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Qiao Y, Yang R, Liu Y, Chen J, Zhao L, Huo P, Wang Z, Bu D, Wu Y, Zhao Y. DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures. Comput Struct Biotechnol J 2024; 23:617-625. [PMID: 38274994 PMCID: PMC10808905 DOI: 10.1016/j.csbj.2023.12.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 09/06/2023] [Revised: 12/04/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rui Yang
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Liu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxin Chen
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Peipei Huo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhihao Wang
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Wang Z, Yu Y, Liu J, Zhang Q, Guo X, Yang Y, Shi Y. Peanut origin traceability: A hybrid neural network combining an electronic nose system and a hyperspectral system. Food Chem 2024; 447:138915. [PMID: 38452539 DOI: 10.1016/j.foodchem.2024.138915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 11/27/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/09/2024]
Abstract
Peanuts, sourced from various regions, exhibit noticeable differences in quality owing to the impact of their natural environments. This study proposes a fast and nondestructive detection method to identify peanut quality by combining an electronic nose system with a hyperspectral system. First, the electronic nose and hyperspectral systems are used to gather gas and spectral information from peanuts. Second, a module for extracting gas and spectral information is designed, combining the lightweight multi-head transposed attention mechanism (LMTA) and convolutional computation. The fusion of gas and spectral information is achieved through matrix combination and lightweight convolution. A hybrid neural network, named UnitFormer, is designed based on the information extraction and fusion processes. UnitFormer demonstrates an accuracy of 99.06 %, a precision of 99.12 %, and a recall of 99.05 %. In conclusion, UnitFormer effectively distinguishes quality differences among peanuts from various regions, offering an effective technological solution for quality supervision in the food market.
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Affiliation(s)
- Zi Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yang Yu
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China; Advanced Sensor Research Institution, Northeast Electric Power University, Jilin 132012, China.
| | - Junqi Liu
- School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Qinglun Zhang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
| | - Xiaoqin Guo
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yixin Yang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Yan Shi
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China; Advanced Sensor Research Institution, Northeast Electric Power University, Jilin 132012, China.
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Ma L, Wan C, Hao K, Cai A, Liu L. A novel fusion algorithm for benign-malignant lung nodule classification on CT images. BMC Pulm Med 2023; 23:474. [PMID: 38012620 PMCID: PMC10683224 DOI: 10.1186/s12890-023-02708-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 10/12/2023] [Indexed: 11/29/2023] Open
Abstract
The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.
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Affiliation(s)
- Ling Ma
- College of Software, Nankai University, Tianjin, 300350, China
| | - Chuangye Wan
- College of Software, Nankai University, Tianjin, 300350, China
| | - Kexin Hao
- College of Software, Nankai University, Tianjin, 300350, China
| | - Annan Cai
- College of Software, Nankai University, Tianjin, 300350, China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
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Yang X, Yang Y, Meng L, Zhao Y. Spatio-temporal PV power forecasting considering the time-shift correction and the information fusion strategy of multi-stations. ISA Trans 2023; 139:376-390. [PMID: 37062606 DOI: 10.1016/j.isatra.2023.03.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/07/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data.
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Affiliation(s)
- Xiyun Yang
- School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Yan Yang
- School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Lingzhuochao Meng
- School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China.
| | - Ya Zhao
- School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China.
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Huang J, Huang Z, Zhan X. Research on three-state reliability evaluation method of high reliability system based on multi-source prior information. PeerJ Comput Sci 2023; 9:e1439. [PMID: 37547401 PMCID: PMC10403173 DOI: 10.7717/peerj-cs.1439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/24/2023] [Indexed: 08/08/2023]
Abstract
A high reliability system has the characteristics of complexity, modularization, high cost and small sample size. Throughout the entire lifecycle of system development, storage and use, the high reliability requirements and the risk analysis form a direct contradiction with the testing expenses. In order to ensure the system, module or component maintains good reliability status and effectively reduces the cost of sampling tests, it is necessary to make full use of multi-source prior information to evaluate its reliability. Therefore, in order to evaluate the reliability of highly reliable equipment under the condition of a small sample size correctly, the equipment reliability evaluation model should be built based on multi-source prior information and form scientific computing methods to meet the needs of condition evaluation and fund assurance of high reliability system. In engineering practice, high reliability system or module gradually develops from normal state to failure state, generally going through three working states of "safety-potential failure-functional failure". Firstly, the historical test data under the three states can be used for the data source for the reliability evaluation of the system at the current stage, which supplements the deficiency of the field data; secondly, due to the lack of accurate judgment on the working state of a high reliability system or modules and analysis of the health status, the unnecessary maintenance may aggravate the evolution speed from potential failure to functional failure; thirdly, when high reliability system or module operates under overload or harsh conditions, the potential failure will be worsened to a certain extent. Aiming at the difficulty of multi-state system reliability evaluation, a reliability evaluation method based on non-information prior distribution is proposed by fusing multi-source prior information, which provides ideas and methods for reliability evaluation and optimization analysis of high reliability system or module. The results show that the three-state reliability evaluation method proposed in this article is consistent with the actual engineering situation, providing a scientific theoretical basis for preventive maintenance of high reliability system. At the same time, the research method not only helps evaluate the reliability state of a high reliability system accurately, but also achieves the goal of effectively reducing test costs with good economic benefits and engineering application value.
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Affiliation(s)
- Jingde Huang
- Guangdong Intelligent Vision Precision Detection Engineering Technology Research Center, Zhuhai College of Science and Technology, Zhuhai, China
| | - Zhangyu Huang
- Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
| | - Xin Zhan
- School of Mechanical Engineering, Zhuhai College of Science and Technology, Zhuhai, China
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Wang J, Tian S, Yu L, Zhou Z, Wang F, Wang Y. HIGF-Net: Hierarchical information-guided fusion network for polyp segmentation based on transformer and convolution feature learning. Comput Biol Med 2023; 161:107038. [PMID: 37230017 DOI: 10.1016/j.compbiomed.2023.107038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 07/27/2022] [Revised: 01/22/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023]
Abstract
Polyp segmentation plays a role in image analysis during colonoscopy screening, thus improving the diagnostic efficiency of early colorectal cancer. However, due to the variable shape and size characteristics of polyps, small difference between lesion area and background, and interference of image acquisition conditions, existing segmentation methods have the phenomenon of missing polyp and rough boundary division. To overcome the above challenges, we propose a multi-level fusion network called HIGF-Net, which uses hierarchical guidance strategy to aggregate rich information to produce reliable segmentation results. Specifically, our HIGF-Net excavates deep global semantic information and shallow local spatial features of images together with Transformer encoder and CNN encoder. Then, Double-stream structure is used to transmit polyp shape properties between feature layers at different depths. The module calibrates the position and shape of polyps in different sizes to improve the model's efficient use of the rich polyp features. In addition, Separate Refinement module refines the polyp profile in the uncertain region to highlight the difference between the polyp and the background. Finally, in order to adapt to diverse collection environments, Hierarchical Pyramid Fusion module merges the features of multiple layers with different representational capabilities. We evaluate the learning and generalization abilities of HIGF-Net on five datasets using six evaluation metrics, including Kvasir-SEG, CVC-ClinicDB, ETIS, CVC-300, and CVC-ColonDB. Experimental results show that the proposed model is effective in polyp feature mining and lesion identification, and its segmentation performance is better than ten excellent models.
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Affiliation(s)
- Junwen Wang
- College of Software, Xinjiang University, Urumqi, 830000, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China
| | - Shengwei Tian
- College of Software, Xinjiang University, Urumqi, 830000, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China.
| | - Long Yu
- College of Network Center, Xinjiang University, Urumqi, 830000, China; Signal and Signal Processing Laboratory, College of Information Science and Engineering, Xinjiang University, Urumqi, 830000, China
| | - Zhicheng Zhou
- College of Software, Xinjiang University, Urumqi, 830000, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China
| | - Fan Wang
- College of Software, Xinjiang University, Urumqi, 830000, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China
| | - Yongtao Wang
- College of Software, Xinjiang University, Urumqi, 830000, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China
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Cai Z, Huang Z, He M, Li C, Qi H, Peng J, Zhou F, Zhang C. Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches. Food Chem 2023; 422:136169. [PMID: 37119596 DOI: 10.1016/j.foodchem.2023.136169] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 07/17/2022] [Revised: 04/04/2023] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.
