Author: "Li, Yuanqing" / Topic: deep learning - Searchworks@Jio Institute Digital Library Search Results

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1. Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Author: Ye Y, Xia L, Yang S, Luo Y, Tang Z, Li Y, Han L, Xie H, Ren Y, and Na N
Subjects: Humans, Biopsy, Male, Female, Allografts pathology, Adult, Middle Aged, Kidney pathology, Kidney immunology, Reproducibility of Results, Deep Learning, Kidney Transplantation adverse effects, Graft Rejection pathology, Graft Rejection immunology, Graft Rejection diagnosis
Abstract: Background: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection., Method: We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations., Results: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively., Conclusion: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ye, Xia, Yang, Luo, Tang, Li, Han, Xie, Ren and Na.)
Published: 2024
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2. A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.

Author: Zeng J, Deng W, Yu J, Xiao L, Chen S, Zhang X, Zeng L, Chen D, Li P, Chen Y, Zhang H, Shu F, Wu M, Su Y, Li Y, Cai Y, and Zheng Y
Subjects: Humans, Ear, Middle, Tympanic Membrane, Deep Learning, Otitis Media, Otitis Media with Effusion diagnosis, Otitis Media with Effusion diagnostic imaging, Pulmonary Atelectasis
Abstract: Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images., Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images., Results: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM., Conclusion: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME., (© 2022. The Author(s).)
Published: 2023
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3. The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning.

Author: Li Y and Li X
Subjects: Artificial Intelligence, Female, Humans, Physical Fitness, Quality of Life, Deep Learning, Sports
Abstract: In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into all walks of life. In particular, the in-depth integration of AI technology with sports education guidance and physical fitness evaluation has achieved very significant progress and results, which has improved the quality of life of people and provided more high-quality, customized, and personalized health management services for human beings. This study aimed to explore the application model of deep learning in sports education and guidance and in the analysis of the residents' physical fitness, so as to formulate a personalized and intelligent exercise program. The residents of A and B units are selected as the research object to evaluate the physical fitness. Subsequently, the self-designed questionnaire is used to survey the chronic disease online, and the acquired data are put into a deep learning model for the analysis to obtain the physique scoring results and exercise guidance. According to the results of physical fitness evaluation, the proportion of overweight was the highest (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%), showing high incidence in people aged 41-50 years. The highest incidence of female gynecological diseases was gout (23.0%) and hyperlipidemia (20.6%). After exercise therapy, the scores were excellent and good. Conclusions: The database SQL Server 2005 was a platform for storing all kinds of data and knowledge-based rule information. The user's access service was provided by the remote server via the browser. Therefore, building a rule-based reasoning mechanism can realize physical test data collection, physical fitness evaluation, and information management for improving physical fitness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li and Li.)
Published: 2022
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4. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.

Author: Xiong H, Lin P, Yu JG, Ye J, Xiao L, Tao Y, Jiang Z, Lin W, Liu M, Xu J, Hu W, Lu Y, Liu H, Li Y, Zheng Y, and Yang H
Subjects: Humans, Models, Theoretical, Reproducibility of Results, Sensitivity and Specificity, Deep Learning, Diagnosis, Computer-Assisted, Image Processing, Computer-Assisted, Laryngeal Neoplasms diagnosis, Laryngoscopy methods
Abstract: Objective: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images., Methods: A DCNN-based diagnostic system was constructed and trained using 13,721 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM) from 2 tertiary hospitals in China, including 2293 from 206 LCA subjects, 1807 from 203 PRELCA subjects, 6448 from 774 BLT subjects and 3191 from 633 NORM subjects. An independent test set of 1176 laryngoscopic images from other 3 tertiary hospitals in China, including 132 from 44 LCA subjects, 129 from 43 PRELCA subjects, 504 from 168 BLT subjects and 411 from 137 NORM subjects, was applied to the constructed DCNN to evaluate its performance against experienced endoscopists., Results: The DCCN achieved a sensitivity of 0.731, a specificity of 0.922, an AUC of 0.922, and the overall accuracy of 0.867 for detecting LCA and PRELCA among all lesions and normal tissues. When compared to human experts in an independent test set, the DCCN' s performance on detection of LCA and PRELCA achieved a sensitivity of 0.720, a specificity of 0.948, an AUC of 0.953, and the overall accuracy of 0.897, which was comparable to that of an experienced human expert with 10-20 years of work experience. Moreover, the overall accuracy of DCNN for detection of LCA was 0.773, which was also comparable to that of an experienced human expert with 10-20 years of work experience and exceeded the experts with less than 10 years of work experience., Conclusions: The DCNN has high sensitivity and specificity for automated detection of LCA and PRELCA from BLT and NORM in laryngoscopic images. This novel and effective approach facilitates earlier diagnosis of early LCA, resulting in improved clinical outcomes and reducing the burden of endoscopists., (Copyright © 2018. Published by Elsevier B.V.)
Published: 2019
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5. The Improved U-STFM: A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling.

