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REVIEW 4 major objections 4 minor 111 references

Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read EHR text-mining pipeline predicts diabetes risk at 89% accuracy.

desk verdict The paper's headline result is unverifiable and likely contaminated by leakage: the feature list includes the outcome variable, and no temporal or patient-level split is described. read the letter →

arxiv 2412.03961 v1 pith:GTPHMOJF submitted 2024-12-05 cs.LG

Huadong Pang , Li Zhou , Yiping Dong , Peiyuan Chen , Dian Gu , Tianyi Lyu , Hansong Zhang This is my paper · ORCID
classification cs.LG
keywords electronichealthrecordsdiabetesriskpredictionBiLSTM-CRFXGBoostlogisticregressionnaturallanguageprocessingensemblelearningclinicaldecisionsupport
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Electronic health records mix structured lab values with unstructured clinical text, and most predictive models use only one of the two. This paper tries to show that a two-stage pipeline—a BiLSTM-CRF network that extracts medical entities from text, followed by an XGBoost-and-logistic-regression ensemble that classifies—uses both, and that the combination beats the individual models. On a cohort of 1,097 people from a Beijing health check center, the authors report the ensemble reaching 89% accuracy and 0.93 AUC, with BiLSTM-CRF alone at 82% accuracy. If the result holds, it would give clinicians a data-driven way to flag diabetes risk earlier from records they already have.

What carries the argument

The load-bearing mechanism is a two-stage pipeline. Stage one is a BiLSTM-CRF: a Bidirectional Long Short-Term Memory network (two LSTM passes, forward and backward, to capture surrounding context) topped by a Conditional Random Field layer, which constrains the output to coherent label sequences. It takes clinical text preprocessed with BIO tags (Begin/Inside/Outside entity markers) and word embeddings (dimension 300) and produces medical-entity tags. Stage two fuses those tags with 48 structured fields—demographics, vital signs, and lab results—and feeds them to XGBoost (a gradient-boosted decision tree ensemble) and logistic regression, whose predictions are combined into an ensemble. The CRF layer does the key work: it lets the model choose optimal entity-label sequences rather than independent per-token decisions, which the authors say is why BiLSTM-CRF beats plain BiLSTM.

What would settle it

Re-running the same pipeline on an independent EHR cohort from a different institution, using the same 48 features and an identical train/test split, would settle the claim: if the ensemble loses its clear margin over logistic regression alone and over Med-BERT on that external data, the reported 89% accuracy does not generalize.

Watch

Extended reading notes

Core claim

The central claim is that a hybrid architecture outperforms both pure deep-learning text models and pure structured-data classifiers on diabetes risk prediction. The paper's evidence is a set of comparisons: BiLSTM-CRF reaches 82% accuracy, 80.90% precision, 74.87% F1, and a Kappa of 0.6219, edging out Med-BERT (81%), 3D-CNN-SPP (80%), CNN-Bi-LSTM (77%), and BiLSTM (76%). When the extracted entities are merged with 48 structured EHR features, the ensemble of XGBoost and logistic regression reaches 89% accuracy, 90% precision, 87% recall, and 0.93 AUC, beating XGBoost alone (86%, AUC 0.91) and logistic regression alone (81%, AUC 0.87). The authors present these numbers as evidence that combining deep contextual text analysis with gradient-boosted and linear classifiers yields an improvement over traditional modeling.

Load-bearing premise

The reported superiority rests on the assumption that the 1,097 people who remain after filtering from a single Beijing health check center represent the wider patient population, so the accuracy numbers would transfer to other hospitals and patients.

Editorial extensions

If this is right

  • A clinical deployment could flag high-risk patients from routine check-up records without additional tests, because the required features are already in the EHR.
  • The entity tags produced in stage one double as interpretable output, letting clinicians see which symptoms or findings drove a patient's risk score.
  • The same pipeline could be retrained for other chronic conditions that leave traces in both structured labs and clinical text, such as hypertension or chronic kidney disease.
  • The reported margins suggest that adding an NLP front end to structured-data classifiers is worth the extra complexity, at least on records similar to this cohort.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not isolate how much the BiLSTM-CRF tags contribute beyond the 48 structured features; a natural test would run the same XGBoost-logistic ensemble on the structured fields alone and compare the drop.
  • Because the cohort is a single health check center with 1,097 people after exclusions, the absolute numbers are likely optimistic for broader populations; external validation at another institution would be the decisive check.
  • The comparison omits established clinical risk scores such as FINDRISC or Framingham-based diabetes scores; adding them would place the 89% accuracy claim in context with tools already in use.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a two-stage pipeline for diabetes risk prediction from electronic health records: a BiLSTM-CRF model extracts medical entities and temporal features from text, and an ensemble of XGBoost and logistic regression classifies the fused feature set. On a private Beijing health-check dataset reduced from 5,046 to 1,097 individuals, the authors report 82% accuracy for BiLSTM-CRF and 89% accuracy with 0.93 AUC for the ensemble, claiming superiority over LSTM, BiLSTM, CNN-Bi-LSTM, 3D-CNN-SPP, and Med-BERT.