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Affiliation(s)
- Zeyi Cai
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Zihong Huang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Mengyu He
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fei Zhou
- College of Standardization, China Jiliang University, Hangzhou 310018, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China.
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Wu P, Wang Z, Zheng B, Li H, Alsaadi FE, Zeng N. AGGN: Attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion. Comput Biol Med 2023; 152:106457. [PMID: 36571937 DOI: 10.1016/j.compbiomed.2022.106457] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/06/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
In this paper, a magnetic resonance imaging (MRI) oriented novel attention-based glioma grading network (AGGN) is proposed. By applying the dual-domain attention mechanism, both channel and spatial information can be considered to assign weights, which benefits highlighting the key modalities and locations in the feature maps. Multi-branch convolution and pooling operations are applied in a multi-scale feature extraction module to separately obtain shallow and deep features on each modality, and a multi-modal information fusion module is adopted to sufficiently merge low-level detailed and high-level semantic features, which promotes the synergistic interaction among different modality information. The proposed AGGN is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the proposed AGGN in comparison to other advanced models, which also presents high generalization ability and strong robustness. In addition, even without the manually labeled tumor masks, AGGN can present considerable performance as other state-of-the-art algorithms, which alleviates the excessive reliance on supervised information in the end-to-end learning paradigm.
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Sun L, Liu C, Wang Y, Bing Z. Substation equipment temperature prediction based on multivariate information fusion and deep learning network. PeerJ Comput Sci 2022; 8:e1172. [PMID: 37346312 PMCID: PMC10280280 DOI: 10.7717/peerj-cs.1172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/07/2022] [Indexed: 06/23/2023]
Abstract
Background Substation equipment temperature is difficult to achieve accurate prediction because of its typical seasonality, periodicity and instability, complex working environment and less available characteristic information. Methods To overcome these difficulties, a substation equipment temperature prediction method is proposed based on multivariate information fusion, convolutional neural network (CNN) and gated recurrent unite (GRU) in this article. Firstly, according to the correlation analysis including linear correlation mapping, autocorrelation function and partial autocorrelation function for substation equipment temperature data, the feature vectors from ambient, time and space are determined, that is the multivariate information fusion feature vector (denoted as MIFFV); secondly, the dimension of MIFFV is reduced by principal component analysis (PCA), extract some of the most important features and form the reduced feature vector (denoted as RFV); then, CNN is used for deep learning to extract the relationship between RFV and the high-dimensional space feature, and construct the high-dimensional feature vector of multivariate time series (denoted as HDFV); finally, the high-dimensional feature vector is used to train GRU deep learning network and predict the equipment temperature. Results A substation equipment in Taizhou City, Zhejiang Province is conducted by the method proposed in this article. Through the comparative experiment from the two aspects of features and methods, under the two prediction performance evaluation indexes of mean absolute percentage error (MAPE) and root mean square error (RSME), two main conclusions are drawn: (1) MIFFV from three aspects of ambient features, time features and space features have better prediction performance than the single feature vector and the combined feature vector of two aspects; (2) compared with other four related models under the same conditions, RFV is regarded as the input of the models, the proposed model has better prediction performance.
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Affiliation(s)
- Lijie Sun
- School of Electronics and Information Engineering, Taizhou University, Taizhou, Zhejiang, China
| | - Chunxue Liu
- School of Information, Liaoning University, Shenyang, Liaoning, China
| | - Ying Wang
- Economic and Technological Research Institute of State Grid Heilongjiang Electric Power Co., Ltd., Haerbin, Heilongjiang, China
| | - Zhaohong Bing
- Computing Technology Institute of East China, Shanghai, China
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Qiu Y, Wang W, Wu C, Zhang Z. A risk factor attention-based model for cardiovascular disease prediction. BMC Bioinformatics 2022; 23:425. [PMID: 36241999 PMCID: PMC9569064 DOI: 10.1186/s12859-022-04963-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 09/19/2022] [Accepted: 09/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient's electronic medical record (EMR) to predict CVD automatically has important application value in intelligent assisted diagnosis and treatment, and is a hot issue in intelligent medical research. However, existing methods based on natural language processing can only predict CVD according to the whole or part of the context information of EMR. RESULTS Given the deficiencies of the existing research on CVD prediction based on EMRs, this paper proposes a risk factor attention-based model (RFAB) to predict CVD by utilizing CVD risk factors and general EMRs text, which adopts the attention mechanism of a deep neural network to fuse the character sequence and CVD risk factors contained in EMRs text. The experimental results show that the proposed method can significantly improve the prediction performance of CVD, and the F-score reaches 0.9586, which outperforms the existing related methods. CONCLUSIONS RFAB focuses on the key information in EMR that leads to CVD, that is, 12 risk factors. In the stage of risk factor identification and extraction, risk factors are labeled with category information and time attribute information by BiLSTM-CRF model. In the stage of CVD prediction, the information contained in risk factors and their labels is fused with the information of character sequence in EMR to predict CVD. RFAB makes well use of the fine-grained information contained in EMR, and also provides a reliable idea for predicting CVD.
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Affiliation(s)
- Yanlong Qiu
- Institute for Quantum Information and State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, 109 Deya Road, Changsha, 410073, People's Republic of China.,College of Computer, National University of Defense Technology, 109 Deya Road, Changsha, 410073, People's Republic of China
| | - Wei Wang
- National Supercomputer Center in Tianjin, 10 Xinhuan West Road, Tianjin, 300457, People's Republic of China
| | - Chengkun Wu
- Institute for Quantum Information and State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, 109 Deya Road, Changsha, 410073, People's Republic of China. .,College of Computer, National University of Defense Technology, 109 Deya Road, Changsha, 410073, People's Republic of China.
| | - Zhichang Zhang
- College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, Lanzhou, 730070, People's Republic of China.
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11
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Peng W, Wu W, Zhang J, Xie H, Zhang S, Gu L. An automatic framework for estimating the pose of the catheter distal section using a coarse-to-fine network. Comput Methods Programs Biomed 2022; 225:107036. [PMID: 35905696 DOI: 10.1016/j.cmpb.2022.107036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 01/27/2022] [Revised: 06/22/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE During percutaneous coronary intervention procedures, generally only 2D X-ray images are provided. The consequent lack of depth perception makes it difficult for interventionists to visually estimate the pose of medical tools inside the vasculature, especially for novices. Although some automatic methods have been developed to aid interventionists, it is still a challenging task to obtain stable and accurate pose estimation. In this paper, we describe a learning-based framework for estimating the pose of the catheter distal section (CDS). The main innovation of this framework is the proposal of a coarse-to-fine fusion network (CFF-Net) which can achieve the shape and orientation estimation for the CDS. METHODS By adopting a two-step fusion, CFF-Net progressively solves the shape and orientation ambiguities. The first step is the early fusion where the 2D projection image fuses with the shape prior before input, which makes the estimated result own a specific catheter distal shape. The second step is the late fusion where CFF-Net fuse feature maps and the orientation data from Electromagnetic (EM) sensors to confirm the overall orientation of the CDS. Finally, the estimated pose in the EM space will be obtained after we combine the estimated shape and orientation from CFF-Net with the position information from the EM sensor. RESULTS The effectiveness of CFF-Net has been verified in a simulated environment where RMSE of CFF-Net is 0.706 ± 0.121 mm. This approach was further transferred from simulation to reality using the real-world data, where RMSE of CFF-Net is 1.121 ± 0.124 mm and RMSE of the whole proposed framework is 1.577 ± 0.144 mm. CONCLUSION In simulated and real-world experiments, our proposed approach has been proven to achieve high accuracy while ensuring real-time processing for estimating the pose of the CDS.
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Affiliation(s)
- Wenjia Peng
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wu
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jingyang Zhang
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Hongzhi Xie
- Department of Cardiology, Peking Union Medical College Hospital, Peking, China.
| | - Shuyang Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Peking, China
| | - Lixu Gu
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
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支 佩, 邓 健, 钟 震. [Medical nucleus image segmentation network based on convolution and attention mechanism]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:730-739. [PMID: 36008337 PMCID: PMC10957366 DOI: 10.7507/1001-5515.202112013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/21/2022] [Indexed: 06/15/2023]
Abstract
Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.