Author: Guo, Shanxin, Li, Min, Li, Yuanqing, Chen, Jinsong, Zhang, Hankui K., Sun, Luyi, Wang, Jingwen, Wang, Ruxin, and Yang, Yan
Subjects: DEEP learning, LAND surface temperature, DOWNSCALING (Climatology), ATMOSPHERE, ERROR functions
Abstract: The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction. [ABSTRACT FROM AUTHOR]
Published: 2024
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6. Feature Fusion for Multimodal Emotion Recognition Based on Deep Canonical Correlation Analysis.

Author: Zhang, Ke, Li, Yuanqing, Wang, Jingyu, Wang, Zhen, and Li, Xuelong
Subjects: EMOTION recognition, STATISTICAL correlation, DEEP learning, CANONICAL correlation (Statistics), LOGIC circuits, FEATURE extraction
Abstract: Fusion of multimodal features is a momentous problem for video emotion recognition. As the development of deep learning, directly fusing feature matrixes of each mode through neural networks at feature level becomes mainstream method. However, unlike unimodal issues, for multimodal analysis, finding the correlations between different modal is as important as discovering effective unimodal features. To make up the deficiency in unearthing the intrinsic relationships between multimodal, a novel modularized multimodal emotion recognition model based on deep canonical correlation analysis (MERDCCA) is proposed in this letter. In MERDCCA, four utterances are gathered as a new group and each utterance contains text, audio and visual information as multimodal input. Gated recurrent unit layers are used to extract the unimodal features. Deep canonical correlation analysis based on encoder-decoder network is designed to extract cross-modal correlations by maximizing the relevance between multimodal. The experiments on two public datasets show that MERDCCA achieves the better results. [ABSTRACT FROM AUTHOR]
Published: 2021
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7. Capsule Network for ERP Detection in Brain-Computer Interface.

Author: Ma, Ronghua, Yu, Tianyou, Zhong, Xiaoli, Yu, Zhu Liang, Li, Yuanqing, and Gu, Zhenghui
Subjects: CAPSULE neural networks, BRAIN-computer interfaces, EVOKED potentials (Electrophysiology), COGNITIVE neuroscience, PATTERN recognition systems
Abstract: Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism. [ABSTRACT FROM AUTHOR]
Published: 2021
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8. Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain–Computer Interfaces.

Author: Chen, Junjian, Yu, Zhuliang, Gu, Zhenghui, and Li, Yuanqing
Subjects: BRAIN-computer interfaces, CONVOLUTIONAL neural networks, MOTOR learning, DEEP learning, FEATURE extraction, FILTER banks
Abstract: Motor imagery (MI) decoding is an important part of brain-computer interface (BCI) research, which translates the subject’s intentions into commands that external devices can execute. The traditional methods for discriminative feature extraction, such as common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP), have only focused on the energy features of the electroencephalography (EEG) and thus ignored the further exploration of temporal information. However, the temporal information of spatially filtered EEG may be critical to the performance improvement of MI decoding. In this paper, we proposed a deep learning approach termed filter-bank spatial filtering and temporal-spatial convolutional neural network (FBSF-TSCNN) for MI decoding, where the FBSF block transforms the raw EEG signals into an appropriate intermediate EEG presentation, and then the TSCNN block decodes the intermediate EEG signals. Moreover, a novel stage-wise training strategy is proposed to mitigate the difficult optimization problem of the TSCNN block in the case of insufficient training samples. Firstly, the feature extraction layers are trained by optimization of the triplet loss. Then, the classification layers are trained by optimization of the cross-entropy loss. Finally, the entire network (TSCNN) is fine-tuned by the back-propagation (BP) algorithm. Experimental evaluations on the BCI IV 2a and SMR-BCI datasets reveal that the proposed stage-wise training strategy yields significant performance improvement compared with the conventional end-to-end training strategy, and the proposed approach is comparable with the state-of-the-art method. [ABSTRACT FROM AUTHOR]
Published: 2020
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9. A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