Significance. If the reported results were valid, the pipeline would be a practical engineering contribution to EHR-based diabetes risk stratification. The manuscript does provide explicit hyperparameter settings for the main models (Sections 4.1.2 and 4.1.3), which is helpful for reproducibility. However, as written, the experiments do not establish the central claim of superior predictive performance: the outcome variable is not clearly separated from the features, the evaluation protocol does not respect the temporal structure of the data, and the reported numbers contain internal inconsistencies and no uncertainty quantification. These issues are load-bearing because they directly affect the accuracy and AUC values that the abstract and conclusions emphasize.

major comments (4)
  1. [Section 4.2] The dataset description lists 'diabetes status' as one of the 48 features, and no separate target variable is defined anywhere in Section 4. If 'diabetes status' is also the predicted outcome, the ensemble's 89% accuracy and 0.93 AUC in Table 2 could be obtained simply by reading the label from the input features. The manuscript must explicitly define the outcome, remove it from the feature list, and state the prediction horizon (e.g., whether the task is to predict incident diabetes in a future year from features recorded in prior years). Without this clarification, the headline performance numbers are not interpretable.
  2. [Section 4.1.4] The evaluation uses a random 80/20 split (for BiLSTM-CRF) and 5-fold cross-validation (for XGBoost and logistic regression) with no temporal split. Since the data are annual health checks from 2010 to 2015, random splitting can place the same patient's visits in both training and test sets, and using diagnostic biomarkers such as fasting blood glucose as predictors of a same-visit 'diabetes status' label reproduces the diagnostic criteria rather than forecasting future risk. A temporal split (e.g., train on 2010-2013, test on 2014-2015) or at least a strict per-patient split with a defined outcome time is required to support the paper's claims of 'risk prognosis' and 'early detection.'
  3. [Section 4.3 and Table 1] The text states that 'LSTM achieves the highest recall at 79.82%,' but Table 1 reports LSTM recall of 0.6942 and Med-BERT recall of 0.7982. This internal inconsistency, combined with the absence of confidence intervals or significance tests in Tables 1 and 2, means the stated conclusion that BiLSTM-CRF (or the ensemble) 'outperforms all other models' is not supported by the reported evidence. The table/text should be corrected, and interval estimates or pairwise significance tests should be provided for the accuracy, F1, and AUC comparisons.
  4. [Section 4.1.1] The manuscript says SMOTE is applied 'to balance class distribution,' but it does not state whether SMOTE is applied before or after the train/test split. If SMOTE runs on the full dataset before splitting, synthetic minority-class examples can appear in both training and test sets, biasing all reported metrics upward. This needs to be clarified, and ideally SMOTE should be applied inside each cross-validation fold or training split only.
minor comments (4)
  1. [Section 4.2] The section opens with placeholder text 'Font: Open Sans ; Font size; 10. Paragraph comes content here.' This unfinished content should be removed before any consideration for publication.
  2. [References] Reference [33] is displayed as '!!! INVALID CITATION !!!' and must be replaced with a valid citation.
  3. [Section 3.2] The LSTM description lists 'three types of gates: forget gate; output gate and input gate' but the equations also include a candidate cell update that is not described as a gate. Please reconcile the wording with the equations.
  4. [Section 5] The conclusion says the dataset consists of 'only 1000 clinical electronic health records,' but Section 4.2 reports 1,097 individuals; please make the numbers consistent.

Circularity Check

1 steps flagged · score 6.0 of 10

Outcome may be forced by construction: 'diabetes status' is listed as one of the 48 input features, while the paper's task is diabetes risk prediction, with no separate target or temporal split defined.

  1. self definitional [Section 4.2 (Dataset); Section 4.1.3-4.1.4 (Risk Prediction Model Construction and Evaluation)]
    "narrowing down to 1,097 individuals with 48 features, including ... fasting blood glucose, ... and diabetes status."

    The paper's stated goal is diabetes risk prediction, and the only diabetes outcome column named in the paper is 'diabetes status.' Section 4.2 explicitly places 'diabetes status' inside the 48-feature input list rather than defining a separate target vector. If 'diabetes status' is the label, then the XGBoost, logistic regression, and ensemble classifiers can reproduce the prediction by reading the input column itself. No temporal split, prediction horizon, or incident-versus-prevalent labeling rule is described in Section 4.1.4, which only mentions an 80/20 split and 5-fold cross-validation.

full rationale

The load-bearing circular issue is feature/label overlap: Section 4.2 lists 'diabetes status' among the 48 features used for the study, while the study claims to predict diabetes risk and reports classification metrics in Tables 1 and 2. Because the manuscript never defines a separate target variable, the prediction chain can reduce to copying the 'diabetes status' input column. This is a concrete, quotable self-definitional step and is not merely a data-leakage concern. I also checked the substantial self-citation presence (e.g., Refs. [49], [74], [93], [104]); those citations support peripheral related-work statements and are not load-bearing for the model derivation, so they do not raise the circularity score beyond the feature/label issue. Separately, Section 5's statement that 'this study used a public dataset' contradicts Section 4.2's description of data from a Beijing health check center, and the admitted small cohort size are validity/consistency concerns, not additional circularity. The SMOTE-before-split procedure could produce leakage, but leakage is an evaluation-protocol problem rather than derivation-level circularity, so it is not counted as a separate circular step.