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Affiliation(s)
- 佩佩 支
- 桂林理工大学 信息科学与工程学院(广西桂林 541004)School of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China
| | - 健志 邓
- 桂林理工大学 信息科学与工程学院(广西桂林 541004)School of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China
- 桂林理工大学 广西嵌入式技术与智能系统重点实验室(广西桂林 541004)Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China
| | - 震霄 钟
- 桂林理工大学 信息科学与工程学院(广西桂林 541004)School of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, P. R. China
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13
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Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. Inf Fusion 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
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Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
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14
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Escalona MJ, Koch N, Garcia-Borgoñon L. Lean requirements traceability automation enabled by model-driven engineering. PeerJ Comput Sci 2022; 8:e817. [PMID: 35174261 PMCID: PMC8802773 DOI: 10.7717/peerj-cs.817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the additional costs associated with manual efforts or the use of additional tools. METHODS This article presents a "low-cost" mechanism for automating requirements traceability based on the model-driven paradigm and formalized by a metamodel for the creation and monitoring of traces and an integration process for traceability management. This approach can also be useful for information fusion in industry insofar that it facilitates data traceability. RESULTS This article extends an existing model-driven development methodology to incorporate traceability as part of its development tool. The tool has been used successfully by several companies in real software development projects, helping developers to manage ongoing changes in functional requirements. One of those projects is cited as an example in the paper. The authors' current work leads them to conclude that a model-driven engineering approach, traditionally used only for the automatic generation of code in a software development process, can also be used to successfully automate and integrate traceability management without additional costs. The systematic evaluation of traceability management in industrial projects constitutes a promising area for future work.
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Affiliation(s)
| | - Nora Koch
- University of Seville, Seville, Spain
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15
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Ronaghi F, Salimibeni M, Naderkhani F, Mohammadi A. COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction. Expert Syst Appl 2022; 187:115879. [PMID: 34566272 PMCID: PMC8450050 DOI: 10.1016/j.eswa.2021.115879] [Citation(s) in RCA: 2] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 07/08/2021] [Accepted: 09/04/2021] [Indexed: 06/13/2023]
Abstract
The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.
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Affiliation(s)
- Farnoush Ronaghi
- Concordia Institute for Information Systems Engineering, Concordia University, Canada
| | - Mohammad Salimibeni
- Concordia Institute for Information Systems Engineering, Concordia University, Canada
| | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering, Concordia University, Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Canada
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16
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Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inf Fusion 2022; 77:29-52. [PMID: 34980946 PMCID: PMC8459787 DOI: 10.1016/j.inffus.2021.07.016] [Citation(s) in RCA: 119] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/25/2021] [Accepted: 07/25/2021] [Indexed: 05/04/2023]
Abstract
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.
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Affiliation(s)
- Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
- Imperial Institute of Advanced Technology, Hangzhou, China
| | - Qinghao Ye
- Hangzhou Ocean’s Smart Boya Co., Ltd, China
- University of California, San Diego, La Jolla, CA, USA
| | - Jun Xia
- Radiology Department, Shenzhen Second People’s Hospital, Shenzhen, China
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17
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Gupta A, Singh A. An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques. New Gener Comput 2022; 40:1093-1123. [PMID: 35730007 PMCID: PMC9195408 DOI: 10.1007/s00354-022-00175-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/24/2022] [Indexed: 05/02/2023]
Abstract
Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT-fog-cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptom-related data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work.
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Affiliation(s)
- Aditya Gupta
- Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
| | - Amritpal Singh
- Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
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18
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Ziraki N, Dornaika F, Bosaghzadeh A. Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation. Neural Netw 2021; 146:174-180. [PMID: 34883367 DOI: 10.1016/j.neunet.2021.11.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 06/27/2021] [Revised: 10/04/2021] [Accepted: 11/11/2021] [Indexed: 11/18/2022]
Abstract
Graph construction plays an essential role in graph-based label propagation since graphs give some information on the structure of the data manifold. While most graph construction methods rely on predefined distance calculation, recent algorithms merge the task of label propagation and graph construction in a single process. Moreover, the use of several descriptors is proved to outperform a single descriptor in representing the relation between the nodes. In this article, we propose a Multiple-View Consistent Graph construction and Label propagation algorithm (MVCGL) that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Furthermore, it provides a mapping function from the feature space to the label space with which we estimate the label of unseen samples via a linear projection. The constructed graph does not rely on a predefined similarity function and exploits data and label smoothness. Experiments conducted on three face and one handwritten digit databases show that the proposed method can gain better performance compared to other graph construction and label propagation methods.
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Affiliation(s)
- Najmeh Ziraki
- Shahid Rajaee Teacher Training University, Tehran, Iran.
| | - Fadi Dornaika
- School of Computer and Information Engineering, Henan University, Kaifeng, China; University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
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19
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Tian Y, Sun S, Tang J. Multi-view Teacher-Student Network. Neural Netw 2021; 146:69-84. [PMID: 34839092 DOI: 10.1016/j.neunet.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 05/18/2021] [Revised: 10/27/2021] [Accepted: 11/04/2021] [Indexed: 11/25/2022]
Abstract
Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for performance improvement. Knowledge Distillation (KD), characterized by the so-called "Teacher-Student" (T-S) learning framework, can transfer information learned from one model to another. Inspired by knowledge distillation, we propose a Multi-view Teacher-Student Network (MTS-Net), which combines knowledge distillation and multi-view learning into a unified framework. We first redefine the teacher and student for the multi-view case. Then the MTS-Net is built by optimizing both the view classification loss and the knowledge distillation loss in an end-to-end training manner. We further extend MTS-Net to image recognition tasks and present a multi-view Teacher-Student framework with convolutional neural networks called MTSCNN. To the best of our knowledge, MTS-Net and MTSCNN bring a new insight to extend the Teacher-Student framework to tackle the multi-view learning problem. We theoretically verify the mechanism of MTS-Net and MTSCNN and comprehensive experiments demonstrate the effectiveness of the proposed methods.
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Affiliation(s)
- Yingjie Tian
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| | - Shiding Sun
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Jingjing Tang
- School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China; Institute of Big Data, Southwestern University of Finance and Economics, Chengdu 611130, China.
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20
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Huang L, Lin J, Li X, Song L, Zheng Z, Wong KC. EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information. Brief Bioinform 2021; 23:6425806. [PMID: 34791012 DOI: 10.1093/bib/bbab451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 07/06/2021] [Revised: 09/06/2021] [Accepted: 09/30/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules. RESULTS We evaluated the classification part on 'DDIs 2013' dataset and 'DTIs' dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships. AVAILABILITY Source code are publicly available at https://github.com/Layne-Huang/EGFI.
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Affiliation(s)
- Lei Huang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Jiecong Lin
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, China
| | - Linqi Song
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.,Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
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21
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Maxime DF, Pamela M, Patrick C, Nicolas D. Characterizing interactions between cardiac shape and deformation by non-linear manifold learning. Med Image Anal 2021; 75:102278. [PMID: 34731772 DOI: 10.1016/j.media.2021.102278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 04/26/2021] [Revised: 09/08/2021] [Accepted: 10/18/2021] [Indexed: 10/20/2022]
Abstract
In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.
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Affiliation(s)
- Di Folco Maxime
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France.
| | - Moceri Pamela
- Centre Hospitalier Universitaire de Nice, Service de Cardiologie, Nice, France
| | - Clarysse Patrick
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France
| | - Duchateau Nicolas
- Univ Lyon, UCBL, Inserm, INSA Lyon, CNRS, CREATIS, UMR5220, U1294,Villeurbanne 69621, France
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22
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Basiri ME, Nemati S, Abdar M, Asadi S, Acharrya UR. A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowl Based Syst 2021; 228:107242. [PMID: 36570870 PMCID: PMC9759659 DOI: 10.1016/j.knosys.2021.107242] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 04/30/2021] [Accepted: 06/15/2021] [Indexed: 12/27/2022]
Abstract
Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of all times. The ongoing COVID-19 pandemic has caused more than 150 million infected cases and one million deaths globally as of May 5, 2021. Understanding the sentiment of people expressed in their social media comments can help in monitoring, controlling, and ultimately eradicating the disease. This is a sensitive matter as the threat of infectious disease significantly affects the way people think and behave in various ways. In this study, we proposed a novel method based on the fusion of four deep learning and one classical supervised machine learning model for sentiment analysis of coronavirus-related tweets from eight countries. Also, we analyzed coronavirus-related searches using Google Trends to better understand the change in the sentiment pattern at different times and places. Our findings reveal that the coronavirus attracted the attention of people from different countries at different times in varying intensities. Also, the sentiment in their tweets is correlated to the news and events that occurred in their countries including the number of newly infected cases, number of recoveries and deaths. Moreover, common sentiment patterns can be observed in various countries during the spread of the virus. We believe that different social media platforms have great impact on raising people's awareness about the importance of this disease as well as promoting preventive measures among people in the community.