Author: Li, Jingcong, Yu, Zhu Liang, Gu, Zhenghui, Wu, Wei, Li, Yuanqing, and Jin, Lianwen
Subjects: EVOKED potentials (Electrophysiology), BOLTZMANN machine, SIGNAL-to-noise ratio
Abstract: Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET. With hybrid deep structure, the proposed network is able to learn complex spatial and temporal patterns from single-trial ERP signals. To verify the effectiveness of ERP-NET, we carried out a few ERP detection experiments that the proposed model achieved cutting-edge performance. The experimental results demonstrate that the patterns learned by the ERP-NET are discriminative ERP components in which the ERP signals are properly characterized. More importantly, as an effective approach to single-trial analysis, ERP-NET is able to discover new ERP patterns which are significant to neuroscience study as well as BCI applications. Therefore, the proposed ERP-NET is a promising tool for the research on ERP signals. [ABSTRACT FROM AUTHOR]
Published: 2018
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10. A Deep Learning Method to Detect Web Attacks Using a Specially Designed CNN

Author: Zhang, Ming, Xu, Boyi, Bai, Shuai, Lu, Shuaibing, Lin, Zhechao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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11. Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media

Author: Abbes, Mariem, Kechaou, Zied, Alimi, Adel M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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12. Thyroid Nodule Classification Using Hierarchical Recurrent Neural Network with Multiple Ultrasound Reports

Author: Chen, Dehua, Shi, Cheng, Wang, Mei, Pan, Qiao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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13. Prediction of Stroke Using Deep Learning Model

Author: Chantamit-o-pas, Pattanapong, Goyal, Madhu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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14. Category Prediction of Questions Posted in Community-Based Question Answering Services Using Deep Learning Methods

Author: Ma, Qing, Kato, Reo, Murata, Masaki, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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15. Deep Sequence-to-Sequence Neural Networks for Ionospheric Activity Map Prediction

Author: Cherrier, Noëlie, Castaings, Thibaut, Boulch, Alexandre, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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16. Tree Factored Conditional Restricted Boltzmann Machines for Mixed Motion Style

Author: Xie, Chunzhi, Lv, Jiancheng, Jia, Bijue, Xia, Lei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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17. Deep Bi-directional Long Short-Term Memory Model for Short-Term Traffic Flow Prediction

Author: Wang, Jingyuan, Hu, Fei, Li, Li, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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18. Extracting Deep Semantic Information for Intelligent Recommendation

Author: Chen, Wang, Zheng, Hai-Tao, Mao, Xiao-Xi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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19. A New Vector Space Model Based on the Deep Learning

Author: Karamti, Hanen, Tmar, Mohamed, Gargouri, Faiez, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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20. CNN Based Transfer Learning for Scene Script Identification

Author: Tounsi, Maroua, Moalla, Ikram, Lebourgeois, Frank, Alimi, Adel M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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21. Supervised Deep Canonical Correlation Analysis for Multiview Feature Learning

Author: Liu, Yan, Li, Yun, Yuan, Yun-Hao, Qiang, Ji-Peng, Ruan, Min, Zhang, Zhao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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22. On-Road Object Detection Based on Deep Residual Networks

Author: Chen, Kang, Zhao, Qi, Lin, Yaorong, Zhang, Jun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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23. Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography

Author: Tan, Chuanqi, Sun, Fuchun, Zhang, Wenchang, Chen, Jianhua, Liu, Chunfang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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24. Ultra-deep Neural Network for Face Anti-spoofing

Author: Tu, Xiaokang, Fang, Yuchun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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25. Intent Recognition in Smart Living Through Deep Recurrent Neural Networks

Author: Zhang, Xiang, Yao, Lina, Huang, Chaoran, Sheng, Quan Z., Wang, Xianzhi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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26. TinyPoseNet: A Fast and Compact Deep Network for Robust Head Pose Estimation