Assumptions & free parameters 10 free parameters · 4 assumptions · 0 invented entities

The model depends on standard deep learning and gradient boosting formulations, a specific private dataset, BIO entity labels as features, and SMOTE balancing. None of these are independently verified in the paper, and the dataset is not available.

free parameters (10)
  • BiLSTM learning rate = 0.001
    Set in Section 4.1.2, not tuned on a held-out validation set.
  • LSTM hidden units = 128
    Set in Section 4.1.2 with no sensitivity analysis.
  • Number of BiLSTM layers = 2
    Set in Section 4.1.2 with no ablation.
  • Batch size = 32
    Set in Section 4.1.2 with no justification.
  • Dropout rate = 0.5
    Set in Section 4.1.4 to address overfitting.
  • XGBoost learning rate = 0.1
    Set in Section 4.1.3, a standard default.
  • XGBoost max depth = 5
    Set in Section 4.1.3, no search reported.
  • Logistic regression regularization C = 0.1
    Set in Section 4.1.3, no search reported.
  • Word embedding dimension = 300
    Set in Section 4.1.1.
  • Vocabulary size = 10000
    Set in Section 4.1.1.
assumptions (4)
  • standard math Standard LSTM, CRF, and XGBoost formulations are correct as presented.
    Section 3 cites equations (1)-(8) without proof or reference to formal sources.
  • domain assumption The private dataset from one Beijing health check center is representative of a broader population for diabetes risk prediction.
    Section 4.2 describes a single center with exclusion criteria, with no external validation.
  • ad hoc to paper BIO entity labels produced by BiLSTM-CRF are informative features for downstream diabetes risk classification.
    Section 4.1.1 builds a structured feature set with BIO tags, but no feature importance or ablation is shown.
  • domain assumption SMOTE balancing does not distort real-world prevalence in a way that invalidates accuracy comparisons.
    Section 4.1.1 applies SMOTE, but the class distribution is not reported.

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Cite this review

Pith. "Pith review of Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis." pith.science (2026). https://pith.science/paper/GTPHMOJF

@misc{pith2026241203961,
  author       = {Pith},
  title        = {Pith review of: Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GTPHMOJF}},
  note         = {Machine review of arXiv:2412.03961}
}
read the original abstract

In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The first phase of our approach involves employing BiLSTM-CRF to delve into the temporal characteristics and latent patterns present in EHR data. This method effectively uncovers the progression trends of diabetes, which are often hidden in the complex data structures of medical records. The second phase leverages the combined strength of XGBoost and Logistic Regression to classify these extracted features and evaluate associated risks. This dual approach facilitates a more nuanced and precise prediction of diabetes, outperforming traditional models, particularly in handling multifaceted and nonlinear medical datasets. Our research demonstrates a notable advancement in diabetes prediction over traditional methods, showcasing the effectiveness of our combined BiLSTM-CRF, XGBoost, and Logistic Regression model. This study highlights the value of data-driven strategies in clinical decision-making, equipping healthcare professionals with precise tools for early detection and intervention. By enabling personalized treatment and timely care, our approach signifies progress in incorporating advanced analytics in healthcare, potentially improving outcomes for diabetes and other chronic conditions.

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Reference graph

Works this paper leans on

111 extracted references · 57 canonical work pages

  1. [1]

    Health information systems, electronic medical records, and big data in global healthcare: Progress and challenges in oecd countries,

    F. Colombo, J. Oderkirk, and L. Slawomirski, "Health information systems, electronic medical records, and big data in global healthcare: Progress and challenges in oecd countries," Handbook of global health, pp. 1- 31, 2020

    2020

  2. [2]

    Making sense of big data in health research: towards an EU action plan,

    C. Auffray et al., "Making sense of big data in health research: towards an EU action plan," Genome medicine, vol. 8, pp. 1-13, 2016

    2016

  3. [3]

    Creating value in health care through big data: opportunities and policy implications,

    J. Roski, G. W. Bo-Linn, and T. A. Andrews, "Creating value in health care through big data: opportunities and policy implications," Health affairs, vol. 33, no. 7, pp. 1115-1122, 2014

    2014

  4. [4]

    Developing public policy to advance the use of big data in health care,

    A. Heitmueller, S. Henderson, W. Warburton, A. Elmagarmid, A. S. Pentland, and A. Darzi, "Developing public policy to advance the use of big data in health care," Health Affairs, vol. 33, no. 9, pp. 1523-1530, 2014