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Affiliation(s)
- Mohammad Ehsan Basiri
- Department of Computer Engineering, Shahrekord University, Shahrekord, Iran,Corresponding author
| | - Shahla Nemati
- Department of Computer Engineering, Shahrekord University, Shahrekord, Iran
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Somayeh Asadi
- Department of Architectural Engineering, Pennsylvania State University, 104 Engineering Unit A, University Park, PA, 16802, USA
| | - U. Rajendra Acharrya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, Singapore,Department Bioinformatics and Medical Engineering, Asia University, Taiwan,International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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23
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Alkenani AH, Li Y, Xu Y, Zhang Q. Predicting Alzheimer's Disease from Spoken and Written Language Using Fusion-Based Stacked Generalization. J Biomed Inform 2021; 118:103803. [PMID: 33965639 DOI: 10.1016/j.jbi.2021.103803] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 12/09/2020] [Revised: 04/06/2021] [Accepted: 05/03/2021] [Indexed: 11/29/2022]
Abstract
The importance of automating the diagnosis of Alzheimer disease (AD) towards facilitating its early prediction has long been emphasized, hampered in part by lack of empirical support. Given the evident association of AD with age and the increasing aging population owing to the general well-being of individuals, there have been unprecedented estimated economic complications. Consequently, many recent studies have attempted to employ the language deficiency caused by cognitive decline in automating the diagnostic task via training machine learning (ML) algorithms with linguistic patterns and deficits. In this study, we aim to develop multiple heterogeneous stacked fusion models that harness the advantages of several base learning algorithms to improve the overall generalizability and robustness of AD diagnostic ML models, where we parallelly utilized two different written and spoken-based datasets to train our stacked fusion models. Further, we examined the effect of linking these two datasets to develop a hybrid stacked fusion model that can predict AD from written and spoken languages. Our feature spaces involved two widely used linguistic patterns: lexicosyntactics and character n-gram spaces. We firstly investigated lexicosyntactics of AD alongside healthy controls (HC), where we explored a few new lexicosyntactic features, then optimized the lexicosyntactic feature space by proposing a correlation feature selection technique that eliminates features based on their feature-feature inter-correlations and feature-target correlations according to a certain threshold. Our stacked fusion models establish benchmarks on both datasets with AUC of 98.1% and 99.47% for the spoken and written-based datasets, respectively, and corresponding accuracy and F1 score values around 95% on spoken-based dataset and around 97% on the written-based dataset. Likewise, the hybrid stacked fusion model on linked data presents an optimal performance with 99.2% AUC as well as accuracy and F1 score falling around 97%. In view of the achieved performance and enhanced generalizability of such fusion models over single classifiers, this study suggests replacing the initial traditional screening test with such models that can be embedded into an online format for a fully automated remote diagnosis.
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Affiliation(s)
- Ahmed H Alkenani
- School of Computer Science, Queensland University of Technology, Brisbane 4001, Australia; The Australian e-Health Research Centre, CSIRO, Brisbane 4029, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane 4001, Australia.
| | - Yue Xu
- School of Computer Science, Queensland University of Technology, Brisbane 4001, Australia
| | - Qing Zhang
- The Australian e-Health Research Centre, CSIRO, Brisbane 4029, Australia
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Makarov I, Makarov M, Kiselev D. Fusion of text and graph information for machine learning problems on networks. PeerJ Comput Sci 2021; 7:e526. [PMID: 34084929 PMCID: PMC8157042 DOI: 10.7717/peerj-cs.526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/14/2021] [Indexed: 06/12/2023]
Abstract
Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.
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Affiliation(s)
- Ilya Makarov
- HSE University, Moscow, Russia
- University of Ljubljana, Ljubljana, Slovenia
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25
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Yan Y, Conze PH, Lamard M, Quellec G, Cochener B, Coatrieux G. Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal 2021; 71:102083. [PMID: 33979759 DOI: 10.1016/j.media.2021.102083] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 03/23/2020] [Revised: 02/18/2021] [Accepted: 04/14/2021] [Indexed: 11/18/2022]
Abstract
Breast cancer screening benefits from the visual analysis of multiple views of routine mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be enhanced by integrating multi-view information. In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and classification, towards better detection performance. Specifically, we propose a unified Siamese network that combines patch-level mass/non-mass classification and dual-view mass matching to take full advantage of multi-view information. This model is exploited in a full image detection pipeline based on You-Only-Look-Once (YOLO) region proposals. We carry out exhaustive experiments to highlight the contribution of dual-view matching for both patch-level classification and examination-level detection scenarios. Results demonstrate that mass matching highly improves the full-pipeline detection performance by outperforming conventional single-task schemes with 94.78% as Area Under the Curve (AUC) score and a classification accuracy of 0.8791. Interestingly, mass classification also improves the performance of mass matching, which proves the complementarity of both tasks. Our method further guides clinicians by providing accurate dual-view mass correspondences, which suggests that it could act as a relevant second opinion for mammogram interpretation and breast cancer diagnosis.
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Affiliation(s)
- Yutong Yan
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
| | - Pierre-Henri Conze
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France.
| | - Mathieu Lamard
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France
| | - Gwenolé Quellec
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France
| | - Béatrice Cochener
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; CHRU de Brest, 2 avenue Foch, Brest 29200, France
| | - Gouenou Coatrieux
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
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26
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Chen S, Xu K, Yao X, Zhu S, Zhang B, Zhou H, Guo X, Zhao B. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Comput Biol Med 2021; 133:104413. [PMID: 33915363 DOI: 10.1016/j.compbiomed.2021.104413] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 01/17/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 12/11/2022]
Abstract
Fatigue-induced human error is a leading cause of accidents. The purpose of this exploratory study in China was to perform field tests to measure fatigue psychophysiological parameters, such as electrocardiography (ECG), electromyography (EMG), pulse, blood pressure, reaction time and vital capacity (VC), in miners in high-altitude and cold areas and to perform multi-feature information fusion and fatigue identification. Forty-five miners were randomly selected as subjects for a field test, and feature signals were extracted from 90 psychophysiological features as basic signals for fatigue analysis. Fatigue sensitivity indices were obtained by Pearson correlation analysis, t-test and receiver operating characteristic (ROC) curve performance evaluation. The ECG time-domain, ECG frequency-domain, EMG, VC, systolic blood pressure (SBP), and pulse were significantly different after miner fatigue. The support vector machine (SVM) and random forest (RF) techniques were used to classify and identify fatigue by information fusion and factor combination. The optimal fatigue classification factors were ECG-FD (CV Accuracy = 85.0%) and EMG (CV Accuracy = 90.0%). The optimal combination of factors was ECG-TD + ECG-FD + EMG (CV accuracy = 80.0%). Furthermore, SVM machine learning had a good recognition effect. This study shows that SVM and RF can effectively identify miner fatigue based on fatigue-related factor combinations. ECG-FD and EMG are the best indicators of fatigue, and the best performance and robustness are obtained with three-factor combination classification. This study on miner fatigue identification provides a reference for research on clinical medicine and the identification of human fatigue under high-altitude, cold and low-oxygen conditions.
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Affiliation(s)
- Shoukun Chen
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Kaili Xu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Xiwen Yao
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Siyi Zhu
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Bohan Zhang
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Haodong Zhou
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Xin Guo
- Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China.
| | - Bingfeng Zhao
- Yunnan Diqing Non-ferrous Metals Co., Ltd, Yunnan, 674400, China.
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Wang C, Yang G, Papanastasiou G, Tsaftaris SA, Newby DE, Gray C, Macnaught G, MacGillivray TJ. DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. Inf Fusion 2021; 67:147-160. [PMID: 33658909 PMCID: PMC7763495 DOI: 10.1016/j.inffus.2020.10.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 05/22/2023]
Abstract
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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Affiliation(s)
- Chengjia Wang
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Corresponding author.
