provided by Kinect can be successfully used in an inexpensive clinical gait monitoring system, with the results moderately better than reported state-of-the-art for three normal/pathological gait classes.
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Affiliation(s)
| | | | - Alexey Morozov
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Moscow 125009, Russia
| | - Olga Sushkova
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Moscow 125009, Russia
| | - Albert Dipanda
- Le2i, FRE CNRS 2005, Univ. Bourgogne Franche-Comté, France.
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632
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Abstract
Background Chemical and biomedical named entity recognition (NER) is an essential preprocessing task in natural language processing. The identification and extraction of named entities from scientific articles is also attracting increasing interest in many scientific disciplines. Locating chemical named entities in the literature is an essential step in chemical text mining pipelines for identifying chemical mentions, their properties, and relations as discussed in the literature. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of chemical named entities. For this purpose, we transform the task of NER into a sequence labeling problem. We present a series of sequence labeling systems that we used, adapted and optimized in our experiments for solving this task. To this end, we experiment with hyperparameter optimization. Finally, we present LSTMVoter, a two-stage application of recurrent neural networks that integrates the optimized sequence labelers from our study into a single ensemble classifier. Results We introduce LSTMVoter, a bidirectional long short-term memory (LSTM) tagger that utilizes a conditional random field layer in conjunction with attention-based feature modeling. Our approach explores information about features that is modeled by means of an attention mechanism. LSTMVoter outperforms each extractor integrated by it in a series of experiments. On the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus, LSTMVoter achieves an F1-score of 90.04%; on the BioCreative V.5 chemical entity mention in patents corpus, it achieves an F1-score of 89.01%. Availability and implementation Data and code are available at https://github.com/texttechnologylab/LSTMVoter.
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Affiliation(s)
- Wahed Hemati
- Text Technology Lab, Goethe-University Frankfurt, Robert-Mayer-Straße 10, 60325, Frankfurt am Main, Germany.
| | - Alexander Mehler
- Text Technology Lab, Goethe-University Frankfurt, Robert-Mayer-Straße 10, 60325, Frankfurt am Main, Germany
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633
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Chen M, Miao Y, Gharavi H, Hu L, Humar I. Intelligent Traffic Adaptive Resource Allocation for Edge Computing-based 5G Networks. IEEE Trans Cogn Commun Netw 2019; 6:10.1109/tccn.2019.2953061. [PMID: 33490308 PMCID: PMC7818356 DOI: 10.1109/tccn.2019.2953061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The popularity of smart mobile devices has led to a tremendous increase in mobile traffic, which has put a considerable strain on the fifth generation of mobile communication networks (5G). Among the three application scenarios covered by 5G, ultra-high reliability and ultra-low latency (uRLLC) communication can best be realized with the assistance of artificial intelligence. For a combined 5G, edge computing and IoT-Cloud (a platform that integrates the Internet of Things and cloud) in particular, there remains many challenges to meet the uRLLC latency and reliability requirements despite a tremendous effort to develop smart data-driven methods. Therefore, this paper mainly focuses on artificial intelligence for controlling mobile-traffic flow. In our approach, we first develop a traffic-flow prediction algorithm that is based on long short-term memory (LSTM) with an attention mechanism to train mobile-traffic data in single-site mode. The algorithm is capable of effectively predicting the peak value of the traffic flow. For a multi-site case, we present an intelligent IoT-based mobile traffic prediction-and-control architecture capable of dynamically dispatching communication and computing resources. In our experiments, we demonstrate the effectiveness of the proposed scheme in reducing communication latency and its impact on lowering packet-loss ratio. Finally, we present future work and discuss some of the open issues.
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634
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Xiao L, Zhang Y, Peng G. Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway. Sensors (Basel) 2018; 18:s18124436. [PMID: 30558225 PMCID: PMC6308679 DOI: 10.3390/s18124436] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 11/27/2018] [Accepted: 12/12/2018] [Indexed: 11/16/2022]
Abstract
The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.