    2014

  5. [5]

    Big data for health,

    J. Andreu-Perez, C. C. Poon, R. D. Merrifield, S. T. Wong, and G.-Z. Yang, "Big data for health," IEEE journal of biomedical and health informatics, vol. 19, no. 4, pp. 1193-1208, 2015

    2015

  6. [6]

    Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper,

    C. Safran et al., "Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper," Journal of the American Medical Informatics Association, vol. 14, no. 1, pp. 1-9, 2007

    2007

  7. [7]

    Personalized care planning for diabetes: policy lessons from systematic reviews of consultation and self-management interventions,

    J. Graffy, S. Eaton, J. Sturt, and P. Chadwick, "Personalized care planning for diabetes: policy lessons from systematic reviews of consultation and self-management interventions," Primary Health Care Research & Development, vol. 10, no. 3, pp. 210-222, 2009

    2009

  8. [8]

    Data driven analytics for personalized healthcare,

    J. Hu, A. Perer, and F. Wang, "Data driven analytics for personalized healthcare," Healthcare Information Management Systems: Cases, Strategies, and Solutions, pp. 529-554, 2016

    2016

Show all 111 references
  1. [9]

    Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes,

    A. Z. Woldaregay et al., "Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes," Artificial intelligence in medicine, vol. 98, pp. 109-134, 2019

    2019

  2. [10]

    Enhancing healthcare quality in hospitals through electronic health records: A systematic review,

    P. Gatiti, E. Ndirangu, J. Mwangi, A. Mwanzu, and T. Ramadhani, "Enhancing healthcare quality in hospitals through electronic health records: A systematic review," Journal of Health Informatics in Developing Countries, vol. 15, no. 2, p. 1, 2021

    2021

  3. [11]

    Challenges and opportunities of big data in health care: a systematic review,

    C. S. Kruse, R. Goswamy, Y. J. Raval, and S. Marawi, "Challenges and opportunities of big data in health care: a systematic review," JMIR medical informatics, vol. 4, no. 4, p. e5359, 2016

    2016

  4. [12]

    A structured analysis to study the role of 12 machine learning and deep learning in the healthcare sector with big data analytics,

    J. Kumari, E. Kumar, and D. Kumar, "A structured analysis to study the role of 12 machine learning and deep learning in the healthcare sector with big data analytics," Archives of Computational Methods in Engineering, vol. 30, no. 6, pp. 3673-3701, 2023

    2023

  5. [13]

    Automatic News Generation and Fact-Checking System Based on Language Processing,

    X. Peng et al., "Automatic News Generation and Fact-Checking System Based on Language Processing," arXiv preprint arXiv:2405.10492, 2024

    2024 arXiv

  6. [14]

    AI and big data in healthcare: towards a more comprehensive research framework for multimorbidity,

    L. T. Majnarić, F. Babič, S. O’Sullivan, and A. Holzinger, "AI and big data in healthcare: towards a more comprehensive research framework for multimorbidity," Journal of Clinical Medicine, vol. 10, no. 4, p. 766, 2021

    2021

  7. [15]

    Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review,

    K. Kreimeyer et al., "Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review," Journal of biomedical informatics, vol. 73, pp. 14-29, 2017

    2017

  8. [16]

    Data processing and text mining technologies on electronic medical records: a review,

    W. Sun, Z. Cai, Y. Li, F. Liu, S. Fang, and G. Wang, "Data processing and text mining technologies on electronic medical records: a review," Journal of healthcare engineering, vol. 2018, 2018

    2018

  9. [17]

    Artificial intelligence approaches using natural language processing to advance EHR-based clinical research,

    Y. Juhn and H. Liu, "Artificial intelligence approaches using natural language processing to advance EHR-based clinical research," Journal of Allergy and Clinical Immunology, vol. 145, no. 2, pp. 463-469, 2020

    2020

  10. [18]

    Simplified Fixed Frequency Phase Shift Modulation for A Novel Single-Stage Single Phase Series-Resonant AC-DC Converter,

    H. Zou, M. Zhang, S. Farzamkia, and A. Q. Huang, "Simplified Fixed Frequency Phase Shift Modulation for A Novel Single-Stage Single Phase Series-Resonant AC-DC Converter," in 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), 2024: IEEE, pp. 1261-1268

    2024

  11. [19]

    Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm,

    R. Kamalraj, S. Neelakandan, M. R. Kumar, V. C. S. Rao, R. Anand, and H. Singh, "Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm," Measurement, vol. 183, p. 109804, 2021

    2021

  12. [20]

    Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia,

    S. Henrard, N. Speybroeck, and C. Hermans, "Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia," Haemophilia, vol. 21, no. 6, pp. 715-722, 2015

    2015

  13. [21]

    Utilizing home health care electronic health records for telehomecare patients with heart failure: a decision tree approach to detect associations with rehospitalizations,

    Y. Kang, M. D. McHugh, J. Chittams, and K. H. Bowles, "Utilizing home health care electronic health records for telehomecare patients with heart failure: a decision tree approach to detect associations with rehospitalizations," Computers, informatics, nursing: CIN, vol. 34, no...

