161 results on '"An, Minghui"'
Search Results
2. An integrated visual analytics system for studying clinical carotid artery plaques
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Xu, Chaoqing, Zheng, Zhentao, Fu, Yiting, Chang, Baofeng, Chen, Legao, Wu, Minghui, Song, Mingli, and Jiang, Jinsong
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- 2024
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3. Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm
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Wang, Fangbin, Xiao, Minghui, Qi, Jing, and Zhu, Liang
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- 2024
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4. Incorporating informatively collected laboratory data from EHR in clinical prediction models
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Minghui Sun, Matthew M. Engelhard, Armando D. Bedoya, and Benjamin A. Goldstein
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Electronic Health Records ,Missing Data ,Embedding ,Machine Learning ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Background Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance. Methods We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals. Results We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results. Conclusion Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models.
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- 2024
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5. A pan-cancer cuproptosis signature predicting immunotherapy response and prognosis
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Xiaojing Zhu, Zixin Zhang, Yanqi Xiao, Hao Wang, Jiaxing Zhang, Mingwei Wang, Minghui Jiang, and Yan Xu
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Cuproptosis ,Prognosis ,Immunotherapy ,Machine learning ,Pan-cancer ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Background: Cuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient. Methods: The multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms. Results: Malignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms. Conclusions: We developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.
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- 2024
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6. Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows
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Du, Minghui, Liang, Bo, Wang, He, Xu, Peng, Luo, Ziren, and Wu, Yueliang
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- 2024
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7. The seismicity in the middle section of the Altyn Tagh Fault system revealed by a dense nodal seismic array
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Shi Yao, Tao Xu, Yingquan Sang, Lingling Ye, Tingwei Yang, Chenglong Wu, and Minghui Zhang
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Altyn Tagh Fault ,Machine learning ,Seismicity ,Dense seismic array ,Geophysics. Cosmic physics ,QC801-809 ,Dynamic and structural geology ,QE500-639.5 - Abstract
The left-lateral Altyn Tagh Fault (ATF) system is the northern boundary of the Qinghai-Xizang Plateau, separating the Tarim Basin and the Qaidam Basin. The middle section of ATF has not recorded any large earthquakes since 1598 AD, so the potential seismic hazard is unclear. We develope an earthquake catalog using continuous waveform data recorded by the Tarim-Altyn-Qaidam dense nodal seismic array from September 17 to November 23, 2021 in the middle section of ATF. With the machine learning-based picker, phase association, location, match and locate workflow, we detecte 233 earthquakes with ML -1–3, far more than 6 earthquakes in the routine catalog. Combining with focal mechanism solutions and the local fault structure, we find that seismic events are clustered along the ATF with strike-slip focal mechanisms and on the southern secondary faults with thrusting focal mechanisms. This overall seismic activity in the middle section of the ATF might be due to the northeastward transpressional motion of the Qinghai-Xizang Plateau block at the western margin of the Qaidam Basin.
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- 2024
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8. The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis
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Xiwei Zhang, Xiaohui Zhao, Lichao Jin, Qianqian Guo, Minghui Wei, Zhengjiang Li, Lijuan Niu, Zhiqiang Liu, and Changming An
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lateral lymph node metastasis ,machine learning ,medullary thyroid carcinoma ,mobile health applications ,prophylactic neck dissection ,TI‐RADS ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed. Methods From January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI‐RADS). Regularized logistic regression, 5‐fold cross‐validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0−1a patients. Results A total of 218 patients were enrolled. Five baseline characteristics and two TI‐RADS features were identified as high‐risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI‐RADS margin and level. A LLNM model was developed and showed a good discrimination with 5‐fold cross‐validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0−1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88–0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64). Conclusions We developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI‐RADS. Our model can predict occult LLNM for cN0−1a patients more accurately, then benefit the decision of prophylactic LND.
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- 2024
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9. Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost
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Su, Kui, Yuan, Xin, Huang, Yukai, Yuan, Qian, Yang, Minghui, Sun, Jianwu, Li, Shuyi, Long, Xinyi, Liu, Lang, Li, Tianwang, and Yuan, Zhengqiang
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- 2023
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10. CTH-Net: A CNN and Transformer hybrid network for skin lesion segmentation
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Yuhan Ding, Zhenglin Yi, Jiatong Xiao, Minghui Hu, Yu Guo, Zhifang Liao, and Yongjie Wang
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Health sciences ,Artificial intelligence ,Machine learning ,Science - Abstract
Summary: Automatically and accurately segmenting skin lesions can be challenging, due to factors such as low contrast and fuzzy boundaries. This paper proposes a hybrid encoder-decoder model (CTH-Net) based on convolutional neural network (CNN) and Transformer, capitalizing on the advantages of these approaches. We propose three modules for skin lesion segmentation and seamlessly connect them with carefully designed model architecture. Better segmentation performance is achieved by introducing SoftPool in the CNN branch and sandglass block in the bottleneck layer. Extensive experiments were conducted on four publicly accessible skin lesion datasets, ISIC 2016, ISIC 2017, ISIC 2018, and PH2 to confirm the efficacy and benefits of the proposed strategy. Experimental results show that the proposed CTH-Net provides better skin lesion segmentation performance in both quantitative and qualitative testing when compared with state-of-the-art approaches. We believe the CTH-Net design is inspiring and can be extended to other applications/frameworks.
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- 2024
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11. Construct high performance SERS sensing platform assisted by machine learning
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Xiaoling Wu, Zhixiong Liu, Yunxiang Liu, Minghui Qiu, and Dan Xu
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Surface-Enhanced Raman Spectroscopy (SERS) ,Self-assembled gold nanoparticles ,Machine learning ,Trace analyte detection ,Uniform SERS substrates ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for detecting trace analytes using noble metal nanoparticles. In this paper, we present a novel approach to construct a high-performance SERS sensing platform using self-assembled gold nanoparticles on aminated glass capillaries. The surface self-assembly technology ensures uniformity and repeatability of the SERS substrate, addressing the challenges of poor reproducibility observed in conventional methods. The 30 nm gold nanoparticles exhibit excellent plasmonic properties and biocompatibility, making them ideal candidates for SERS applications. We conducted SERS detection using Rhodamine 6G (R6G) as probe molecules, achieving a minimum detectable concentration of 0.1 nM for the AuNPs/GS substrate and 0.1 pM for the S-AuNPs/GC substrate. The S-AuNPs/GC substrate demonstrated commendable uniformity and repeatability, with a relative standard deviation of 12.1 %. Machine learning techniques, including baseline correction, normalization, and smoothing, were employed for data processing, enhancing the accuracy and reliability of the SERS analysis. By employing the K-means clustering algorithm, we identified three distinct groups of spectral characteristics. Additionally, Principal Component Analysis (PCA) allowed visualization and understanding of the clustering results in a two-dimensional space, capturing approximately 86.74 % of the data's variance. The successful construction of a high-performance SERS sensing platform with enhanced sensitivity, accuracy, and reliability, assisted by machine learning, holds great potential for various applications in chemical sensing, environmental monitoring, and biomedical diagnostics.
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- 2023
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12. Learning assisted column generation for model predictive control based energy management in microgrids
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Yuchong Huo, Zaiyu Chen, Jing Bu, and Minghui Yin
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Column generation ,Deep neural network ,Energy management ,Machine learning ,Microgrid ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Model predictive control is an effective approach for microgrid energy management. However, the main downside of such method is its expensive online computational cost, which is not amenable to most practical microgrid implementations. To address this issue, we propose a deep neural network assisted column generation approach that can accelerate the solution procedure of model predictive control. In each iteration, our approach leverages different deep neural networks to predict the optimal solutions of all the subproblems in column generation, which can accelerate the computation of all the subproblems and the entire process of column generation. The pre-existing knowledge of the microgrid is also exploited to guarantee the feasibility of the neural network outputs using multi-parametric programming theory. The numerical results show that our approach leads to a reduction in computational time of approximately 50% in a medium-sized microgrid compared with the full mixed integer solution based on traditional branch and bound method, while the optimality loss is only 0.02% in terms of operating costs.
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- 2023
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13. LoS Probability Prediction for A2G mmWave Communications by Using Ray-tracer Under Virtual Urban Scenarios
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Tian, Yue, Pang, Minghui, Duan, Hongtao, Lv, Bing, Chen, Xiaomin, Zhu, Qiuming, Hua, Boyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor
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- 2023
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14. Fast extraction of three-dimensional nanofiber orientation from WAXD patterns using machine learning
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Minghui Sun, Zheng Dong, Liyuan Wu, Haodong Yao, Wenchao Niu, Deting Xu, Ping Chen, Himadri S. Gupta, Yi Zhang, Yuhui Dong, Chunying Chen, and Lina Zhao
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machine learning ,synchrotron microfocus x-ray diffraction ,biological materials ,nanofiber networks ,Crystallography ,QD901-999 - Abstract
Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials.