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Affiliation(s)
- Liming Xiao
- Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Yonghong Zhang
- Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Gongzhuang Peng
- Engineering Research Institute, University of Science and Technology Beijing, Beijing 100083, China.
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635
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Liu T, Bao J, Wang J, Zhang Y. A Hybrid CNN⁻ LSTM Algorithm for Online Defect Recognition of CO₂ Welding. Sensors (Basel) 2018; 18:s18124369. [PMID: 30544744 PMCID: PMC6308811 DOI: 10.3390/s18124369] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 12/06/2018] [Accepted: 12/06/2018] [Indexed: 11/23/2022]
Abstract
At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks.
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Affiliation(s)
- Tianyuan Liu
- College of Mechanical Engineering, Dong Hua University, Shanghai 201620, China.
| | - Jinsong Bao
- College of Mechanical Engineering, Dong Hua University, Shanghai 201620, China.
| | - Junliang Wang
- College of Mechanical Engineering, Dong Hua University, Shanghai 201620, China.
| | - Yiming Zhang
- College of Literature, Science and the Arts, The University of Michigan, Ann Arbor, MI 48109, USA.
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636
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Lim SM, Oh HC, Kim J, Lee J, Park J. LSTM-Guided Coaching Assistant for Table Tennis Practice. Sensors (Basel) 2018; 18:s18124112. [PMID: 30477175 PMCID: PMC6308608 DOI: 10.3390/s18124112] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 11/16/2018] [Accepted: 11/21/2018] [Indexed: 11/16/2022]
Abstract
Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.
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Affiliation(s)
- Se-Min Lim
- Department of Electronic and Information Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, Korea.
| | - Hyeong-Cheol Oh
- Department of Electronic and Information Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, Korea.
| | - Jaein Kim
- Department of Mathematics, Korea University, 145 Anam-ro, Anamdong 5-ga, Seoul 02841, Korea.
| | - Juwon Lee
- Department of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, Korea.
| | - Jooyoung Park
- Department of Control and Instrumentation Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, Korea.
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637
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Bjerrum EJ, Sattarov B. Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders. Biomolecules 2018; 8:E131. [PMID: 30380783 PMCID: PMC6316879 DOI: 10.3390/biom8040131] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 10/22/2018] [Accepted: 10/23/2018] [Indexed: 11/16/2022] Open
Abstract
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here, it is shown that the choice of chemical representation, such as strings from the simplified molecular-input line-entry system (SMILES), has a large influence on the properties of the latent space. It is further explored to what extent translating between different chemical representations influences the latent space similarity to the SMILES strings or circular fingerprints. By employing SMILES enumeration for either the encoder or decoder, it is found that the decoder has the largest influence on the properties of the latent space. Training a sequence to sequence heteroencoder based on recurrent neural networks (RNNs) with long short-term memory cells (LSTM) to predict different enumerated SMILES strings from the same canonical SMILES string gives the largest similarity between latent space distance and molecular similarity measured as circular fingerprints similarity. Using the output from the code layer in quantitative structure activity relationship (QSAR) of five molecular datasets shows that heteroencoder derived vectors markedly outperforms autoencoder derived vectors as well as models built using ECFP4 fingerprints, underlining the increased chemical relevance of the latent space. However, the use of enumeration during training of the decoder leads to a marked increase in the rate of decoding to different molecules than encoded, a tendency that can be counteracted with more complex network architectures.
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Affiliation(s)
- Esben Jannik Bjerrum
- Wildcard Pharmaceutical Consulting, Zeaborg Science Center, Frødings Allé 41, 2860 Søborg, Denmark.
| | - Boris Sattarov
- Science Data Software LLC, 14914 Bradwill Court, Rockville, MD 20850, USA.