    2016

  14. [22]

    Combining structured and unstructured data for predictive models: a deep learning approach,

    D. Zhang, C. Yin, J. Zeng, X. Yuan, and P. Zhang, "Combining structured and unstructured data for predictive models: a deep learning approach," BMC medical informatics and decision making, vol. 20, no. 1, pp. 1-11, 2020

    2020

  15. [23]

    Predicting cardiovascular health trajectories in time- series electronic health records with LSTM models,

    A. Guo, R. Beheshti, Y. M. Khan, J. R. Langabeer, and R. E. Foraker, "Predicting cardiovascular health trajectories in time- series electronic health records with LSTM models," BMC Medical Informatics and Decision Making, vol. 21, pp. 1-10, 2021

    2021

  16. [24]

    Occluded person re-identification with deep learning: A survey and perspectives,

    E. Ning, C. Wang, H. Zhang, X. Ning, and P. Tiwari, "Occluded person re-identification with deep learning: A survey and perspectives," Expert Systems with Applications, p. 122419, 2023

    2023

  17. [25]

    Implementation and use of disease diagnosis systems for electronic medical records based on machine learning: A complete review,

    J. Latif, C. Xiao, S. Tu, S. U. Rehman, A. Imran, and A. Bilal, "Implementation and use of disease diagnosis systems for electronic medical records based on machine learning: A complete review," IEEE Access, vol. 8, pp. 150489-150513, 2020

    2020

  18. [26]

    Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF,

    W. Gao, X. Zheng, and S. Zhao, "Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF," in Journal of physics: conference series, 2021, vol. 1848, no. 1: IOP Publishing, p. 012083

    2021

  19. [27]

    Research of clinical named entity recognition based on Bi-LSTM- CRF,

    Y. Qin and Y. Zeng, "Research of clinical named entity recognition based on Bi-LSTM- CRF," Journal of Shanghai Jiaotong University (Science), vol. 23, pp. 392-397, 2018

    2018

  20. [28]

    Deformation depth decoupling network for point cloud domain adaptation,

    H. Zhang, X. Ning, C. Wang, E. Ning, and L. Li, "Deformation depth decoupling network for point cloud domain adaptation," Neural Networks, vol. 180, p. 106626, 2024

    2024

  21. [29]

    Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed,

    J. Wang et al., "Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed," Journal of medical Internet research, vol. 22, no. 1, p. e16816, 2020

    2020

  22. [30]

    Research and application progress of Chinese medical knowledge graph,

    F. Yuanyuan and L. Zhongmin, "Research and application progress of Chinese medical knowledge graph," Journal of Frontiers of Computer Science & Technology, vol. 16, no. 10, p. 2219, 2022

    2022

  23. [31]

    A data-driven fault detection approach for Modular Reconfigurable Flying Array based on the Improved Deep Forest,

    Z. Yao, C. Yang, Y. Peng, X. Zhang, and F. Chen, "A data-driven fault detection approach for Modular Reconfigurable Flying Array based on the Improved Deep Forest," Measurement, vol. 206, p. 112217, 2023

    2023

  24. [32]

    PFVAE: a planar flow-based 13 variational auto-encoder prediction model for time series data,

    X.-B. Jin, W.-T. Gong, J.-L. Kong, Y.-T. Bai, and T.-L. Su, "PFVAE: a planar flow-based 13 variational auto-encoder prediction model for time series data," Mathematics, vol. 10, no. 4, p. 610, 2022

    2022

  25. [33]

    !!! INVALID CITATION !!! [Mehta, 2000 #37;Wang, 2021 #48]

    2000

  26. [34]

    Inducing decision trees with an ant colony optimization algorithm,

    F. E. Otero, A. A. Freitas, and C. G. Johnson, "Inducing decision trees with an ant colony optimization algorithm," Applied Soft Computing, vol. 12, no. 11, pp. 3615-3626, 2012

    2012

  27. [35]

    Decision tree (DT): a valuable tool for water resources engineering,

    M. Enayati, O. Bozorg-Haddad, M. Pourgholam-Amiji, B. Zolghadr-Asli, and M. Tahmasebi Nasab, "Decision tree (DT): a valuable tool for water resources engineering," in Computational Intelligence for Water and Environmental Sciences: Springer, 2022, pp. 201-223

    2022

  28. [36]

    Optimization of automated garbage recognition model based on resnet- 50 and weakly supervised cnn for sustainable urban development,

    Y. Zhou et al., "Optimization of automated garbage recognition model based on resnet- 50 and weakly supervised cnn for sustainable urban development," Alexandria Engineering Journal, vol. 108, pp. 415-427, 2024