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- 2023
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15. Swarm intelligence machine-learning-assisted progressive global optimization of DNAPL-contaminated aquifer remediation strategy
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Yunfeng Zhang, Huanliang Chen, Minghui Lv, Zeyu Hou, and Yu Wang
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dnapls ,groundwater contamination ,machine learning ,multi-objective optimization ,progressive global searching ,Water supply for domestic and industrial purposes ,TD201-500 ,River, lake, and water-supply engineering (General) ,TC401-506 - Abstract
Remediation projects of DNAPL-contaminated groundwater generally face difficulties of low contaminant removal rate and high remediation cost. Hence, a machine-learning-assisted mixed-integer multi-objective optimization technique was presented for efficiently programming remediation strategies. A swarm intelligence multi-kernel extreme learning machine (SI-MKELM) was proposed to build a reliable intelligent surrogate model of the multiphase flow numerical simulation model for reducing the computational cost of repetitive CPU-demanding remediation efficiency evaluations, and a hyper-heuristic homotopy algorithm was developed for progressively searching the global optimum of the remediation strategy. The results showed that: (1) The multi-kernel extreme learning machine improved by swarm intelligence algorithm significantly improved the approximation accuracy to the numerical model, and the mean residual and mean relative error were only 0.7596% and 1.0185%, respectively. (2) It only took 0.1 s to run the SI-MKELM. Replacing the numerical model with SI-MKELM considerably reduced the computational burden of the simulation–optimization process and maintained high computational accuracy for optimizing the DNAPL-contaminated aquifer remediation strategy. (3) The hyper-heuristic homotopy algorithm was capable of progressively searching the global optimum, and avoiding premature convergence in the optimization process. It effectively improved the searching ability of the traditional heuristic algorithms. HIGHLIGHTS A swarm intelligence multi-kernel extreme learning machine is proposed to sufficiently approximate the multiphase flow numerical model.; A mixed-integer multi-objective model is established to realize the comprehensive SEAR strategy optimization.; A hyper-heuristic homotopy algorithm is constructed as a more efficient tool for progressively searching the global optimum in wide areas.;
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- 2023
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16. Dense-repeated Jamming Suppression Algorithm Based on the Support Vector Machine for Frequency Agility Radar
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Siyu DU, Zhixing LIU, Yaojun WU, Minghui SHA, and Yinghui QUAN
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electronic counter-measures (ecm) ,machine learning ,frequency agility radar ,dense repeated jamming ,support vector machine (svm) ,Electricity and magnetism ,QC501-766 - Abstract
Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.
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- 2023
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17. Students’ Course Results Prediction Based on Data Processing and Machine Learning Methods
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Liu, Jinyang, Yin, Chuantao, Wang, Kunyang, Guan, Minghui, Wang, Xi, and Zhou, Hong
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- 2022
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18. Combinations of Feature Selection and Machine Learning Models for Object-Oriented 'Staple-Crop-Shifting' Monitoring Based on Gaofen-6 Imagery
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Yujuan Cao, Jianguo Dai, Guoshun Zhang, Minghui Xia, and Zhitan Jiang
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Gaofen-6 ,crop classification ,feature selection ,object-oriented ,machine learning ,remote sensing ,Agriculture (General) ,S1-972 - Abstract
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively.
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- 2024
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19. Predicting Ki‐67 labeling index level in early‐stage lung adenocarcinomas manifesting as ground‐glass opacity nodules using intra‐nodular and peri‐nodular radiomic features
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Minghui Zhu, Zhen Yang, Wei Zhao, Miaoyu Wang, Wenjia Shi, Zhenshun Cheng, Cheng Ye, Qiang Zhu, Lu Liu, Zhixin Liang, and Liangan Chen
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ground‐glass opacity nodule ,Ki‐67 ,lung cancer ,machine learning ,radiomics ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Objectives To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground‐glass opacity nodules (GGO) with high‐ and low Ki‐67 expression levels. Materials and Methods From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Results Seven hundred and sixty‐nine patients with 769 GGOs were included in this study. Two hundred and forty‐five GGOs were confirmed to be of high Ki‐67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high‐ and low Ki‐67 LI groups (p
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- 2022
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20. Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
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Pang, Minghui, Zhu, Qiuming, Lin, Zhipeng, Bai, Fei, Tian, Yue, Li, Zhuo, and Chen, Xiaomin
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- 2022
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21. Accurate Contact-Free Material Recognition with Millimeter Wave and Machine Learning
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He, Shuang, Qian, Yuhang, Zhang, Huanle, Zhang, Guoming, Xu, Minghui, Fu, Lei, Cheng, Xiuzhen, Wang, Huan, Hu, Pengfei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor
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- 2022
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22. Uncovering ferroptosis in Parkinson’s disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products
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Peng Wang, Qi Chen, Zhuqian Tang, Liang Wang, Bizhen Gong, Min Li, Shaodan Li, and Minghui Yang
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Parkinson’s disease ,ferroptosis ,transcriptomics ,machine learning ,natural product ,ingredient ,Genetics ,QH426-470 - Abstract
Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson’s disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes.Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD.Results: We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the “Overlapping Genes-Ingredients” network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD.Conclusion: Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD.
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- 2023
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23. Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics
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Yangyang Guo, Kenan Cen, Kai Hong, Yifeng Mai, and Minghui Jiang
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renal fibrosis ,machine learning ,immune cell ,CIBERSORT ,diagnostic biomarker ,Immunologic diseases. Allergy ,RC581-607 - Abstract
BackgroundRecently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis.MethodsUsing the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis.ResultsFour candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers.ConclusionDOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.
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- 2023
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24. Editorial: Protein recognition and associated diseases
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M. Michael Gromiha, Petras Kundrotas, Marcelo Adrian Marti, Česlovas Venclovas, and Minghui Li
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protein-protein interactions ,binding affinity ,machine learning ,protein-protein interaction networks ,phylogenetic profiles ,Computer applications to medicine. Medical informatics ,R858-859.7 - Published
- 2023
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25. Visualization and accuracy improvement of soil classification using laser-induced breakdown spectroscopy with deep learning
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Yanwu Chu, Yu Luo, Feng Chen, Chengwei Zhao, Tiancheng Gong, Yanqing Wang, Lianbo Guo, and Minghui Hong
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Soil science ,Laser ,Machine learning ,Science - Abstract
Summary: Deep learning method is applied to spectral detection due to the advantage of not needing feature engineering. In this work, the deep neural network (DNN) model is designed to perform data mining on the laser-induced breakdown spectroscopy (LIBS) spectra of the ore. The potential of heat diffusion for an affinity-based transition embedding model is first used to perform nonlinear mapping of fully connected layer data in the DNN model. Compared with traditional methods, the DNN model has the highest recognition accuracy rate (75.92%). A training set update method based on DNN output is proposed, and the final model has a recognition accuracy of 85.54%. The method of training set update proposed in this work can not only obtain the sample labels quickly but also improve the accuracy of deep learning models. The results demonstrate that LIBS combined with the DNN model is a valuable tool for ore classification at a high accuracy rate.
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- 2023
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26. Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission
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Hu, Zhixu, Qiu, Hang, Wang, Liya, and Shen, Minghui
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- 2022
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27. Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission
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Zhixu Hu, Hang Qiu, Liya Wang, and Minghui Shen
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Length of stay ,Machine learning ,Multimorbidity network ,Network analysis ,Patient similarity network ,Point of admission ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Background An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately. Methods In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance. Results The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions. Conclusion Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.
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- 2022
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28. Discovery and validation of colorectal cancer tissue-specific methylation markers: a dual-center retrospective cohort study.
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Cao, Qinxing, Dan, Zhenjia, Hou, Nengyi, Yan, Li, Yuan, Xingmei, Lu, Hejiang, Yu, Song, Zhang, Jiangping, Xiao, Huasheng, Liu, Qiang, Zhang, Xiaoyong, Zhang, Min, and Pang, Minghui
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RANDOM forest algorithms ,MACHINE learning ,PRECANCEROUS conditions ,COLORECTAL cancer ,DNA methylation ,FECAL contamination - Abstract
Background and purpose: Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. Method: We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. Results: This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. Conclusion: The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation. [ABSTRACT FROM AUTHOR]
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- 2024
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29. ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction.
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Xu, Ran, Pan, Qican, Zhu, Guoliang, Ye, Yilin, Xin, Minghui, Wang, Zechen, Wang, Sheng, Li, Weifeng, Wei, Yanjie, Guo, Jingjing, and Zheng, Liangzhen
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Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence‐ and structure‐based features and constructed machine‐learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost‐DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS‐bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink. [ABSTRACT FROM AUTHOR]
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- 2024
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30. Aerodynamic force prediction of compressor blade surfaces based on machine learning.