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638
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Reddy BK, Delen D. Predicting hospital readmission for lupus patients: An RNN- LSTM-based deep-learning methodology. Comput Biol Med 2018; 101:199-209. [PMID: 30195164 DOI: 10.1016/j.compbiomed.2018.08.029] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 10/28/2022]
Abstract
Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act. However, patients diagnosed with lupus are the sixth highest in terms of rehospitalization. The heterogeneity in the disease and patient characteristics makes it very hard to predict rehospitalization. This research utilizes deep learning methods to predict rehospitalization within 30 days by extracting the temporal relationships in the longitudinal EHR clinical data. Prediction results from deep learning methods such as LSTM are evaluated and compared with traditional classification methods such as penalized logistic regression and artificial neural networks. The simple recurrent neural network method and its variant, gated recurrent unit network, are also developed and validated to compare their performance against the proposed LSTM model. The results indicated that the deep learning method RNN-LSTM has a significantly better performance (with an AUC of .70) compared to traditional classification methods such as ANN (with an AUC of 0.66) and penalized logistic regression (with an AUC of 0.63). The rationale for the better performance of the deep learning method may be due to its ability to leverage the temporal relationships of the disease state in patients over time and to capture the progression of the disease-relevant clinical information from patients' prior visits is carried forward in the memory, which may have enabled the higher predictability for the deep learning methods.
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Affiliation(s)
- Bhargava K Reddy
- UCB Biosciences, Inc., 8010 Arco Corporate Drive, Suite 100, Raleigh, NC, 27617, USA.
| | - Dursun Delen
- Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, OK, 74106, USA.
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639
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Mao D, Wang F, Hao Z, Li H. Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain. Int J Environ Res Public Health 2018; 15:E1627. [PMID: 30071695 DOI: 10.3390/ijerph15081627] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 07/24/2018] [Accepted: 07/26/2018] [Indexed: 02/06/2023]
Abstract
The food supply chain is a complex system that involves a multitude of "stakeholders" such as farmers, production factories, distributors, retailers and consumers. "Information asymmetry" between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders' credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.
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640
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Lee S, Lee D. Four Major South Korea's Rivers Using Deep Learning Models. Int J Environ Res Public Health 2018; 15:E1322. [PMID: 29937531 PMCID: PMC6069434 DOI: 10.3390/ijerph15071322] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 06/21/2018] [Accepted: 06/21/2018] [Indexed: 02/06/2023]
Abstract
Harmful algal blooms are an annual phenomenon that cause environmental damage, economic losses, and disease outbreaks. A fundamental solution to this problem is still lacking, thus, the best option for counteracting the effects of algal blooms is to improve advance warnings (predictions). However, existing physical prediction models have difficulties setting a clear coefficient indicating the relationship between each factor when predicting algal blooms, and many variable data sources are required for the analysis. These limitations are accompanied by high time and economic costs. Meanwhile, artificial intelligence and deep learning methods have become increasingly common in scientific research; attempts to apply the long short-term memory (LSTM) model to environmental research problems are increasing because the LSTM model exhibits good performance for time-series data prediction. However, few studies have applied deep learning models or LSTM to algal bloom prediction, especially in South Korea, where algal blooms occur annually. Therefore, we employed the LSTM model for algal bloom prediction in four major rivers of South Korea. We conducted short-term (one week) predictions by employing regression analysis and deep learning techniques on a newly constructed water quality and quantity dataset drawn from 16 dammed pools on the rivers. Three deep learning models (multilayer perceptron, MLP; recurrent neural network, RNN; and long short-term memory, LSTM) were used to predict chlorophyll-a, a recognized proxy for algal activity. The results were compared to those from OLS (ordinary least square) regression analysis and actual data based on the root mean square error (RSME). The LSTM model showed the highest prediction rate for harmful algal blooms and all deep learning models out-performed the OLS regression analysis. Our results reveal the potential for predicting algal blooms using LSTM and deep learning.
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Affiliation(s)
- Sangmok Lee
- Department of Business Administration, Korea Polytechnic University, 237, Sangidaehak-ro, Siheung-si, Gyeonggi-do 15073, Korea.
| | - Donghyun Lee
- Department of Business Administration, Korea Polytechnic University, 237, Sangidaehak-ro, Siheung-si, Gyeonggi-do 15073, Korea.