    2024

  29. [37]

    Unsupervised K- means clustering algorithm,

    K. P. Sinaga and M.-S. Yang, "Unsupervised K- means clustering algorithm," IEEE access, vol. 8, pp. 80716-80727, 2020

    2020

  30. [38]

    HCFNN: High-order coverage function neural network for image classification,

    X. Ning, W. Tian, Z. Yu, W. Li, X. Bai, and Y. Wang, "HCFNN: High-order coverage function neural network for image classification," Pattern Recognition, vol. 131, p. 108873, 2022/11/01/ 2022, doi: https://doi.org/10.1016/j.patcog.2022.10887 3

    2022

  31. [39]

    Bayesianism—Its scope and limits,

    E. Sober, "Bayesianism—Its scope and limits," in Proceedings-British Academy, 2002, vol. 113: OXFORD UNIVERSITY PRESS INC., pp. 21- 38

    2002

  32. [40]

    Support vector machines based non- contact fault diagnosis system for bearings,

    D. Goyal, A. Choudhary, B. Pabla, and S. Dhami, "Support vector machines based non- contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, vol. 31, pp. 1275-1289, 2020

    2020

  33. [41]

    Lightgbm: A highly efficient gradient boosting decision tree,

    G. Ke et al., "Lightgbm: A highly efficient gradient boosting decision tree," Advances in neural information processing systems, vol. 30, 2017

    2017

  34. [42]

    CLU-CNNs: Object detection for medical images,

    Z. Li, M. Dong, S. Wen, X. Hu, P. Zhou, and Z. Zeng, "CLU-CNNs: Object detection for medical images," Neurocomputing, vol. 350, pp. 53-59, 2019

    2019

  35. [43]

    3D deep learning on medical images: a review,

    S. P. Singh, L. Wang, S. Gupta, H. Goli, P. Padmanabhan, and B. Gulyás, "3D deep learning on medical images: a review," Sensors, vol. 20, no. 18, p. 5097, 2020

    2020

  36. [44]

    Predicting patients' satisfaction with doctors in online medical communities: An approach based on XGBoost algorithm,

    Y. Xu, G. Wu, and Y. Chen, "Predicting patients' satisfaction with doctors in online medical communities: An approach based on XGBoost algorithm," Journal of Organizational and End User Computing (JOEUC), vol. 34, no. 4, pp. 1-17, 2022

    2022

  37. [45]

    An optimization-based diabetes prediction model using CNN and Bi- directional LSTM in real-time environment,

    P. Madan et al., "An optimization-based diabetes prediction model using CNN and Bi- directional LSTM in real-time environment," Applied Sciences, vol. 12, no. 8, p. 3989, 2022

    2022

  38. [46]

    3D-CNN-SPP: A patient risk prediction system from electronic health records via 3D CNN and spatial pyramid pooling,

    R. Ju et al., "3D-CNN-SPP: A patient risk prediction system from electronic health records via 3D CNN and spatial pyramid pooling," IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 2, pp. 247-261, 2020

    2020

  39. [47]

    Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction,

    L. Rasmy, Y. Xiang, Z. Xie, C. Tao, and D. Zhi, "Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction," NPJ digital medicine, vol. 4, no. 1, p. 86, 2021

    2021

  40. [48]

    Fleet rebalancing for expanding shared e-Mobility systems: A multi- agent deep reinforcement learning approach

    M. Luo, B. Du, W. Zhang, T. Song, K. Li, H. Zhu, M. Birkin, and H. Wen, "Fleet rebalancing for expanding shared e-Mobility systems: A multi- agent deep reinforcement learning approach", IEEE Transactions on Intelligent Transportation Systems, 2023, vol. 24, no. 4, pp. 3868--3881

    2023

  41. [49]

    Application of Deep Learning Models Based on EfficientDet and OpenPose in User-Oriented Motion Rehabilitation Robot Control

    M. Sui, L. Jiang, T. Lyu, H. Wang, L. Zhou, P. Chen, and A. Alhosain, "Application of Deep Learning Models Based on EfficientDet and OpenPose in User-Oriented Motion Rehabilitation Robot Control", Journal of Intelligence Technology and Innovation, 2024, vol. 2, no. 3, pp. 47--77

    2024

  42. [50]

    Classification of DNA Sequences: Performance Evaluation of Multiple Machine Learning Methods

    Y. Wang, V. Khandelwal, A. K. Das, and M. Anantram, "Classification of DNA Sequences: Performance Evaluation of Multiple Machine Learning Methods", in 2022 IEEE 22nd International Conference on Nanotechnology (NANO), 2022, pp. 333--336

    2022

  43. [51]

    Real-time monitoring of lower limb movement resistance based on deep learning

    Y. Liu, T. Lyu, and . others, "Real-time monitoring of lower limb movement resistance based on deep learning", Alexandria Engineering Journal, 2025, vol. 111, pp. 136--147