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Niu, Yan, Zhao, Kainuo, Yao, Minghui, Wu, Qiliang, Yang, Shaowu, and Ma, Li
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CONVOLUTIONAL neural networks ,MACHINE learning ,COMPUTATIONAL fluid dynamics ,COMPRESSOR blades ,RANDOM forest algorithms ,AERODYNAMIC load - Abstract
The flow field distribution of compressor blades is critical to the performance of aero-engine. To efficiently obtain the aerodynamic loads on the blades, this study employs machine learning models to predict the aerodynamic characteristics of compressor blade surfaces. The predictive performances of these models are evaluated by applying random forest, multi-layer perceptron (MLP), one-dimensional convolutional neural network, and long short-term memory network based on simulation data of computational fluid dynamics (CFD). The results indicate that the MLP model performs exceptionally well among all test metrics, with its predictions closely matching the CFD simulation results. Further analysis using SHapley Additive exPlanations methods is performed to interpret the MLP model and reveal the importance of various input features. The research demonstrates the significant potential of machine learning methods in predicting the aerodynamics of compressor blades and providing accurate and reliable results. [ABSTRACT FROM AUTHOR]
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- 2024
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31. A General Deep Learning Method for Computing Molecular Parameters of a Viscoelastic Constitutive Model by Solving an Inverse Problem
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Minghui Ye, Yuan-Qi Fan, and Xue-Feng Yuan
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machine learning ,inverse problem ,deep neural network ,constitutive equation ,polymeric fluids ,viscoelasticity ,Organic chemistry ,QD241-441 - Abstract
Prediction of molecular parameters and material functions from the macroscopic viscoelastic properties of complex fluids are of great significance for molecular and formulation design in fundamental research as well as various industrial applications. A general learning method for computing molecular parameters of a viscoelastic constitutive model by solving an inverse problem is proposed. The accuracy, convergence and robustness of a deep neural network (DNN)-based numerical solver have been validated by considering the Rolie-Poly model for modeling the linear and non-linear steady rheometric properties of entangled polymer solutions in a wide range of concentrations. The results show that as long as the DNN could be trained with a sufficiently high accuracy, the DNN-based numerical solver would rapidly converge to its solution in solving an inverse problem. The solution is robust against small white noise disturbances to the input stress data. However, if the input stress significantly deviates from the original stress, the DNN-based solver could readily converge to a different solution. Hence, the resolution of the numerical solver for inversely computing molecular parameters is demonstrated. Moreover, the molecular parameters computed by the DNN-based numerical solver not only reproduce accurately the steady viscoelastic stress of completely monodisperse linear lambda DNA solutions over a wide range of shear rates and various concentrations, but also predict a power law concentration scaling with a nearly same scaling exponent as those estimated from experimental results.
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- 2023
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32. Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model
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Jiayan Zheng, Tianchen Yao, Jianhong Yue, Minghui Wang, and Shuangchen Xia
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BFRC ,compressive strength ,genetic algorithm ,machine learning ,Building construction ,TH1-9745 - Abstract
Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for the projection of compressive strength (CS) on BFRC. GA optimization may reduce many debugging efforts and provide optimal parameter combinations for machine learning (ML) algorithms. The XGBoost is a powerful integrated learning algorithm with efficient, accurate, and scalable features. First, we created and provided a common dataset using test data on BFRC strength from the literature. We segmented and scaled this dataset to enhance the robustness of the ML model. Second, to better predict and evaluate the CS of BFRC, we simultaneously used five other regression models: XGBoost, random forest (RF), gradient-boosted decision tree (GBDT) regressor, AdaBoost, and support vector regression (SVR). The analysis results of test sets indicated that the correlation coefficient and mean absolute error were 0.9483 and 2.0564, respectively, when using the GA-XGBoost model. The GA-XGBoost model demonstrated superior performance, while the AdaBoost model exhibited the poorest performance. In addition, we verified the accuracy and feasibility of the GA-XGBoost model through SHAP analysis. The findings indicated that the water–binder ratio (W/B), fine aggregate (FA), and water–cement ratio (W/C) in BFRC were the variables that had the greatest effect on CS, while silica fume (SF) had the least effect on CS. The results demonstrated that GA-XGBoost exhibits exceptional accuracy in predicting the CS of BFRC, which offers a valuable reference for the engineering domain.
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- 2023
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33. The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China
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Tianyu Feng, Zhou Zheng, Jiaying Xu, Minghui Liu, Ming Li, Huanhuan Jia, and Xihe Yu
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road traffic injuries ,time series analysis ,machine learning ,predictive models ,comparative study ,Public aspects of medicine ,RA1-1270 - Abstract
ObjectiveThis cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.MethodologySeasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.FindingsIn this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.ConclusionBy adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.ImplicationThe results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.
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- 2022
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34. Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning
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Jingjing Rao, Zhen Fan, Qicheng Huang, Yongjian Luo, Xingmin Zhang, Haizhong Guo, Xiaobing Yan, Guo Tian, Deyang Chen, Zhipeng Hou, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Xingsen Gao, and Jun-Ming Liu
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Machine learning ,ferroelectric tunnel junctions ,ON/OFF ratio ,nonvolatile memory ,Electricity ,QC501-721 - Abstract
Ferroelectric tunnel junction (FTJ) has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing. However, the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials, fabrication methods and conditions and so on. Here, machine learning (ML) is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios. A dataset consisting of 152 FTJ samples with nine features and one target attribute (i.e., ON/OFF ratio) is established for ML modeling. Among various ML models, the gradient boosting classification model achieves the highest prediction accuracy. Combining the feature importance analysis based on this model with the association rule mining, it is extracted that the utilizations of {graphene/graphite (Gra) (top), LaNiO3 (LNO) (bottom)} and {Gra (top), Ca[Formula: see text]Ce[Formula: see text]MnO3 (CCMO) (bottom)} electrode pairs are likely to result in high ON/OFF ratios in FTJs. Moreover, two previously unexplored FTJs: Gra/BaTiO3 (BTO)/LNO and Gra/BTO/CCMO, are predicted to achieve ON/OFF ratios higher than 1000. Guided by the ML predictions, the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated, which unsurprisingly exhibit [Formula: see text]1000 ON/OFF ratios ([Formula: see text]8540 and [Formula: see text]7890, respectively). This study demonstrates a new paradigm of developing high-performance FTJs by using ML.
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- 2022
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35. ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study
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Miao Hui, Jun Ma, Hongyu Yang, Bixia Gao, Fang Wang, Jinwei Wang, Jicheng Lv, Luxia Zhang, Li Yang, and Minghui Zhao
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chronic kidney disease ,progression ,prediction model ,machine learning ,Medicine - Abstract
Background and objectives: In light of the growing burden of chronic kidney disease (CKD), it is of particular importance to create disease prediction models that can assist healthcare providers in identifying cases of CKD individual risk and integrate risk-based care for disease progress management. The objective of this study was to develop and validate a new pragmatic end-stage kidney disease (ESKD) risk prediction utilizing the Cox proportional hazards model (Cox) and machine learning (ML). Design, setting, participants, and measurements: The Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE), a multicenter CKD cohort in China, was employed as the model’s training and testing datasets, with a split ratio of 7:3. A cohort from Peking University First Hospital (PKUFH cohort) served as the external validation dataset. The participants’ laboratory tests in those cohorts were conducted at PKUFH. We included individuals with CKD stages 1~4 at baseline. The incidence of kidney replacement therapy (KRT) was defined as the outcome. We constructed the Peking University-CKD (PKU-CKD) risk prediction model employing the Cox and ML methods, which include extreme gradient boosting (XGBoost) and survival support vector machine (SSVM). These models discriminate metrics by applying Harrell’s concordance index (Harrell’s C-index) and Uno’s concordance (Uno’s C). The calibration performance was measured by the Brier score and plots. Results: Of the 3216 C-STRIDE and 342 PKUFH participants, 411 (12.8%) and 25 (7.3%) experienced KRT with mean follow-up periods of 4.45 and 3.37 years, respectively. The features included in the PKU-CKD model were age, gender, estimated glomerular filtration rate (eGFR), urinary albumin–creatinine ratio (UACR), albumin, hemoglobin, medical history of type 2 diabetes mellitus (T2DM), and hypertension. In the test dataset, the values of the Cox model for Harrell’s C-index, Uno’s C-index, and Brier score were 0.834, 0.833, and 0.065, respectively. The XGBoost algorithm values for these metrics were 0.826, 0.825, and 0.066, respectively. The SSVM model yielded values of 0.748, 0.747, and 0.070, respectively, for the above parameters. The comparative analysis revealed no significant difference between XGBoost and Cox, in terms of Harrell’s C, Uno’s C, and the Brier score (p = 0.186, 0.213, and 0.41, respectively) in the test dataset. The SSVM model was significantly inferior to the previous two models (p < 0.001), in terms of discrimination and calibration. The validation dataset showed that XGBoost was superior to Cox, regarding Harrell’s C, Uno’s C, and the Brier score (p = 0.003, 0.027, and 0.032, respectively), while Cox and SSVM were almost identical concerning these three parameters (p = 0.102, 0.092, and 0.048, respectively). Conclusions: We developed and validated a new ESKD risk prediction model for patients with CKD, employing commonly measured indicators in clinical practice, and its overall performance was satisfactory. The conventional Cox regression and certain ML models exhibited equal accuracy in predicting the course of CKD.