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641
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Wang L, Duan X, Zhang Q, Niu Z, Hua G, Zheng N. Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation. Sensors (Basel) 2018; 18:E1657. [PMID: 29789447 PMCID: PMC5982167 DOI: 10.3390/s18051657] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 05/16/2018] [Accepted: 05/16/2018] [Indexed: 11/16/2022]
Abstract
Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting/ending frame of each action category in the presence of preceding/subsequent interference actions in untrimmed videos. Simultaneously, the Segment-tube detector produces per-frame segmentation masks instead of bounding boxes, offering superior spatial accuracy to tubelets. This is achieved by alternating iterative optimization between temporal action localization and spatial action segmentation. Experimental results on three datasets validated the efficacy of the proposed method, including (1) temporal action localization on the THUMOS 2014 dataset; (2) spatial action segmentation on the Segtrack dataset; and (3) joint spatio-temporal action localization on the newly proposed ActSeg dataset. It is shown that our method compares favorably with existing state-of-the-art methods.
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Affiliation(s)
- Le Wang
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shannxi 710049, China.
| | - Xuhuan Duan
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shannxi 710049, China.
| | | | | | - Gang Hua
- Microsoft Research, Redmond, WA 98052, USA.
| | - Nanning Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shannxi 710049, China.
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642
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Alzantot M, Wang Y, Ren Z, Srivastava MB. RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices. MobiSys 2018; 2017:7-12. [PMID: 29629431 DOI: 10.1145/3089801.3089805] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.
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Affiliation(s)
| | - Yingnan Wang
- University of California, Los Angeles, Los Angeles, CA 90095
| | - Zhengshuang Ren
- University of California, Los Angeles, Los Angeles, CA 90095
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643
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Zimmermann T, Taetz B, Bleser G. IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning. Sensors (Basel) 2018; 18:s18010302. [PMID: 29351262 PMCID: PMC5795510 DOI: 10.3390/s18010302] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 01/09/2018] [Accepted: 01/10/2018] [Indexed: 12/05/2022]
Abstract
Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of 2.91° for the I2S alignment task.
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Affiliation(s)
- Tobias Zimmermann
- Junior Research Group wearHEALTH, University of Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany.
- Augmented Vision Department, DFKI, Trippstadter Str. 122, 67663 Kaiserslautern, Germany.
| | - Bertram Taetz
- Junior Research Group wearHEALTH, University of Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany.
- Augmented Vision Department, DFKI, Trippstadter Str. 122, 67663 Kaiserslautern, Germany.
| | - Gabriele Bleser
- Junior Research Group wearHEALTH, University of Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany.
- Augmented Vision Department, DFKI, Trippstadter Str. 122, 67663 Kaiserslautern, Germany.
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644
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Nagarajan D, Nagarajan T, Roy N, Kulkarni O, Ravichandran S, Mishra M, Chakravortty D, Chandra N. Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria. J Biol Chem 2017; 293:3492-3509. [PMID: 29259134 DOI: 10.1074/jbc.m117.805499] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 12/04/2017] [Indexed: 12/19/2022] Open
Abstract
There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum β-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.
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Affiliation(s)
| | | | | | | | | | | | - Dipshikha Chakravortty
- Department of Microbiology and Cell Biology, and.,Centre for Biosystems Science and Engineering, Indian Institute of Science (IISc), Bangalore 560012, India
| | - Nagasuma Chandra
- From the Departments of Biochemistry and .,Centre for Biosystems Science and Engineering, Indian Institute of Science (IISc), Bangalore 560012, India
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645
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Abstract
Background Biomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features. Results We present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus. Conclusions Our neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER.
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Affiliation(s)
- Chen Lyu
- School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China
| | - Bo Chen
- Department of Chinese Language & Literature, Hubei University of Art & Science, Xiangyang, 24105, Hubei, China
| | - Yafeng Ren
- Guangdong Collaborative Innovation Center for Language Research & Services, Guangdong University of Foreign Studies, Guangzhou, 510420, Guangdong, China
| | - Donghong Ji
- School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China.