    2025

  44. [52]

    AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing

    T. Zhou, J. Zhao, Y. Luo, X. Xie, W. Wen, C. Ding, and X. Xu, "AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing", arXiv preprint arXiv:2407.05633, 2024

    2024 arXiv

  45. [53]

    Big data and machine learning in defence

    Y. Weng, "Big data and machine learning in defence", International Journal of Computer Science and Information Technology, 2024, vol. 16, no. 2, pp. 25--35

    2024

  46. [54]

    Construction and optimization of health behavior prediction 14 model for the elderly in smart elderly care

    Q. Guo and P. Chen, "Construction and optimization of health behavior prediction 14 model for the elderly in smart elderly care", 2024

    2024

  47. [55]

    Computational study of the role of counterions and solvent dielectric in determining the conductance of B-DNA

    Y. Wang, B. Demir, H. Mohammad, E. E. Oren, and M. Anantram, "Computational study of the role of counterions and solvent dielectric in determining the conductance of B-DNA", Physical Review E, 2023, vol. 107, no. 4, pp. 044404

    2023

  48. [56]

    Regional BDS satellite clock estimation with triple-frequency ambiguity resolution based on undifferenced observation

    X. Yang, S. Gu, X. Gong, W. Song, Y. Lou, and J. Liu, "Regional BDS satellite clock estimation with triple-frequency ambiguity resolution based on undifferenced observation", GPS Solutions, 2019, vol. 23, pp. 1--11

    2019

  49. [57]

    Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development

    Y. Zhou, Z. Wang, S. Zheng, L. Zhou, L. Dai, H. Luo, Z. Zhang, and M. Sui, "Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development", Alexandria Engineering Journal, 2024, vol. 108, pp. 415-- 427

    2024

  50. [58]

    TD3 Based Collision Free Motion Planning for Robot Navigation

    H. Liu, Y. Shen, C. Zhou, Y. Zou, Z. Gao, and Q. Wang, "TD3 Based Collision Free Motion Planning for Robot Navigation", arXiv preprint arXiv:2405.15460, 2024

    2024 arXiv

  51. [59]

    Reinforcement Learning with Communication Latency with Application to Stop-and-Go Wave Dissipation

    A. Richardson, X. Wang, A. Dubey, and J. Sprinkle, "Reinforcement Learning with Communication Latency with Application to Stop-and-Go Wave Dissipation", in 2024 IEEE Intelligent Vehicles Symposium (IV), 2024, pp. 1187--1193

    2024

  52. [60]

    Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees

    C. Wang, M. Sui, D. Sun, Z. Zhang, and Y. Zhou, "Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees", 2024, pp. 153– 159, doi: 10.1145/3677779.3677804

    2024

  53. [61]

    Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis

    L. Wang, Y. Hu, and Y. Zhou, "Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis", arXiv preprint arXiv:2408.12115, 2024

    2024 arXiv

  54. [62]

    Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning

    K. Li, J. Wang, X. Wu, X. Peng, R. Chang, X. Deng, Y. Kang, Y. Yang, F. Ni, and B. Hong, "Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning", arXiv preprint arXiv:2408.16633, 2024

    2024

  55. [63]

    Modeling and Simulation of DNA Origami based Electronic Read-only Memory

    A. De, H. Mohammad, Y. Wang, R. Kubendran, A. K. Das, and M. Anantram, "Modeling and Simulation of DNA Origami based Electronic Read-only Memory", in 2022 IEEE 22nd International Conference on Nanotechnology (NANO), 2022, pp. 385--388

    2022

  56. [64]

    Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm

    X. Tang, B. Long, and L. Zhou, "Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm", arXiv preprint arXiv:2411.06720, 2024

    2024 arXiv

  57. [65]

    Performance analysis of DNA crossbar arrays for high- density memory storage applications

    A. De, H. Mohammad, Y. Wang, R. Kubendran, A. K. Das, and M. Anantram, "Performance analysis of DNA crossbar arrays for high- density memory storage applications", Scientific Reports, 2023, vol. 13, no. 1, pp. 6650

    2023

  58. [66]

    LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting

    T. Li, M. Zhang, and Y. Zhou, "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting", arXiv preprint arXiv:2410.15286, 2024

    2024 arXiv

  59. [67]

    Leveraging artificial intelligence to enhance data security and combat cyber attacks

    Y. Weng and J. Wu, "Leveraging artificial intelligence to enhance data security and combat cyber attacks", Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2024, vol. 5, no. 1, pp. 392--399

    2024

  60. [68]

    Graphical Structural Learning of rs-fMRI data in Heavy Smokers

    Y. Gong, Q. Zhang, H. Zheng, Z. Liu, and S. Chen, "Graphical Structural Learning of rs-fMRI data in Heavy Smokers", arXiv preprint arXiv:2409.08395, 2024

    2024 arXiv

  61. [69]