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- 2023
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36. Incorporating informatively collected laboratory data from EHR in clinical prediction models.
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Sun, Minghui, Engelhard, Matthew M., Bedoya, Armando D., and Goldstein, Benjamin A.
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MISSING data (Statistics) , *PREDICTION models , *ELECTRONIC health records , *MULTIPLE imputation (Statistics) - Abstract
Background: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance. Methods: We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals. Results: We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results. Conclusion: Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer.
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Liang, Rong, Li, Fangfang, Yao, Jingyuan, Tong, Fang, Hua, Minghui, Liu, Junjun, Shi, Chenlei, Sui, Lewen, and Lu, Hong
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DEEP learning ,MAGNETIC resonance mammography ,CANCER invasiveness ,BREAST cancer ,MACHINE learning - Abstract
To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions. [ABSTRACT FROM AUTHOR]
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- 2024
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38. Self‐Powered Biomimetic Pressure Sensor Based on Mn–Ag Electrochemical Reaction for Monitoring Rehabilitation Training of Athletes.
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Yang, Ziyan, Wang, Qingzhou, Yu, Huixin, Xu, Qing, Li, Yuanyue, Cao, Minghui, Dhakal, Rajendra, Li, Yang, and Yao, Zhao
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PRESSURE sensors ,ATHLETE training ,INFORMATION display systems ,WEARABLE technology ,PIEZOELECTRIC detectors ,MACHINE learning ,LIGHT emitting diodes ,POTENTIOMETRY - Abstract
Self‐powered pressure detection using smart wearable devices is the subject of intense research attention, which is intended to address the critical need for prolonged and uninterrupted operations. Current piezoelectric and triboelectric sensors well respond to dynamic stimuli while overlooking static stimuli. This study proposes a dual‐response potentiometric pressure sensor that responds to both dynamic and static stimuli. The proposed sensor utilizes interdigital electrodes with MnO2/carbon/polyvinyl alcohol (PVA) as the cathode and conductive silver paste as the anode. The electrolyte layer incorporates a mixed hydrogel of PVA and phosphoric acid. The optimized interdigital electrodes and sandpaper‐like microstructured surface of the hydrogel electrolyte contribute to enhanced performance by facilitating an increased contact area between the electrolyte and electrodes. The sensor features an open‐circuit voltage of 0.927 V, a short‐circuit current of 6 µA, a higher sensitivity of 14 mV/kPa, and outstanding cycling performance (>5000 cycles). It can accurately recognize letter writing and enable capacitor charging and LED lighting. Additionally, a data acquisition and display system employing the proposed sensor, which facilitates the monitoring of athletes' rehabilitation training, and machine learning algorithms that effectively guide rehabilitation actions are presented. This study offers novel solutions for the future development of smart wearable devices. [ABSTRACT FROM AUTHOR]
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- 2024
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39. The machine learning‐based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.
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Zhang, Xiwei, Zhao, Xiaohui, Jin, Lichao, Guo, Qianqian, Wei, Minghui, Li, Zhengjiang, Niu, Lijuan, Liu, Zhiqiang, and An, Changming
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NECK dissection ,LYMPHATIC metastasis ,THYROID cancer ,MEDULLARY thyroid carcinoma ,OCCULTISM ,METASTASIS ,NECK - Abstract
Background: For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed. Methods: From January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI‐RADS). Regularized logistic regression, 5‐fold cross‐validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0−1a patients. Results: A total of 218 patients were enrolled. Five baseline characteristics and two TI‐RADS features were identified as high‐risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI‐RADS margin and level. A LLNM model was developed and showed a good discrimination with 5‐fold cross‐validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0−1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88–0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64). Conclusions: We developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI‐RADS. Our model can predict occult LLNM for cN0−1a patients more accurately, then benefit the decision of prophylactic LND. [ABSTRACT FROM AUTHOR]
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- 2024
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40. Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study.
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Tang, Minghui, Sugiyama, Taku, Takahari, Ren, Sugimori, Hiroyuki, Yoshimura, Takaaki, Ogasawara, Katsuhiko, Kudo, Kohsuke, and Fujimura, Miki
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SURGICAL anastomosis , *BLOOD substitutes , *SURGICAL education , *PILOT projects , *ALGORITHMS , *PATIENT education , *TRAINING of surgeons - Abstract
Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the "respect for tissue." The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety. [ABSTRACT FROM AUTHOR]
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- 2024
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41. Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion.
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Shao, Minghui, Li, Songyuan, Li, You, and Li, Shuncai
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MACHINE learning , *SURFACE roughness , *PRINCIPAL components analysis , *LIGHT sources , *SURFACE texture , *TITANIUM alloys - Abstract
Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing. [ABSTRACT FROM AUTHOR]
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- 2024
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42. Age Estimation from Brain Magnetic Resonance Images Using Deep Learning Techniques in Extensive Age Range
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Kousuke Usui, Takaaki Yoshimura, Minghui Tang, and Hiroyuki Sugimori
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deep learning ,age estimation ,regression model ,machine learning ,ResNet-50 ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Estimation of human age is important in the fields of forensic medicine and the detection of neurodegenerative diseases of the brain. Particularly, the age estimation methods using brain magnetic resonance (MR) images are greatly significant because these methods not only are noninvasive but also do not lead to radiation exposure. Although several age estimation methods using brain MR images have already been investigated using deep learning, there are no reports involving younger subjects such as children. This study investigated the age estimation method using T1-weighted (sagittal plane) two-dimensional brain MR imaging (MRI) of 1000 subjects aged 5–79 (31.64 ± 18.04) years. This method uses a regression model based on ResNet-50, which estimates the chronological age (CA) of unknown brain MR images by training brain MR images corresponding to the CA. The correlation coefficient, coefficient of determination, mean absolute error, and root mean squared error were used as the evaluation indices of this model, and the results were 0.9643, 0.9299, 5.251, and 6.422, respectively. The present study showed the same degree of correlation as those of related studies, demonstrating that age estimation can be performed for a wide range of ages with higher estimation accuracy.
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- 2023
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43. Prediction of the rate of penetration using logistic regression algorithm of machine learning model
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Deng, Shuang, Wei, Minghui, Xu, Mingze, and Cai, Wei
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- 2021
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44. Decoding the metabolomic responses of Caragana tibetica to livestock grazing in fragile ecosystems.
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Minghui He, Yanlong Han, Yong Gao, Min Han, and Liqing Duan
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MACHINE learning ,GRAZING ,METABOLOMICS ,LIVESTOCK growth ,PLANT adaptation ,ECOSYSTEMS - Abstract
The population of Caragana tibetica, situated on the edge of the typical grassland-to-desert transition in the Mu Us Sandy Land, plays a vital ecological role in maintaining stability within the regional fragile ecosystem. Despite the consistent growth of C. tibetica following animal grazing, the biological mechanisms underlying its compensatory growth in response to livestock consumption remain unclear. Analyzing 48 metabolomic profiles from C. tibetica, our study reveals that the grazing process induces significant changes in the metabolic pathways of C. tibetica branches. Differential metabolites show correlations with soluble protein content, catalase, peroxidase, superoxide dismutase, malondialdehyde, and proline levels. Moreover, machine learning models built on these differential metabolites accurately predict the intensity of C. tibetica grazing (with an accuracy of 83.3%). The content of various metabolites, indicative of plant stress responses, including Enterolactone, Narceine, and Folcepri, exhibits significant variations in response to varying grazing intensities (P<0.05). Our investigation reveals that elevated grazing intensity intensifies the stress response in C. tibetica, triggering heightened antioxidative defenses and stress-induced biochemical activities. Distinctive metabolites play a pivotal role in responding to stress, facilitating the plant's adaptation to environmental challenges and fostering regeneration. [ABSTRACT FROM AUTHOR]
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- 2024
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45. Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis.
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Zhang, Minghui, Wang, Yan, Lv, Mutian, Sang, Li, Wang, Xuemei, Yu, Zijun, Yang, Ziyi, Wang, Zhongqing, and Sang, Liang
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DEEP learning ,BIBLIOMETRICS ,RADIOMICS ,INTERNATIONAL cooperation ,MACHINE learning ,WEBSITES - Abstract
Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research. [ABSTRACT FROM AUTHOR]
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- 2024
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46. Combinations of Feature Selection and Machine Learning Models for Object-Oriented "Staple-Crop-Shifting" Monitoring Based on Gaofen-6 Imagery.