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Sotirios A. Tsaftaris
- Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, UK
| | - David E. Newby
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Calum Gray
- Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
| | - Gillian Macnaught
- Edinburgh Imaging Facility QMRI, University of Edinburgh, Edinburgh, UK
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Thakkar A, Chaudhari K. Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions. Inf Fusion 2021; 65:95-107. [PMID: 32868979 PMCID: PMC7448965 DOI: 10.1016/j.inffus.2020.08.019] [Citation(s) in RCA: 18] [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] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/27/2020] [Accepted: 08/20/2020] [Indexed: 06/11/2023]
Abstract
Investment in a financial market is aimed at getting higher benefits; this complex market is influenced by a large number of events wherein the prediction of future market dynamics is challenging. The investors' etiquettes towards stock market may demand the need of studying various associated factors and extract the useful information for reliable forecasting. Fusion can be considered as an approach to integrate data or characteristics, in general, and enhance the prediction based on the combinational approach that can aid each other. We conduct a systematic approach to present a survey for the years 2011-2020 by considering articles that have used fusion techniques for various stock market applications and broadly categorize them into information fusion, feature fusion, and model fusion. The major applications of stock market include stock price and trend prediction, risk analysis and return forecasting, index prediction, as well as portfolio management. We also provide an infographic overview of fusion in stock market prediction and extend our survey for other finely addressed financial prediction problems. Based on our surveyed articles, we provide potential future directions and concluding remarks on the significance of applying fusion in stock market.
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Affiliation(s)
- Ankit Thakkar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382 481, Gujarat, India
| | - Kinjal Chaudhari
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382 481, Gujarat, India
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Yao S, Qin H, Wang Q, Lu Z, Yao X, Yu Z, Chen X, Zhang L, Lu J. Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies. Spectrochim Acta A Mol Biomol Spectrosc 2020; 239:118492. [PMID: 32470810 DOI: 10.1016/j.saa.2020.118492] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/13/2020] [Accepted: 05/13/2020] [Indexed: 06/11/2023]
Abstract
Coal properties have different correlations with elements or molecules. It is difficult to optimize the analysis of multiple coal properties simultaneously by a single analytical technique. This paper reports a method for optimizing analysis of coal properties by using laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS). Briefly, LIBS, NIRS, as well as spectral information fusion of LIBS and NIRS (LIBS&NIRS) were used to establish the quantitative analysis models of coal properties with partial least squares (PLS) method. The performance of models based on different spectral information was compared with each other according to the determination coefficient (R2), root mean square error of prediction (RMSEP), average absolute error (AAE), and average relative error (ARE). As a result, the models of calorific value and volatile matter based on LIBS&NIRS have the best performance with minimum root mean square error for prediction (RMSEP) of 0.192 MJ/kg and 0.672%. However, for the model of ash content, the minimum RMSEP of 0.774% was achieved by using LIBS. Meanwhile, optimal performance of modeling moisture content was obtained from NIRS with the minimum RMSEP of 0.308%. After obtaining the best prediction results of volatile matter content, ash content, and moisture content, the fixed carbon content can be calculated by the definition formula. These results demonstrated that the reported method can optimize the rapid analysis of multiple coal properties simultaneously.
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Affiliation(s)
- Shunchun Yao
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China.
| | - Huaiqing Qin
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Qi Wang
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
| | - Zhimin Lu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Xiayang Yao
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Ziyu Yu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Xiaoxuan Chen
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Lifeng Zhang
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
| | - Jidong Lu
- School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China; Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou, Guangdong 510640, China
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30
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Xu J, Jia B, Pan X, Li R, Cao L, Cui C, Wang H, Li B. Hydrographic data inspection and disaster monitoring using shipborne radar small range images with electronic navigation chart. PeerJ Comput Sci 2020; 6:e290. [PMID: 33816941 PMCID: PMC7924651 DOI: 10.7717/peerj-cs.290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 07/15/2020] [Indexed: 06/12/2023]
Abstract
Shipborne radars cannot only enable navigation and collision avoidance but also play an important role in the fields of hydrographic data inspection and disaster monitoring. In this paper, target extraction methods for oil films, ships and coastlines from original shipborne radar images are proposed. First, the shipborne radar video images are acquired by a signal acquisition card. Second, based on remote sensing image processing technology, the radar images are preprocessed, and the contours of the targets are extracted. Then, the targets identified in the radar images are integrated into an electronic navigation chart (ENC) by a geographic information system. The experiments show that the proposed target segmentation methods of shipborne radar images are effective. Using the geometric feature information of the targets identified in the shipborne radar images, information matching between radar images and ENC can be realized for hydrographic data inspection and disaster monitoring.
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Affiliation(s)
- Jin Xu
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
- Navigation College, Dalian Martime University, Dalian, Liaoning, China
| | - Baozhu Jia
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Xinxiang Pan
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
- Marine Engineering College, Dalian Maritime University, Dalian, Liaoning, China
| | - Ronghui Li
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Liang Cao
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
| | - Can Cui
- Civil Aviation College, Shenyang Aerospace University, Shenyang, Liaoning, China
| | - Haixia Wang
- Navigation College, Dalian Martime University, Dalian, Liaoning, China
| | - Bo Li
- Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China
- Laboratory Department, Liaoning Hydrogeology and Engineering Geology Reconnaissance Institute, Dalian, China
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Sedghi A, Mehrtash A, Jamzad A, Amalou A, Wells WM, Kapur T, Kwak JT, Turkbey B, Choyke P, Pinto P, Wood B, Xu S, Abolmaesumi P, Mousavi P. Improving detection of prostate cancer foci via information fusion of MRI and temporal enhanced ultrasound. Int J Comput Assist Radiol Surg 2020; 15:1215-1223. [PMID: 32372384 DOI: 10.1007/s11548-020-02172-5] [Citation(s) in RCA: 16] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 04/16/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE The detection of clinically significant prostate cancer (PCa) is shown to greatly benefit from MRI-ultrasound fusion biopsy, which involves overlaying pre-biopsy MRI volumes (or targets) with real-time ultrasound images. In previous literature, machine learning models trained on either MRI or ultrasound data have been proposed to improve biopsy guidance and PCa detection. However, quantitative fusion of information from MRI and ultrasound has not been explored in depth in a large study. This paper investigates information fusion approaches between MRI and ultrasound to improve targeting of PCa foci in biopsies. METHODS We build models of fully convolutional networks (FCN) using data from a newly proposed ultrasound modality, temporal enhanced ultrasound (TeUS), and apparent diffusion coefficient (ADC) from 107 patients with 145 biopsy cores. The architecture of our models is based on U-Net and U-Net with attention gates. Models are built using joint training through intermediate and late fusion of the data. We also build models with data from each modality, separately, to use as baseline. The performance is evaluated based on the area under the curve (AUC) for predicting clinically significant PCa. RESULTS Using our proposed deep learning framework and intermediate fusion, integration of TeUS and ADC outperforms the individual modalities for cancer detection. We achieve an AUC of 0.76 for detection of all PCa foci, and 0.89 for PCa with larger foci. Results indicate a shared representation between multiple modalities outperforms the average unimodal predictions. CONCLUSION We demonstrate the significant potential of multimodality integration of information from MRI and TeUS to improve PCa detection, which is essential for accurate targeting of cancer foci during biopsy. By using FCNs as the architecture of choice, we are able to predict the presence of clinically significant PCa in entire imaging planes immediately, without the need for region-based analysis. This reduces the overall computational time and enables future intra-operative deployment of this technology.