Author: Li, Shanru, Wang, Liping, Yang, Shuang, Wang, Yuanquan, Wang, Chongwen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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27. Layer-Wise Training to Create Efficient Convolutional Neural Networks

Author: Zeng, Linghua, Tian, Xinmei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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28. Robust Deep Face Recognition with Label Noise

Author: Yuan, Jirui, Ma, Wenya, Zhu, Pengfei, Egiazarian, Karen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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29. Breast Cancer Malignancy Prediction Using Incremental Combination of Multiple Recurrent Neural Networks

Author: Chen, Dehua, Qian, Guangjun, Shi, Cheng, Pan, Qiao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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30. 3HAN: A Deep Neural Network for Fake News Detection

Author: Singhania, Sneha, Fernandez, Nigel, Rao, Shrisha, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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31. Knowledge Memory Based LSTM Model for Answer Selection

Author: An, Weijie, Chen, Qin, Yang, Yan, He, Liang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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32. Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs

Author: Al-Azani, Sadam, El-Alfy, El-Sayed M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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33. Regularized Deep Convolutional Neural Networks for Feature Extraction and Classification

Author: Jayech, Khaoula, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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34. Layer Removal for Transfer Learning with Deep Convolutional Neural Networks

Author: Zhi, Weiming, Chen, Zhenghao, Yueng, Henry Wing Fung, Lu, Zhicheng, Zandavi, Seid Miad, Chung, Yuk Ying, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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35. Temporal Attention Neural Network for Video Understanding

Author: Son, Jegyung, Jang, Gil-Jin, Lee, Minho, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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36. Regularizing CNN via Feature Augmentation

Author: Ou, Liechuan, Chen, Zheng, Lu, Jianwei, Luo, Ye, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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37. An Incremental Deep Learning Network for On-line Unsupervised Feature Extraction

Author: Liang, Yu, Yang, Yi, Shen, Furao, Zhao, Jinxi, Zhu, Tao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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38. Training Deep Neural Networks for Detecting Drinking Glasses Using Synthetic Images

Author: Jabbar, Abdul, Farrawell, Luke, Fountain, Jake, Chalup, Stephan K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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39. Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net

Author: Zhang, Qiao, Cui, Zhipeng, Niu, Xiaoguang, Geng, Shijie, Qiao, Yu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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40. DeepBIBX: Deep Learning for Image Based Bibliographic Data Extraction

Author: Bhardwaj, Akansha, Mercier, Dominik, Dengel, Andreas, Ahmed, Sheraz, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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41. Will Outlier Tasks Deteriorate Multitask Deep Learning?

Author: Cai, Sirui, Fang, Yuchun, Ma, Zhengyan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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42. Patterns Versus Characters in Subword-Aware Neural Language Modeling

Author: Takhanov, Rustem, Assylbekov, Zhenisbek, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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43. Three-Means Ternary Quantization

Author: Ding, Jie, Wu, JunMin, Wu, Huan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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44. Learning Joint Multimodal Representation Based on Multi-fusion Deep Neural Networks

Author: Gu, Zepeng, Lang, Bo, Yue, Tongyu, Huang, Lei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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45. The Effect of Task Similarity on Deep Transfer Learning

Author: Zhang, Wei, Fang, Yuchun, Ma, Zhengyan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
Full Text: View/download PDF

46. Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network

Author: Yamanaka, Jin, Kuwashima, Shigesumi, Kurita, Takio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
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47. An Efficient Binary Search Based Neuron Pruning Method for ConvNet Condensation

Author: Zhang, Boyu, Qin, A. K., Chan, Jeffrey, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
Full Text: View/download PDF

48. Morph-CNN: A Morphological Convolutional Neural Network for Image Classification

Author: Mellouli, Dorra, Hamdani, Tarek M., Ayed, Mounir Ben, Alimi, Adel M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
Full Text: View/download PDF

49. Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images

Author: He, Jia, Shang, Lin, Ji, Hong, Zhang, XiuLing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
Full Text: View/download PDF

50. Using Transfer Learning with Convolutional Neural Networks to Diagnose Breast Cancer from Histopathological Images

Author: Zhi, Weiming, Yueng, Henry Wing Fung, Chen, Zhenghao, Zandavi, Seid Miad, Lu, Zhicheng, Chung, Yuk Ying, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
Published: 2017
Full Text: View/download PDF

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