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Cao, Yujuan, Dai, Jianguo, Zhang, Guoshun, Xia, Minghui, and Jiang, Zhitan
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MACHINE learning ,FEATURE selection ,ARABLE land ,IMAGE recognition (Computer vision) ,FEATURE extraction ,COTTON ,FOOD crops ,CASH crops - Abstract
This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. "Staple-food-shifting" refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of "staple-food-shifting" on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively. [ABSTRACT FROM AUTHOR]
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- 2024
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47. Diagnostic Performance of Machine Learning-Derived Radiomics Signature of Pericoronary Adipose Tissue in Coronary Computed Tomography Angiography for Coronary Artery In-Stent Restenosis.
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Cui, Keyi, Liang, Shuo, Hua, Minghui, Gao, Yufan, Feng, Zhenxing, Wang, Wenjiao, and Zhang, Hong
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Coronary inflammation can alter the perivascular fat phenotype. Hence, we aimed to assess the diagnostic performance of radiomics features of pericoronary adipose tissue (PCAT) in coronary computed tomography angiography (CCTA) for in-stent restenosis (ISR) after percutaneous coronary intervention. In this study, 165 patients with 214 eligible vessels were included, and ISR was found in 79 vessels. After evaluating clinical and stent characteristics, peri-stent fat attenuation index, and PCAT volume, 1688 radiomics features were extracted from each peri-stent PCAT segmentation. The eligible vessels were randomly categorized into training and validation groups in a ratio of 7:3. After performing feature selection using Pearson's correlation, F test, and least absolute shrinkage and selection operator analysis, radiomics models and integrated models that combined selected clinical features and Radscore were established using five different machine learning algorithms (logistic regression, support vector machine, random forest, stochastic gradient descent, and XGBoost). Subgroup analysis was performed using the same method for patients with stent diameters of ≤ 3 mm. Nine significant radiomics features were selected, and the areas under the curves (AUCs) for the radiomics model and the integrated model were 0.69 and 0.79, respectively, for the validation group. The AUCs of the subgroup radiomics model based on 15 selected radiomics features and the subgroup integrated model were 0.82 and 0.85, respectively, for the validation group, which showed better diagnostic performance. CCTA-based radiomics signature of PCAT has the potential to identify coronary artery ISR without additional costs or radiation exposure. [ABSTRACT FROM AUTHOR]
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- 2023
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48. A physics-based predictive model for pulse design to realize high-performance memristive neural networks.
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Deng, Haoyue, Fan, Zhen, Dong, Shuai, Chen, Zhiwei, Li, Wenjie, Chen, Yihong, Liu, Kun, Tao, Ruiqiang, Tian, Guo, Chen, Deyang, Qin, Minghui, Zeng, Min, Lu, Xubing, Zhou, Guofu, Gao, Xingsen, and Liu, Jun-Ming
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COMPUTATIONAL physics ,PREDICTION models ,MACHINE learning ,ARTIFICIAL neural networks ,ARTIFICIAL intelligence - Abstract
Memristive neural networks have extensively been investigated for their capability in handling various artificial intelligence tasks. The training performance of memristive neural networks depends on the pulse scheme applied to the constituent memristors. However, the design of the pulse scheme in most previous studies was approached in an empirical manner or through a trial-and-error method. Here, we choose ferroelectric tunnel junction (FTJ) as a model memristor and demonstrate a physics-based predictive model for the pulse design to achieve high training performance. This predictive model comprises a physical model for FTJ that can adequately describe the polarization switching and memristive switching behaviors of the FTJ and an FTJ-based neural network that uses the long-term potentiation (LTP)/long-term depression (LTD) characteristics of the FTJ for the weight update. Simulation results based on the predictive model demonstrate that the LTP/LTD characteristics with a good trade-off between ON/OFF ratio, nonlinearity, and asymmetry can lead to high training accuracies for the FTJ-based neural network. Moreover, it is revealed that an amplitude-increasing pulse scheme may be the most favorable pulse scheme as it offers the widest ranges of pulse amplitudes and widths for achieving high accuracies. This study may provide useful guidance for the pulse design in the experimental development of high-performance memristive neural networks. [ABSTRACT FROM AUTHOR]
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- 2023
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49. Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s} = 13$ TeV with the ATLAS detector
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Aad, Georges, Abbott, Braden Keim, Abeling, Kira, Abicht, Nils Julius, Abidi, Haider, Aboulhorma, Asmaa, Abramowicz, Halina, Abreu, Henso, Abulaiti, Yiming, Abusleme, Angel, Acharya, Bobby Samir, Adam Bourdarios, Claire, Adamczyk, Leszek, Adamek, Lukas, Addepalli, Sagar, Addison, Matt, Adelman, Jahred, Adiguzel, Aytul, Adye, Tim, Affolder, Tony, Afik, Yoav, Agaras, Merve Nazlim, Agarwala, Jinky, Aggarwal, Anamika, Agheorghiesei, Catalin, Ahmad, Ammara, Ahmadov, Faig, Ahmed, Waleed Syed, Ahuja, Sudha, Ai, Xiaocong, Aielli, Giulio, Aikot, Arya, Ait Tamlihat, Malak, Aitbenchikh, Brahim, Aizenberg, Iakov, Akbiyik, Melike, Akesson, Torsten, Akimov, Andrei, Akiyama, Daiya, Akolkar, Nilima Nilesh, Al Khoury, Konie, Alberghi, Gian Luigi, Albert, Justin, Albicocco, Pietro, Albouy, Guillaume Lucas, Alderweireldt, Sara, Aleksa, Martin, Alexandrov, Igor, Alexa, Calin, Alexopoulos, Theodoros, Alfonsi, Fabrizio, Algren, Malte, Alhroob, Muhammad, Ali, Babar, Ali, Hanadi, Ali, Shahzad, Alibocus, Samuel William, Aliev, Malik, Alimonti, Gianluca, Alkakhi, Wael, Allaire, Corentin, Allbrooke, Benedict, Allen, Julia Frances, Allendes Flores, Cristian Andres, Allport, Philip Patrick, Aloisio, Alberto, Alonso, Francisco, Alpigiani, Cristiano, Alvarez Estevez, Manuel, Alvarez Fernandez, Adrian, Alves Cardoso, Mario, Alviggi, Mariagrazia, Aly, Mohamed, Do Amaral Coutinho, Yara, Ambler, Alessandro, Amelung, Christoph, Amerl, Maximilian, Ames, Christoph, Amidei, Dante, Amor Dos