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Affiliation(s)
| | - Alireza Mehrtash
- The University of British Columbia, Vancouver, BC, Canada.,Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Amel Amalou
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - William M Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Baris Turkbey
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Peter Choyke
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Peter Pinto
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Bradford Wood
- The National Institutes of Health Research Center, Baltimore, MD, USA
| | - Sheng Xu
- The National Institutes of Health Research Center, Baltimore, MD, USA
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32
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Xu Q, Wang Z, Zhen Z. Information fusion estimation-based path following control of quadrotor UAVs subjected to Gaussian random disturbance. ISA Trans 2020; 99:84-94. [PMID: 31629487 DOI: 10.1016/j.isatra.2019.10.003] [Citation(s) in RCA: 7] [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] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 09/23/2019] [Accepted: 10/04/2019] [Indexed: 06/10/2023]
Abstract
Random disturbance has a detrimental effect on the reliability and safety of quadrotor unmanned aerial vehicles (UAVs). This paper proposes an anti-Gaussian random disturbance control method for the path following of a quadrotor UAV. The quadrotor system is linearized and divided into two subsystems, i.e., a translational subsystem and a rotational subsystem, and hierarchical strategy is used to design the overall control architecture. In order to suppress the negative effects of Gaussian random disturbances and simplify the design process of linear-quadratic optimal output tracking control problem, a new information fusion estimation based robust control named Gaussian information fusion control (GIFC) scheme is proposed. The convergence of the output tracking errors of GIFC system is proved via Lyapunov theory. The proposed GIFC control scheme is employed for position and attitude controller designs to enhance the robustness of the quadrotor system to Gaussian random perturbations. Finally numerical simulation experiments illustrate the effectiveness and robustness of the proposed control strategy.
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Affiliation(s)
- Qingzheng Xu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
| | - Zhisheng Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
| | - Ziyang Zhen
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
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33
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Xu X, Weng X, Xu D, Xu H, Hu Y, Li J. Evidence updating with static and dynamical performance analyses for industrial alarm system design. ISA Trans 2020; 99:110-122. [PMID: 31522822 DOI: 10.1016/j.isatra.2019.09.006] [Citation(s) in RCA: 7] [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] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 06/10/2023]
Abstract
In the Dempster-Shafer theory (DST) of evidence, the alarm evidence updating-based method can effectively deal with the uncertainty of the monitored process variable so as to significantly reduce the false alarm rates (FAR) and missed alarm rates (MAR) of the industrial alarm system. But the price of the decrease of FAR and MAR is the increase of the averaged alarm delay (AAD). In order to obtain better comprehensive performance, besides the accuracy indices (FAR and MAR), the sensitivity index (AAD) should be considered simultaneously in the alarm system parameter optimization design. In the framework of DST, firstly, this paper defines the static and dynamical performance indices in the alarm evidence space which are compatible with FAR/MAR/AAD in the process variable space. But the former can measure the performance of the DST-based alarm systems more naturally and elaborately than the latter; secondly, a systematic parameter optimization design procedure for the alarm system is investigated by using these new indices and the tradeoff among them. Finally, two typical numerical experiments and an industrial case are provided to illustrate the effectiveness of the static and dynamical indices for improving the comprehensive performance of the DST-based alarm systems.
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Affiliation(s)
- Xiaobin Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou 100084, China
| | - Xu Weng
- School of Automation, Hangzhou Dianzi University, Hangzhou 100084, China
| | - Dongling Xu
- Manchester Business School, The University of Manchester, Manchester M15 6PB, United Kingdom.
| | - Haiyang Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou 100084, China
| | - Yanzhu Hu
- School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jianning Li
- School of Automation, Hangzhou Dianzi University, Hangzhou 100084, China
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Sun Y, Wang L, Jiang Z, Li B, Hu Y, Tian W. State recognition of decompressive laminectomy with multiple information in robot-assisted surgery. Artif Intell Med 2019; 102:101763. [PMID: 31980100 DOI: 10.1016/j.artmed.2019.101763] [Citation(s) in RCA: 7] [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: 08/29/2018] [Revised: 11/04/2019] [Accepted: 11/10/2019] [Indexed: 10/25/2022]
Abstract
The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery. For the fenestration, we propose an image-based state recognition method that consists a U-Net derived network, grayscale redistribution and dynamic receptive field assisting in controlling the grinding process to prevent the grinding-bit from crossing the inner edge of the lamina to damage the spinal nerves. For the internal fixation, we propose an audio and force-based state recognition method that consists signal features extraction methods, LSTM-based prediction and information fusion assisting in monitoring the drilling process to prevent the drilling-bit from crossing the outer edge of the vertebral pedicle to damage the spinal nerves. Several experiments are conducted to show the reliability of the proposed system in robot-assisted surgery.
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Affiliation(s)
- Yu Sun
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China; Harbin Institute of Technology (Shenzhen), University Town of Shenzhen, Shenzhen, 518055, China.
| | - Li Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China.
| | | | - Bing Li
- Harbin Institute of Technology (Shenzhen), University Town of Shenzhen, Shenzhen, 518055, China.
| | - Ying Hu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China; SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China.
| | - Wei Tian
- Beijing Jishuitan Hospital, Beijing, 100035, China.
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Chan YM, Ng EYK, Jahmunah V, Wei Koh JE, Lih OS, Wei Leon LY, Acharya UR. Automated detection of glaucoma using optical coherence tomography angiogram images. Comput Biol Med 2019; 115:103483. [PMID: 31698235 DOI: 10.1016/j.compbiomed.2019.103483] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 09/25/2019] [Accepted: 10/03/2019] [Indexed: 11/24/2022]
Abstract
Glaucoma is a malady that occurs due to the buildup of fluid pressure in the inner eye. Detection of glaucoma at an early stage is crucial as by 2040, 111.8 million people are expected to be afflicted with glaucoma globally. Feature extraction methods prove to be promising in the diagnosis of glaucoma. In this study, we have used optical coherence tomography angiogram (OCTA) images for automated glaucoma detection. Ocular sinister (OS) from the left eye while ocular dexter (OD) were obtained from right eye of subjects. We have used OS macular, OS disc, OD macular and OD disc images. In this work, local phase quantization (LPQ) technique was applied to extract the features. Information fusion and principal component analysis (PCA) are used to combine and reduce the features. Our method achieved the highest accuracy of 94.3% using LPQ coupled with PCA for right eye optic disc images with AdaBoost classifier. The proposed technique can aid clinicians in glaucoma detection at an early stage. The developed model is ready to be tested with more images before deploying for clinical application.
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Kang Z, Zhao X, Peng C, Zhu H, Zhou JT, Peng X, Chen W, Xu Z. Partition level multiview subspace clustering. Neural Netw 2019; 122:279-288. [PMID: 31731045 DOI: 10.1016/j.neunet.2019.10.010] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [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: 07/07/2019] [Revised: 09/17/2019] [Accepted: 10/14/2019] [Indexed: 10/25/2022]
Abstract
Multiview clustering has gained increasing attention recently due to its ability to deal with multiple sources (views) data and explore complementary information between different views. Among various methods, multiview subspace clustering methods provide encouraging performance. They mainly integrate the multiview information in the space where the data points lie. Hence, their performance may be deteriorated because of noises existing in each individual view or inconsistent between heterogeneous features. For multiview clustering, the basic premise is that there exists a shared partition among all views. Therefore, the natural space for multiview clustering should be all partitions. Orthogonal to existing methods, we propose to fuse multiview information in partition level following two intuitive assumptions: (i) each partition is a perturbation of the consensus clustering; (ii) the partition that is close to the consensus clustering should be assigned a large weight. Finally, we propose a unified multiview subspace clustering model which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition. These three components are seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution. Experiments on four benchmark datasets demonstrate the efficacy of our approach against the state-of-the-art techniques.
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Affiliation(s)
- Zhao Kang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China
| | - Xinjia Zhao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China
| | - Chong Peng
- College of Computer Science and Technology, Qingdao University, China
| | - Hongyuan Zhu
- Institute for Infocomm Research, A*STAR, Singapore
| | | | - Xi Peng
- College of Computer Science, Sichuan University, China
| | - Wenyu Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China.
| | - Zenglin Xu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China; Centre for Artificial Intelligence, Peng Cheng Lab, Shenzhen 518055, China.