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646
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Bhalla US. Dendrites, deep learning, and sequences in the hippocampus. Hippocampus 2017; 29:239-251. [PMID: 29024221 DOI: 10.1002/hipo.22806] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 10/06/2017] [Accepted: 10/10/2017] [Indexed: 11/06/2022]
Abstract
The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.
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Affiliation(s)
- Upinder S Bhalla
- Neurobiology, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bangalore 560065, Karnataka, India
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647
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Li X, Zhang Y, Zhang J, Zhou M, Chen S, Gu Y, Chen Y, Marsic I, Farneth RA, Burd RS. Progress Estimation and Phase Detection for Sequential Processes. ACTA ACUST UNITED AC 2017; 1. [PMID: 30417164 DOI: 10.1145/3130936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.
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Affiliation(s)
- Xinyu Li
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Yanyi Zhang
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Jianyu Zhang
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Moliang Zhou
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Shuhong Chen
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Yue Gu
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Yueyang Chen
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey;
| | - Ivan Marsic
- Rutgers, the State University of New Jersey, USA, Electrical & Computer Engineering Building, 94 Brett Road, Piscataway, New Jersey
| | - Richard A Farneth
- Division of Trauma and Burn Surgery, Children's National Medical Center, Washington, D.C., 20010, USA;
| | - Randall S Burd
- Division of Trauma and Burn Surgery, Children's National Medical Center, Washington, D.C., 20010, USA;
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648
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Yu Z, Moirangthem DS, Lee M. Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding. Front Neurorobot 2017; 11:42. [PMID: 28878646 PMCID: PMC5572368 DOI: 10.3389/fnbot.2017.00042] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Understanding of human intention by observing a series of human actions has been a challenging task. In order to do so, we need to analyze longer sequences of human actions related with intentions and extract the context from the dynamic features. The multiple timescales recurrent neural network (MTRNN) model, which is believed to be a kind of solution, is a useful tool for recording and regenerating a continuous signal for dynamic tasks. However, the conventional MTRNN suffers from the vanishing gradient problem which renders it impossible to be used for longer sequence understanding. To address this problem, we propose a new model named Continuous Timescale Long-Short Term Memory (CTLSTM) in which we inherit the multiple timescales concept into the Long-Short Term Memory (LSTM) recurrent neural network (RNN) that addresses the vanishing gradient problem. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context. Our experiments show that the proposed model exhibits better context modeling ability and captures the dynamic features on multiple large dataset classification tasks. The results illustrate that the multiple timescales concept enhances the ability of our model to handle longer sequences related with human intentions and hence proving to be more suitable for complex tasks, such as intention recognition.
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Affiliation(s)
- Zhibin Yu
- Department of Electrical Engineering, College of Information Science and Engineering, Ocean University of ChinaQingdao, China
| | - Dennis S Moirangthem
- School of Electronics Engineering, Kyungpook National UniversityDaegu, South Korea
| | - Minho Lee
- School of Electronics Engineering, Kyungpook National UniversityDaegu, South Korea
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649
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Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med. 2017;89:248-255. [PMID: 28843829 DOI: 10.1016/j.compbiomed.2017.08.015] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Revised: 08/15/2017] [Accepted: 08/15/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. OBJECTIVE This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. METHOD Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. RESULTS With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. CONCLUSIONS The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns.
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650
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Ni Z, Yuksel AC, Ni X, Mandel MI, Xie L. Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks. ACM BCB 2017; 2017:241-246. [PMID: 28966996 PMCID: PMC5620019 DOI: 10.1145/3107411.3107513] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.
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Affiliation(s)
- Zhaoheng Ni
- The Graduate Center, City University of New York, New York, NY 10016, USA
| | - Ahmet Cem Yuksel
- The Graduate Center, City University of New York, New York, NY 10016, USA
| | - Xiuyan Ni
- The Graduate Center, City University of New York, New York, NY 10016, USA
| | - Michael I Mandel
- Brooklyn College, City University of New York, Brooklyn, NY 11210, USA
| | - Lei Xie
- Hunter College, City University of New York, New York, NY 10065, USA
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