Santos, Susana Patricia, Amos, Kieran Robert, Ananiev, Viktor, Anastopoulos, Christos, Andeen, Timothy Robert, Anders, John Kenneth, Andrean, Stefio Yosse, Andreazza, Attilio, Angelidakis, Stylianos, Angerami, Aaron, Anisenkov, Alexey, Annovi, Alberto, Antel, Claire, Anthony, Matthew Thomas, Antipov, Egor, Antonelli, Mario, Anulli, Fabio, Aoki, Masato, Aoki, Takumi, Aparisi Pozo, Javier Alberto, Aparo, Marco, Aperio Bella, Ludovica, Appelt, Christian, Apyan, Aram, Aranzabal Barrio, Nordin, Arcangeletti, Chiara, Arce, Ayana Tamu, Arena, Eloisa, Arguin, Jean-Francois, Argyropoulos, Spyros, Arling, Jan-Hendrik, Arnaez, Olivier, Arnold, Hannah, Artoni, Giacomo, Asada, Haruka, Asai, Kanae, Asai, Shoji, Asbah, Nedaa Alexandra, Assahsah, Jihad, Assamagan, Ketevi Adikle, Astalos, Robert, Atashi, Rose, Atkin, Ryan Justin, Atkinson, Markus Julian, Atmani, Hicham, Atmasiddha, Prachi, Augsten, Kamil, Auricchio, Silvia, Auriol, Adrien, Austrup, Volker Andreas, Avolio, Giuseppe, Axiotis, Konstantinos, Azuelos, Georges, Babal, Dominik, Bachacou, Henri, Bachas, Konstantinos, Bachiu, Alexander, Backman, Karl Filip, Badea, Anthony, Bagnaia, Paolo, Bahmani, Marzieh, Bailey, Adam, Bailey, Virginia, Baines, John, Baines, Luke, Bakalis, Christos, Baker, Keith, Bakos, Evelin, Bakshi Gupta, Debottam, Balakrishnan, Veena, Balasubramanian, Rahul, Baldin, Evgenii, Balek, Petr, Ballabene, Eric, Balli, Fabrice, Baltes, Lisa Marie, Balunas, William Keaton, Balz, Johannes, Banas, Elzbieta, Bandieramonte, Marilena, Bandyopadhyay, Anjishnu, Bansal, Shubham, Barak, Liron, Barakat, Marawan, Barberio, Elisabetta, Barberis, Dario, Barbero, Marlon Benoit, Barends, Kevin Nicholas, Barillari, Teresa, Barisits, Martin, Barklow, Tim, Baron, Petr, Baron, Diego, Baroncelli, Toni, Barone, Gaetano, Barr, Alan, Barr, Jackson, Barranco Navarro, Laura, Barreiro Alonso, Fernando, Barreiro Guimaraes Da Costa, Joao, Barron, Uriel, Barros, Maura, Barsov, Sergey, Bartels, Falk, Bartoldus, Rainer, Barton, Adam Edward, Bartos, Pavol, Basan, Alexander, Baselga Bacardit, Marta, Bassalat, Ahmed, Basso, Matthew Joseph, Basson, Candice Ruth, Bates, Richard, Batlamous, Souad, Batley, Richard, Batool, Binish, Battaglia, Marco, Battulga, Daariimaa, Bauce, Matteo, Bauer, Michael, Bauer, Patrick, Bazzano, Tomas, Beacham, James, Beau, Tristan, Beauchemin, Pierre-Hugues, Becherer, Fabian, Bechtle, Philip, Beck, Hans Peter, Becker, Kathrin, Beddall, Andrew, Bednyakov, Vadim, Bee, Chris, Beemster, Lars, Beermann, Thomas, Begalli, Marcia, Begel, Michael, Behera, Arabinda, Behr, Janna Katharina, Beirer, Joshua Falco, Beisiegel, Florian, Belfkir, Mohamed, Bella, Gideon, Bellagamba, Lorenzo, Bellerive, Alain, Bellos, Panagiotis, Beloborodov, Konstantin, Belyaev, Nikita, Benchekroun, Driss, Bendebba, Fatima, Benhammou, Yan, Benoit, Mathieu, Bensinger, Jim, Bentvelsen, Stan, Beresford, Lydia Audrey, Beretta, Matteo Mario, Bergeaas Kuutmann, Elin, Berger, Nicolas, Bergmann, Benedikt Ludwig, Beringer, Juerg, Bernardi, Gregorio, Bernius, Catrin, Bernlochner, Florian Urs, Bernon, Florent, Berry, Tracey, Berta, Peter, Berthold, Anne-Sophie, Bertram, Iain, Bethke, Siegfried, Betti, Alessandra, Bevan, Adrian, Bhamjee, Muaaz, Bhatta, Somadutta, Bhattacharya, Deb Sankar, Bhattarai, Prajita, Bhopatkar, Vallary Shashikant, Bi, Ran, Bianchi, Riccardo Maria, Bianco, Gianluca, Biebel, Otmar, Bielski, Rafal, Biglietti, Michela, Billoud, Thomas, Bindi, Marcello, Bingul, Ahmet, Bini, Cesare, Biondini, Alessandro, Birch-Sykes, Callum Jacob, Bird, Gareth Adam, Birman, Mattias, Biros, Marek, Biryukov, Stanislav, Bisanz, Tobias, Bisceglie, Emanuele, Biswal, Jyoti Prakash, Biswas, Diptaparna, Bitadze, Alexander, Bjoerke, Kristian, Bloch, Ingo, Blocker, Craig, Blue, Andrew James, Blumenschein, Ulla, Blumenthal, Julian, Bobbink, Gerjan, Bobrovnikov, Viktor, Boehler, Michael, Bohm, Burkhard, Bogavac, Danijela, Bogdanchikov, Alexander, Bohm, Christian, Boisvert, Veronique, Bokan, Petar, Bold, Tomasz, Bomben, Marco, Bona, Marcella, Boonekamp, Maarten, Booth, Callum Dale, Borbely, Albert Gyorgy, Bordulev, Iurii, Borecka-Bielska, Hanna Maria, Borgna, Lucas Santiago, Borissov, Guennadi, Bortoletto, Daniela, Boscherini, Davide, Fernandez-Bosman, Martine, Bossio, Jonathan, Bouaouda, Khalil, Bouchhar, Naseem, Boudreau, Joseph, Bouhova-Thacker, Eva, Boumediene, Djamel Eddine, Bouquet, Romain, Boveia, Antonio, Boyd, Jamie, Boye, Diallo, Boyko, Igor, Bracinik, Juraj, Brahimi, Nihal, Brandt, Gerhard Immanuel, Brandt, Oleg, Braren, Frued Erik, Brau, Benjamin Paul, Brau, Jim, Schimmel Brener, Roy, Brenner, Lydia, Brenner, Richard, Bressler, Shikma, Britton, David, Britzger, Daniel Andreas, Brock, Ian, Brooijmans, Gustaaf, Brooks, William King, Brost, Elizabeth, Brown, Leesa Marea, Bruce, Laura Elaine, Bruckler, Tim Lukas, Bruckman De Renstrom, Pawel, Bruers, Ben, Bruni, Alessia, Bruni, Graziano, Bruschi, Marco, Bruscino, Nello, Buanes, Trygve, Buat, Quentin, Buchin, Daniel, Buckley, Andy, Bulekov, Oleg, Bullard, Brendon, Burdin, Sergey, Burgard, Carsten, Burger, Angela Maria, Burghgrave, Blake Oliver, Burlayenko, Oleksandr, Burr, Jon, Burton, Charles, Burzynski, Jackson Carl, Busch, Elena Laura, Buescher, Volker, Bussey, Peter John, Butler, John Mark, Buttar, Craig Macleod, Butterworth, Jonathan, Buttinger, Will, Buxo Vazquez, Carlos Josue, Buzykaev, Alexey, Cabrera Urban, Susana, Cadamuro, Luca, Caforio, Davide, Cai, Huacheng, Cai, Yuchen, Cairo, Valentina, Cakir, Orhan, Calace, Noemi, Calafiura, Paolo, Calderini, Giovanni, Calfayan, Philippe, Callea, Giuseppe, Caloba, Luiz, Calvet, David, Calvet, Samuel, Calvet, Thomas Philippe, Calvetti, Milene, Camacho Toro, Reina Coromoto, Camarda, Stefano, Camarero Munoz, Daniel, Camarri, Paolo, Camerlingo, Maria Teresa, Cameron, David, Camincher, Clement, Campanelli, Mario, Camplani, Alessandra, Canale, Vincenzo, Canesse, Auriane, Cantero Garcia, Josu, Cao, Yumeng, Capocasa, Francesca, Capua, Marcella, Carbone, Antonio, Cardarelli, Roberto, Cardenas, Juan Carlos, Jr., Cardillo, Fabio, Carli, Tancredi, Carlino, Giampaolo, Carlotto, Juan Ignacio, Carlson, Ben, Carlson, Evan Michael, Carminati, Leonardo, Carnelli, Alberto, Carnesale, Maria, Caron, Sascha, Carquin Lopez, Edson, Carra, Sonia, Carratta, Giuseppe, Carrio Argos, Fernando, Carter, Joseph, Carter, Thomas Michael, Casado Lechuga, Pilar, Caspar, Maximilian, Castiglia, Emma Grace, Castillo, Florencia Luciana, Castillo Garcia, Lucia, Castillo Gimenez, Victoria, Castro, Nuno, Catinaccio, Andrea, Catmore, James, Cavaliere, Viviana, Cavalli, Noemi, Cavasinni, Vincenzo, Cekmecelioglu, Yusuf Can, Celebi, Emre, Celli, Federico, Centonze, Martino Salomone, Cepaitis, Vilius, Cerny, Karel, Santiago