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Wang H, Chen M, Li J, Chen N, Chang Y, Dou Z, Zhang Y, Zhuang P, Yang Z. Quality consistency evaluation of Kudiezi Injection based on multivariate statistical analysis of the multidimensional chromatographic fingerprint. J Pharm Biomed Anal 2019; 177:112868. [PMID: 31539713 DOI: 10.1016/j.jpba.2019.112868] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [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: 06/13/2019] [Revised: 08/29/2019] [Accepted: 09/06/2019] [Indexed: 12/17/2022]
Abstract
Traditional Chinese Medicine Injection (TCMI) was restricted due to the batch-to-batch variability caused by the variable compositions of botanical raw materials and complexities of the current manufacturing process. To evaluate and control the quality of Kudiezi Injection (KDZI), a comprehensive and practical method based on multidimensional chromatographic fingerprint associated with multivariate statistical analysis was proposed. The multidimensional chromatographic fingerprint was established by integrating three kinds of chromatographic fingerprints, including High Performance Liquid Chromatography-Ultraviolet spectrum (HPLC-UV), Gas Chromatography-Mass Spectrometer (GC-MS) and High performance ion-exchange chromatography (HPIEC), which were used to detect flavones, nucleosides, organic acids, amino acids and saccharides in KDZI. In addition, four main multivariate statistical analyses were compared to assess the batch-to-batch consistency of samples. Results showed that the cosine method, which has been widely used in the quality evaluation of TCM, failed to distinguish the differences among batches based on neither chromatographic peaks' area nor contents information. t-test and Bayes' theorem could reveal the content difference among batches, while hierarchical clustering analysis could differentiate KDZI batches, and Luteolin-7-O-β-D-glucuronopyranoside, Tau, Ser, guanine and allose were the main indicators. In conclusion, multidimensional chromatographic fingerprints could reflect the quality information of KDZI comprehensively and hierarchical clustering analysis was suitable to identify the differences among batches. This could provide an integrated method for consistency evaluation of TCMI, process improvement of TCMI and solving similar problems in TCMI.
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Affiliation(s)
- Hui Wang
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Meiling Chen
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Jie Li
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Ning Chen
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Yanxu Chang
- Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Zhiying Dou
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Yanjun Zhang
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Pengwei Zhuang
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Zhen Yang
- Tianjin Key Laboratory of Chinese medicine Pharmacology, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Li Y. Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making. Environ Sci Pollut Res Int 2019; 26:17866-17874. [PMID: 28547373 DOI: 10.1007/s11356-017-9100-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 12/20/2016] [Accepted: 04/24/2017] [Indexed: 06/07/2023]
Abstract
The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker's risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.
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Affiliation(s)
- Yan Li
- School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, China.
- Department of Economics, Huai-hua College, Huai-hua, China.
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Naddeo A, Califano R, Vallone M, Cicalese A, Coccaro C, Marcone F, Shullazi E. The effect of spine discomfort on the overall postural (dis)comfort. Appl Ergon 2019; 74:194-205. [PMID: 30487100 DOI: 10.1016/j.apergo.2018.08.025] [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] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 06/11/2018] [Accepted: 08/29/2018] [Indexed: 06/09/2023]
Abstract
Currently, the word 'comfort' is often used in relation to the marketing of products such as chairs, cars interiors, clothing, hand tools and even airplane tickets. In this field of research, the aim of this study is to investigate the influence of spinal posture on postural (dis)comfort perception; the test case is the analysis of the interaction between humans and vending machines for purchasing food or beverages. A statistical sample of 20 healthy students (subjects) performed the required tests, with each participant asked to take a product from three different vending machines (snacks, drinks and coffee). The subjects' postures were acquired non-invasively using cameras; software and instruments for virtual prototyping were used for posture analysis and interaction modelling, both questionnaires (subjective) and comfort-analysis software (objective) were used to rate the perceived (dis)comfort. The results obtained from simulations and questionnaires were compared, and a method to weigh the effect of the perceived spinal discomfort on overall postural (dis)comfort was proposed. These results reveal a good correlation between subjective perception and objective evaluation obtained through simulations, confirming the validity of the proposed method.
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40
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Jiang W, Zhuang M, Qin X, Tang Y. Conflicting evidence combination based on uncertainty measure and distance of evidence. Springerplus 2016; 5:1217. [PMID: 27516955 PMCID: PMC4967071 DOI: 10.1186/s40064-016-2863-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 07/19/2016] [Indexed: 11/10/2022]
Abstract
Dempster-Shafer evidence theory is widely used in many fields of information fusion. However, the counter-intuitive results may be obtained when combining with highly conflicting evidence. To deal with such a problem, we put forward a new method based on the distance of evidence and the uncertainty measure. First, based on the distance of evidence, the evidence is divided into two parts, the credible evidence and the incredible evidence. Then, a novel belief entropy is applied to measure the information volume of the evidence. Finally, the weight of each evidence is obtained and used to modify the evidence before using the Dempster's combination rule. Numerical examples show that the proposed method can effectively handle conflicting evidence with better convergence.
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Affiliation(s)
- Wen Jiang
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072 Shaanxi China
| | - Miaoyan Zhuang
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072 Shaanxi China
| | - Xiyun Qin
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072 Shaanxi China
| | - Yongchuan Tang
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072 Shaanxi China
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41
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Wang J, Hu Y, Xiao F, Deng X, Deng Y. A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis. Artif Intell Med 2016; 69:1-11. [PMID: 27235800 DOI: 10.1016/j.artmed.2016.04.004] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [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: 12/06/2015] [Revised: 04/23/2016] [Accepted: 04/23/2016] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In this study, we improve the performance of reducing uncertainty and raising the choice decision level in fuzzy soft set-based decision making. METHODS AND MATERIAL We make use of two appropriate tools (ambiguity measure and Dempster-Shafer theory of evidence) to improve fuzzy soft set-based decision making. Our proposed approach consists of three procedures: primarily, the uncertainty degree of each parameter is obtained by using ambiguity measure; next, the suitable basic probability assignment with respect to each parameter (or evidence) is constructed based on the uncertainty degree of each parameter obtained in the first step; in the end, the classical Dempster's combination rule is applied to aggregate independent evidences into the collective evidence, by which the candidate alternatives are ranked and the best alternative will be obtained. RESULTS We compare the results of our proposed method with the recent relative works. Through employing our presented approach, in Example 5, the belief measure of the uncertainty falls to 0.0051 from 0.0751; in Example 6, the belief measure of the uncertainty drops to 0.0086 from 0.0547; in Example 7, the belief measure of the uncertainty falls to 0.0847 from 0.1647; in application, the belief measure of the uncertainty drops 0.0001 from 0.0069. CONCLUSION Three numerical examples and an application in medical diagnosis are provided to demonstrate adequately that, on the one hand, our proposed method is feasible and efficient; on the other hand, our proposed method can reduce uncertainty caused by people's subjective cognition and raise the choice decision level with the best performance.
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Affiliation(s)
- Jianwei Wang
- School of Computer and Information Science, Southwest University, Chongqing 400715, China; School of HanHong, Southwest University, Chongqing 400715, China
| | - Yong Hu
- Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China
| | - Fuyuan Xiao
- School of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Xinyang Deng
- School of Computer and Information Science, Southwest University, Chongqing 400715, China; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Vanderbilt University, Nashville, TN 37235, USA
| | - Yong Deng
- School of Computer and Information Science, Southwest University, Chongqing 400715, China; Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China; Institute of Integrated Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xian, Shaanxi 710049, China.
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42
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Şener E, Mumcuoglu EU, Hamcan S. Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling. Comput Methods Programs Biomed 2016; 124:31-44. [PMID: 26574298 DOI: 10.1016/j.cmpb.2015.10.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [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: 03/11/2015] [Revised: 10/06/2015] [Accepted: 10/14/2015] [Indexed: 06/05/2023]
Abstract
BACKGROUND Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. OBJECTIVES In this study, a Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. METHODS The method presented incorporates information fusion from multiple modalities, modelling of image resolution (measurement blurring), image noise, two priors helping to reduce noise and partial volume. Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation. Regularization based on isotropic and directional Markov Random Field priors is integrated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image. Sub-methods as variations of the main method are generated by using a combination of the models. RESULTS Testing of the sub-methods is performed on two patients using single modality three-dimensional (3D) image (magnetic resonance, MR or computerized tomography, CT) as well as registered MR-CT images with information fusion. Numerical, visual and statistical analyses of the methods are conducted. High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images. The methods are also compared with our Bayesian segmentation method proposed in a previous study. The performance is found to be similar to our previous Bayesian approach, but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent. CONCLUSIONS The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head. Meanwhile, free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more systematic way as compared with our previous study.
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Affiliation(s)
- Emre Şener
- Department of Engineering Sciences, Middle East Technical University, Ankara, Turkey.
| | - Erkan U Mumcuoglu
- Health Informatics Department, Informatics Institute, Middle East Technical University, Ankara, Turkey.
| | - Salih Hamcan
- Department of Radiology, Gulhane Military Medical School, Ankara, Turkey.