Cerqueira, Augusto, Cerri, Alex, Cerrito, Lucio, Cerutti, Fabio, Cervato, Beatrice, Cervelli, Alberto, Cesarini, Gianmario, Cetin, Serkant, Chadi, Zakaria, Chakraborty, Dhiman, Chan, Jay, Chan, Wai Yuen, Chapman, John Derek, Chapon, Emilien, Chargeishvili, Bakar, Charlton, Dave, Charman, Thomas Paul, Chatterjee, Meghranjana, Chauhan, Chainika, Chekanov, Sergei, Chekulaev, Sergey, Shelkov, G., Chen, Andy, Chen, Boping, Chen, Charlie, Chen, Huirun, Chen, Hucheng, Chen, Jing, Chen, Jiayi, Chen, Maggie, Chen, Shion, Chen, Shenjian, Chen, Xiang, Chen, Xin, Chen, Ye, Cheng, Alkaid, Cheng, Hok Chuen Tom, Cheong, Sanha, Cheplakov, Alexander, Cheremushkina, Evgeniya, Cherepanova, Elizaveta, Cherkaoui, Rajaa, Cheu, Elliott, Cheung, Kingman, Chevalier, Laurent, Chiarella, Vitaliano, Chiarelli, Giorgio, Chiedde, Nemer, Chiodini, Gabriele, Chisholm, Andrew Stephen, Chitan, Adrian, Chitishvili, Mariam, Chizhov, Mihail, Choi, Kyungeon, Chomont, Arthur, Chou, Yuan-Tang, Chow, Edwin, Chowdhury, Tasnuva, Chu, Michael Kwok Lam, Chu, Ming Chung, Chu, Xiaotong, Chudoba, Jiri, Chwastowski, Janusz, Cieri, Davide, Ciesla, Krzysztof, Cindro, Vladimir, Ciocio, Alessandra, Cirotto, Francesco, Citron, Zvi, Citterio, Mauro, Ciubotaru, Dan Andrei, Ciungu, Bianca Monica, Clark, Allan, Clark, Philip, Clavijo Columbie, Jose Manuel, Clawson, Savannah, Clement, Christophe, Clercx, Joshua, Clissa, Luca, Coadou, Yann, Cobal, Marina, Coccaro, Andrea, Barrue, Ricardo, Coelho Lopes De Sa, Rafael, Coelli, Simone, Cohen, Hadar, Coimbra, Artur, Cole, Brian, Collot, Johann, Conde Muino, Patricia, Connell, Matt, Connell, Simon, Connelly, Ian Allan, Conroy, Eimear Isobel, Conventi, Francesco, Cooke, Harry, Sarkar, Amanda, Cordeiro Oudot Choi, Artur, Cormier, Felix, Corpe, Louie Dartmoor, Corradi, Massimo, Corriveau, Francois, Cortes Gonzalez, Arely, Costa Mezquita, Maria Jose, Costanza, Francesco, Costanzo, Davide, Cote, Benjamin, Cowan, Glen, Cranmer, Kyle Stuart, Cremonini, Davide, Crepe-Renaudin, Sabine, Crescioli, Francesco, Cristinziani, Markus, Cristoforetti, Marco, Croft, Vincent Alexander, Crosby, Jacob Edwin, Crosetti, Nanni, Cueto Gomez, Ana Rosario, Cuhadar Donszelmann, Tulay, Cui, Han, Cui, Zhaoyuan, Cunningham, Liam, Curcio, Francesco, Czodrowski, Patrick Karl, Czurylo, Marta, Sousa, Mario Jose, Da Fonseca Pinto, Joao Victor, Da Via, Cinzia, Dabrowski, Wladyslaw, Dado, Tomas, Dahbi, Salah-Eddine, Dai, Tiesheng, Dal Santo, Daniele, Dallapiccola, Carlo, Dam, Mogens, D'Amen, Gabriele, D'Amico, Valerio, Damp, Johannes Frederic, Dandoy, Jeff, Daneri, Maria Florencia, Danninger, Matthias, Dao, Valerio, Darbo, Nanni, Darmora, Smita, Das, Sruthy Jyothi, D'Auria, Saverio, David, Claire, Davidek, Tomas, Davis-Purcell, Benjamin Richard, Dawson, Ian, Day-Hall, Henry, De, Kaushik, De Asmundis, Riccardo, De Biase, Nicola, De Castro, Stefano, De Groot, Nicolo, De Jong, Paul, De La Torre Perez, Hector, De Maria, Antonio, De Salvo, Alessandro, De Sanctis, Umberto, De Santo, Antonella, De Vivie De Regie, Jean-Baptiste, Dedovich, Dmitri, Degens, Jordy, Deiana, Allison Mccarn, Del Corso, Francesca, Del Peso, Jose, Del Rio, Fer, Deliot, Frederic, Delitzsch, Chris Malena, Della Pietra, Massimo, Della Volpe, Domenico, Dell'Acqua, Andrea, Dell'Asta, Lidia, Delmastro, Marco, Delsart, Pierre Antoine, Demers Konezny, Sarah Marie, Demichev, Mikhail, Denisov, Serguei, D'Eramo, Louis, Derendarz, Dominik Karol, Derue, Frederic, Dervan, Paul, Desch, Klaus, Deutsch, Christopher, Di Bello, Francesco Armando, Di Ciaccio, Anna, Di Ciaccio, Lucia, Di Domenico, Antonio, Di Donato, Camilla, Di Girolamo, Alessandro, Di Gregorio, Giulia, Di Luca, Andrea, Di Micco, Biagio, Di Nardo, Roberto, Diaconu, Cristinel, Diamantopoulou, Magda, De Almeida Dias, Flavia, Vale, Tiago, Diaz Gutierrez, Marco Aurelio, Diaz Capriles, Federico Guillermo, Didenko, Mariia, Diehl, Edward, Diehl, Leena, Diez Cornell, Sergio, Diez Pardos, Carmen, Dimitriadi, Christina, Dimitrievska, Aleksandra, Dingfelder, Jochen Christian, Dinu, Ioan-Mihail, Dittmeier, Sebastian, Dittus, Fido, Djama, Fares, Djobava, Tamar, Djuvsland, Julia Isabell, Doglioni, Caterina, Dohnalova, Adriana, Dolejsi, Jiri, Dolezal, Zdenek, Dona, Kristin, Donadelli, Marisilvia, Dong, Binbin, Donini, Julien Noce, D'Onofrio, Adelina, D'Onofrio, Monica, Dopke, Jens, Doria, Alessandra, Dos Santos Fernandes, Nuno, Dougan, Patrick, Dova, Maria Teresa, Doyle, Tony, Draguet, Maxence, Dreyer, Etienne, Drivas-Koulouris, Ioannis, Drobac, Alec Swenson, Drozdova, Mariia, Du, Dongshuo, Du Pree, Tristan Arnoldus, Dubinin, Filipp, Dubovsky, Michal, Duchovni, Ehud, Duckeck, Guenter, Ducu, Otilia Anamaria, Duda, Dominik, Dudarev, Alexey, Duden, Emily Rose, D'Uffizi, Matteo, Duflot, Laurent, Duehrssen-Debling, Michael, Dulsen, Carsten, Dumitriu, Ana Elena, Dunford, Monica, Dungs, Sascha, Dunne, Katherine Elaine, Duperrin, Arnaud, Yildiz, Hatice, Dueren, Michael Johannes, Durglishvili, Archil, Dwyer, Brianna, Dyckes, Ian, Dyndal, Mateusz, Dysch, Samuel Dezso, Dziedzic, Bartosz Sebastian, Earnshaw, Zoe Olivia, Eberwein, Gregor Hieronymus, Eckerova, Barbora, Eggebrecht, Stephen, Purcino De Souza, Edmar Egidio, Ehrke, Lukas, Eigen, Gerald, Einsweiler, Kevin Frank, Ekelof, Tord Johan Carl, Ekman, Per Alexander, El Farkh, Saad, El Ghazali, Yassine, El Jarrari, Hassnae, El Moussaouy, Ali, Ellajosyula, Venugopal, Ellert, Mattias, Ellinghaus, Frank, Elliot, Alison, Ellis, Nick, Elmsheuser, Johannes, Elsing, Markus, Emeliyanov, Dmitry, Enari, Yuji, Ene, Irina, Epari, Shalini, Erdmann, Johannes, Erland, Paula Agnieszka, Errenst, Martin, Escalier, Marc, Escobar Ibanez, Carlos, Etzion, Erez, Gaspar De Andrade Evans, Guiomar, Evans, Hal, Evans, Levi, Evans, Meirin Oan, Ezhilov, Aleksei, Ezzarqtouni, Sanae, Fabbri, Federica, Fabbri, Laura, Facini, Gabriel, Fadeyev, Vitaliy, Fakhrutdinov, Rinat, Falciano, Speranza, Falda Coelho, Luis, Falke, Peter Johannes, Faltova, Jana, Fan, Cunwei, Fan, Yunyun, Fang, Yaquan, Fanti, Marcello, Faraj, Mohammed, Farazpay, Zahra, Farbin, Amir, Farilla, Ada, Farooque, Trisha, Farrington, Sinead, Fassi, Farida, Fassouliotis, Dimitris, Faucci Giannelli, Michele, Fawcett, William James, Fayard, Louis, Federic, Pavol, Federicova, Pavla, Fedin, Oleg, Fedotov, Gleb, Feickert, Matthew, Feligioni, Lorenzo, Fellers, Deion Elgin, Feng, Cunfeng, Feng, Minyu, Feng, Zhuoran, Fenton, Michael James, Fenyuk, Alexandre, Ferencz, Lars, Ferguson, Ruby Alice Molly, Fernandez Luengo, Sergio Ivan, Fernoux, Maxime, Ferrando, James, Ferrari, Arnaud, Ferrari, Pamela, Ferrari, Roberto, Ferrere, Didier, Ferretti, Claudio, Fiedler, Frank, Filipcic, Andrej, Filmer, Emily, Filthaut, Frank, Castro Nunes Fiolhais, Miguel, Fiorini, Luca, Fisher, Wade Cameron, Fitschen, Tobias, Fitzhugh, Peter Michael, Fleck, Ivor, 