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Azaza L, Kirgizov S, Savonnet M, Leclercq É, Gastineau N, Faiz R. Information fusion-based approach for studying influence on Twitter using belief theory. Comput Soc Netw 2016; 3:5. [PMID: 29355230 DOI: 10.1186/s40649-016-0030-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 08/11/2016] [Indexed: 11/23/2022]
Abstract
Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter. We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.
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Meng J, Li R, Luan Y. Classification by integrating plant stress response gene expression data with biological knowledge. Math Biosci 2015; 266:65-72. [PMID: 26092610 DOI: 10.1016/j.mbs.2015.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [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: 03/24/2015] [Revised: 05/03/2015] [Accepted: 06/05/2015] [Indexed: 12/01/2022]
Abstract
Classification of microarray data has always been a challenging task because of the enormous number of genes. In this study, a clustering method by integrating plant stress response gene expression data with biological knowledge is presented. Clustering is one of the promising tools for attribute reduction, but gene clusters are biologically uninformative. So we integrated biological knowledge into genomic analysis to help to improve the interpretation of the results. Biological similarity based on gene ontology (GO) semantic similarity was combined with gene expression data to find out biologically meaningful clusters. Affinity propagation clustering algorithm was chosen to analyze the impact of the biological similarity on the results. Based on clustering result, neighborhood rough set was used to select representative genes for each cluster. The prediction accuracy of classifiers built on reduced gene subsets indicated that our approach outperformed other classical methods. The information fusion was proven to be effective through quantitative analysis, as it could select gene subsets with high biological significance and select significant genes.
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Affiliation(s)
- Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China..
| | - Rui Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China..
| | - Yushi Luan
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, Liaoning 116023, China..
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Zhen Z, Jiang J, Wang X, Gao C. Information fusion based optimal control for large civil aircraft system. ISA Trans 2015; 55:81-91. [PMID: 25440950 DOI: 10.1016/j.isatra.2014.09.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 09/04/2014] [Accepted: 09/20/2014] [Indexed: 06/04/2023]
Abstract
Wind disturbance has a great influence on landing security of Large Civil Aircraft. Through simulation research and engineering experience, it can be found that PID control is not good enough to solve the problem of restraining the wind disturbance. This paper focuses on anti-wind attitude control for Large Civil Aircraft in landing phase. In order to improve the riding comfort and the flight security, an information fusion based optimal control strategy is presented to restrain the wind in landing phase for maintaining attitudes and airspeed. Data of Boeing707 is used to establish a nonlinear mode with total variables of Large Civil Aircraft, and then two linear models are obtained which are divided into longitudinal and lateral equations. Based on engineering experience, the longitudinal channel adopts PID control and C inner control to keep longitudinal attitude constant, and applies autothrottle system for keeping airspeed constant, while an information fusion based optimal regulator in the lateral control channel is designed to achieve lateral attitude holding. According to information fusion estimation, by fusing hard constraint information of system dynamic equations and the soft constraint information of performance index function, optimal estimation of the control sequence is derived. Based on this, an information fusion state regulator is deduced for discrete time linear system with disturbance. The simulation results of nonlinear model of aircraft indicate that the information fusion optimal control is better than traditional PID control, LQR control and LQR control with integral action, in anti-wind disturbance performance in the landing phase.
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Affiliation(s)
- Ziyang Zhen
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Yudao Street, Nanjing, China.
| | - Ju Jiang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Yudao Street, Nanjing, China
| | - Xinhua Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Yudao Street, Nanjing, China
| | - Chen Gao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Yudao Street, Nanjing, China
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Lelandais B, Ruan S, Denœux T, Vera P, Gardin I. Fusion of multi-tracer PET images for dose painting. Med Image Anal 2014; 18:1247-59. [PMID: 25128684 DOI: 10.1016/j.media.2014.06.014] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [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: 02/02/2014] [Revised: 05/25/2014] [Accepted: 06/28/2014] [Indexed: 11/19/2022]
Abstract
PET imaging with FluoroDesoxyGlucose (FDG) tracer is clinically used for the definition of Biological Target Volumes (BTVs) for radiotherapy. Recently, new tracers, such as FLuoroThymidine (FLT) or FluoroMisonidazol (FMiso), have been proposed. They provide complementary information for the definition of BTVs. Our work is to fuse multi-tracer PET images to obtain a good BTV definition and to help the radiation oncologist in dose painting. Due to the noise and the partial volume effect leading, respectively, to the presence of uncertainty and imprecision in PET images, the segmentation and the fusion of PET images is difficult. In this paper, a framework based on Belief Function Theory (BFT) is proposed for the segmentation of BTV from multi-tracer PET images. The first step is based on an extension of the Evidential C-Means (ECM) algorithm, taking advantage of neighboring voxels for dealing with uncertainty and imprecision in each mono-tracer PET image. Then, imprecision and uncertainty are, respectively, reduced using prior knowledge related to defects in the acquisition system and neighborhood information. Finally, a multi-tracer PET image fusion is performed. The results are represented by a set of parametric maps that provide important information for dose painting. The performances are evaluated on PET phantoms and patient data with lung cancer. Quantitative results show good performance of our method compared with other methods.
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Affiliation(s)
| | - Su Ruan
- QuantIF, LITIS EA 4108, University of Rouen, France
| | - Thierry Denœux
- Heudiasyc (UMR 7253), Université de Technologie de Compiègne, CNRS, Compiègne, France
| | - Pierre Vera
- Department of Nuclear medicine, Henri Becquerel Center, France
| | - Isabelle Gardin
- Department of Nuclear medicine, Henri Becquerel Center, France
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Pham TD, Le DTP, Xu J, Nguyen DT, Martindale RG, Deveney CW. Personalized identification of abdominal wall hernia meshes on computed tomography. Comput Methods Programs Biomed 2013; 113:153-161. [PMID: 24184112 DOI: 10.1016/j.cmpb.2013.09.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [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: 06/05/2012] [Revised: 09/05/2013] [Accepted: 09/23/2013] [Indexed: 06/02/2023]
Abstract
An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.
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Affiliation(s)
- Tuan D Pham
- Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
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48
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Vogt F. Information fusion via constrained principal component regression for robust quantification with incomplete calibrations. Anal Chim Acta 2013; 797:20-9. [PMID: 24050666 DOI: 10.1016/j.aca.2013.08.036] [Citation(s) in RCA: 11] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Revised: 08/21/2013] [Accepted: 08/22/2013] [Indexed: 11/24/2022]
Abstract
Incomplete calibrations are encountered in many applications and hamper chemometric data analyses. Such situations arise when target analytes are embedded in a chemically complex matrix from which calibration concentrations cannot be determined with reasonable efforts. In other cases, the samples' chemical composition may fluctuate in an unpredictable way and thus cannot be comprehensively covered by calibration samples. The reason for calibration model to fail is the regression principle itself which seeks to explain measured data optimally in terms of the (potentially incomplete) calibration model but does not consider chemical meaningfulness. This study presents a novel chemometric approach which is based on experimentally feasible calibrations, i.e. concentration series of the target analytes outside the chemical matrix ('ex situ calibration'). The inherent lack-of-information is then compensated by incorporating additional knowledge in form of regression constraints. Any outside knowledge can be utilized such as literature values of concentration ranges, concentration ratios implied e.g. by stoichiometry, sum parameters to which multiple analytes need to amount to, and/or reasonable signal reconstructions. The core idea is to mitigate the regression principle's strive for the best possible explanation of measured signals toward the best possible explanation under the condition of chemical meaningfulness. As proof-of-principle application, quantitative analyses of selected compounds in microalgae cells have been chosen. After acquiring FTIR calibration spectra from concentration series of 28 analytes, an ex situ calibration model has been built via principal component regression (PCR). Since microalgae biomass is a very complex matrix, the prediction step based on such an incomplete calibration fails. However, after incorporating several regression constraints into PCR predictions, chemically impossible results are avoided as depicted in the graphical abstract. Equally important are enhancements in concentration reproducibility. For most samples in the chosen application, the errorbars were reduced by one order of magnitude. By means of this novel chemometric method, quantitative analyses have been improved so much that cell responses to chemical shifts in their culturing environment can be studied.
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Affiliation(s)
- Frank Vogt
- Department of Chemistry, University of Tennessee, 552 Buehler Hall, Knoxville, TN 37996-1600, USA.
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