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Perez, David, Munoz Sanchez, Francisca, Murin, Martin, Murray, Bill, Murrone, Alessia, Muse, Joseph M., Muskinja, Miha, Mwewa, Chilufya, Myagkov, Alexei, Myers, Andrew Joel, Myers, Ava Anne, Myers, Greg, Myska, Miroslav, Nachman, Ben, Nackenhorst, Olaf, Nag, Abhishek, Nagai, Koichi, Nagano, Kunihiro, Nagle, James Lawrence, Nagy, Elemer, Nairz, Armin, Nakahama Higuchi, Yu, Nakamura, Koji, Nakkalil, Keerthi, Nanjo, Hajime, Narayan, Rohin Thampilali, Narayanan, Easwar Anand, Naryshkin, Iurii, Naseri, Mohsen, Nasri, Salah, Nass, Christian, Navarro, Gabriela Alejandra, Navarro Gonzalez, Josep, Nayak, Ranjit, Nayaz, Ab, Nechaeva, Polina, Nechansky, Filip, Nedic, Luka, Neep, Tom, Negri, Andrea, Negrini, Matteo, Nellist, Clara, Nelson, Christina, Nelson, Kevin Michael, Nemecek, Stanislav, Nessi, Marzio, Neubauer, Mark, Neuhaus, Friedemann, Neundorf, Jonas, Newhouse, Robin, Newman, Paul Richard, Ng, Chi Wing, Ng, Ying Wun Yvonne, Ngair, Badr-Eddine, Nguyen, Hoang Dai Nghia, Nickerson, Richard, 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Onofre, Antonio, Onyisi, Peter, Oreglia, Mark, Orellana, Gonzalo Enrique, Orestano, Domizia, Orlando, Nicola, Orr, Robert, O'Shea, Val, Osojnak, Lauren Melissa, Ospanov, Rustem, Otero Y Garzon, Gustavo, Otono, Hidetoshi, Philipp Sebastian Ott, Ottino, Gregory James, Ouchrif, Mohamed, Ouellette, Jeff, Ould-Saada, Farid, Owen, Mark Andrew, Owen, Rhys, Oyulmaz, Kaan Yuksel, Ozcan, Erkcan, Ozturk, Nurcan, Ozturk, Sertac, Pacey, Holly, Pacheco Pages, Andreu, Padilla Aranda, Cristobal, Padovano, Giovanni, Pagan Griso, Simone, Palacino, Gabriel, Palazzo, Alessandra, Palestini, Sandro, Pan, Jingjing, Pan, Tong, Panchal, Dev, Pandini, Carlo Enrico, Panduro Vazquez, William, Pandya, Hitarthi Deepak, Pang, Hao, Pani, Priscilla, Panizzo, Giancarlo, Paolozzi, Lorenzo, Papadatos, Constantine, Parajuli, Santosh, Paramonov, Alexander, Paraskevopoulos, Christos, Paredes Hernandez, Daniela Katherinne, Park, Tae Hyoun, Parker, Andy, Parodi, Fabrizio, Parrish, Elliot, Parrish, Victoria Alexis, Parsons, 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Williams, Daniel, Williams, Hugh, Williams, Sarah Louise, Willocq, Stephane, Wilson, Benjamin James, Windischhofer, Philipp, Winkel, Federico, Winklmeier, Frank, Winter, Benedict Tobias, Winter, Joshua Krystian, Wittgen, Matthias, Wobisch, Markus, Wolffs, Zef, Wollrath, Julian, Wolter, Marcin, Wolters, Helmut, Wongel, Alicia, Worm, Steven, Wosiek, Barbara Krystyna, Wozniak, Krzysztof Wieslaw, Wozniewski, Sebastian, Wraight, Kenneth Gibb, Wu, Chonghao, Wu, Jinfei, Wu, Minlin, Wu, Mengqing, Wu, Sau Lan, Wu, Xin, Wu, Yusheng, Wu, Zhibo, Wurzinger, Jonas, Wyatt, Terry, Wynne, Benjamin Michael, Xella, Stefania, Xia, Ligang, Xia, Mingming, Xiang, Jianhuan, Xie, Mingzhe, Xie, Xiangyu, Xin, Shuiting, Xiong, Junwen, Xu, Da, Xu, Hao, Xu, Lailin, Xu, Riley, Xu, Tairan, Xu, Yue, Xu, Zhongyukun, Xu, Zijun, Yabsley, Bruce Donald, Yacoob, Sahal, Yamaguchi, Yohei, Yamashita, Erika, Yamauchi, Hiroki, Yamazaki, Tomohiro, Yamazaki, Yuji, Yan, Jun, Yan, Siyuan, Yan, Zhen, Yang, Haijun, Yang, Hongtao, Yang, Siqi, Yang, Tianyi, Yang, Xiao, Yang, Xuan, Yang, Yi-Lin, Yang, Yifan, Yang, Zhe, Yao, Wei-Ming, Yap, Yee Chinn, Ye, Hanfei, Ye, Hua, Ye, Jingbo, Ye, Shuwei, Ye, Xinmeng, Yeh, Yoran, Yeletskikh, Ivan, Yeo, Beom Ki, Yexley, Melissa, Yin, Pengqi, Yorita, Kohei, Younas, Sulman, Young, Christopher, Young, Charlie, Yu, Yi, Yuan, Man, Yuan, Rui, Yue, Luzhan, Zaazoua, Mohamed, Zabinski, Bartlomiej Henryk, Zaid, Estifa'A, Zakareishvili, Tamar, Zakharchuk, Nataliia, Zambito, Stefano, Zamora Saa, Jilberto Antonio, Zang, Jiaqi, Zanzi, Daniele, Zaplatilek, Ota, Zeitnitz, Christian, Zeng, Hao, Zeng, Jiancong, Zenger, Todd, Zenin, Oleg, Zenis, Tibor, Zenz, Seth, Zerradi, Soufiane, Zerwas, Dirk, Zhai, Mingjie, Zhang, Bowen, Zhang, Dengfeng, Zhang, Jie, Zhang, Jinlong, Zhang, Kaili, Zhang, Lei, Zhang, Peng, Zhang, Rui, Zhang, Shuzhou, Zhang, Tingyu, Zhang, Xiangke, Zhang, Xueyao, Zhang, Yulei, Zhang, Yuwen Ebony, Zhang, Zhicai, Zhang, Zhiqing Philippe, Zhao, Haoran, Zhao, Pingchuan, Zhao, Tongbin, Zhao, Yuzhan, Zhao, Zhengguo, Zhemchugov, Alexey, Zheng, Jinchao, Zheng, Kai, Zheng, Xiangxuan, Zheng, Zhi, Zhong, Dewen, Zhou, Bing, Zhou, Hao, Zhou, Ning, Zhou, You, Zhu, Chengguang, Zhu, Junjie, Zhu, Yifan, Zhu, Yingchun, Zhuang, Xuai, Zhukov, Konstantin, Zhulanov, Vladimir, Zimine, Nikolai, Zinsser, Joachim, Ziolkowski, Michal, Zivkovic, Lidija, Zoccoli, Antonio, Zoch, Knut, Zorbas, Theodore, Zormpa, Olga, Zou, Wenkai, Zwalinski, Lukasz, Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Laboratoire des deux Infinis de Toulouse (L2IT), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), and ATLAS
- Subjects
electron ,lepton ,background ,p p, scattering ,photon ,FOS: Physical sciences ,anomaly ,ATLAS ,High Energy Physics - Experiment ,mass spectrum ,High Energy Physics - Experiment (hep-ex) ,machine learning ,CERN LHC Coll ,muon ,[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] ,TeV ,Particle Physics - Experiment - Abstract
Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140 fb$^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or $b$-jet and either one lepton ($e$, $\mu$), photon, or second light jet or $b$-jet in the anomalous regions. No significant deviations from the background hypotheses are observed., Comment: 32 pages in total, author list starting page 15, 4 figures, submitted to Phys. Rev. Lett. All figures including auxiliary figures are available at: http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2022-07
- Published
- 2023
50. Machine Learning and Digital Classical Chinese Texts: Collaboration between the UC Computing Platform and Peking University's Big-Data databases
- Author
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Hu, Minghui, Li, Xiao, Weekley, Jeffrey, Scholger, Walter, Vogeler, Georg, Tasovac, Toma, Baillot, Anne, Raunig, Elisabeth, Scholger, Martina, Steiner, Elisabeth, Centre for Information Modelling, and Helling, Patrick
- Subjects
Paper ,python ,Long Presentation ,machine learning ,text mining and analysis ,database creation ,digital research infrastructures development and analysis ,open access methods ,and analysis ,database ,management ,Asian studies - Abstract
The project develops an open-source machine learning platform for East Asian Studies, the first one in the United States.Our platform will be so versatile that humanists will not have to devote time to learning various digital tools. With our sample codes and tutorials, researchers can conduct computer-aided research.
- Published
- 2023
- Full Text
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