    EITNet: An IoT- enhanced framework for real-time basketball action recognition

    J. Liu, X. Liu, M. Qu, and T. Lyu, "EITNet: An IoT- enhanced framework for real-time basketball action recognition", Alexandria Engineering Journal, 2025, vol. 110, pp. 567--578

    2025

  62. [70]

    Using Automated Vehicle Data as a Fitness Tracker for Sustainability

    X. Wang, S. Onwumelu, and J. Sprinkle, "Using Automated Vehicle Data as a Fitness Tracker for Sustainability", in 2024 Forum for Innovative Sustainable Transportation Systems (FISTS), 2024, pp. 1--6

    2024

  63. [71]

    Comprehensive Overview of Artificial Intelligence Applications in Modern Industries

    Y. Weng, J. Wu, T. Kelly, and W. Johnson, "Comprehensive Overview of Artificial Intelligence Applications in Modern Industries", arXiv preprint arXiv:2409.13059, 2024

    2024 arXiv

  64. [72]

    Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks

    S. Wang, R. Jiang, Z. Wang, and Y. Zhou, "Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks", Journal of Information and Computing, 2024, vol. 2, no. 2, pp. 34--63

    2024

  65. [73]

    DSEM-NeRF: Multimodal feature fusion and global-local attention for enhanced 3D scene reconstruction

    D. Liu, Z. Wang, and P. Chen, "DSEM-NeRF: Multimodal feature fusion and global-local attention for enhanced 3D scene reconstruction", Information Fusion, 2024, pp. 102752

    2024

  66. [74]

    Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion

    P. Chen, Z. Zhang, Y. Dong, L. Zhou, and H. Wang, "Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion", Journal of Intelligence Technology and Innovation, 2024, vol. 2, no. 3, pp. 19--46

    2024

  67. [75]

    Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning

    H. Zheng, Q. Zhang, Y. Gong, Z. Liu, and S. Chen, "Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning", arXiv preprint arXiv:2408.16068, 2024. 15

    2024 arXiv

  68. [76]

    Runtime monitoring of accidents in driving recordings with multi-type logic in empirical models

    Z. An, X. Wang, T. T. Johnson, J. Sprinkle, and M. Ma, "Runtime monitoring of accidents in driving recordings with multi-type logic in empirical models", in International Conference on Runtime Verification, 2023, pp. 376--388

    2023

  69. [77]

    Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

    K. Li, J. Chen, D. Yu, T. Dajun, X. Qiu, L. Jieting, S. Baiwei, Z. Shengyuan, Z. Wan, R. Ji, and . others, "Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments", arXiv preprint arXiv:2409.14972, 2024

    2024 arXiv

  70. [78]

    Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks

    J. Wang, Z. Wang, and G. Liu, "Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks", Alexandria Engineering Journal, 2024, vol. 109, pp. 1--10

    2024

  71. [79]

    Traffic smoothing via connected & automated vehicles: A modular, hierarchical control design deployed in a 100- cav flow smoothing experiment

    J. Lee, H. Wang, K. Jang, A. Hayat, M. Bunting, A. Alanqary, W. Barbour, Z. Fu, X. Gong, G. Gunter, and . others, "Traffic smoothing via connected & automated vehicles: A modular, hierarchical control design deployed in a 100- cav flow smoothing experiment", IEEE Control Syste...

    2024

  72. [80]

    A machine learning approach for accurate and real-time DNA sequence identification

    Y. Wang, M. Alangari, J. Hihath, A. K. Das, and M. Anantram, "A machine learning approach for accurate and real-time DNA sequence identification", BMC genomics, 2021, vol. 22, pp. 1--10

    2021

  73. [81]

    Performance evaluation of QUIC with BBR in satellite internet

    Y. Wang, K. Zhao, W. Li, J. Fraire, Z. Sun, and Y. Fang, "Performance evaluation of QUIC with BBR in satellite internet", in 2018 6th IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), 2018, pp. 195--199

    2018

  74. [82]

    Music genre classification with transformer classifier

    Y. Zhuang, Y. Chen, and J. Zheng, "Music genre classification with transformer classifier", in Proceedings of the 2020 4th international conference on digital signal processing, 2020, pp. 155--159

    2020

  75. [83]

    Research on empirical correction models of GPS Block IIF and BDS satellite inter-frequency clock bias

    X. Gong, S. Gu, Y. Lou, F. Zheng, X. Yang, Z. Wang, and J. Liu, "Research on empirical correction models of GPS Block IIF and BDS satellite inter-frequency clock bias", Journal of Geodesy, 2020, vol. 94, pp. 1--11

    2020

  76. [84]

    Image anomaly detection and prediction scheme based on SSA optimized ResNet50- BiGRU model

    Q. Wan, Z. Zhang, L. Jiang, Z. Wang, and Y. Zhou, "Image anomaly detection and prediction scheme based on SSA optimized ResNet50- BiGRU model", arXiv preprint arXiv:2406.13987, 2024

    2024 arXiv

  77. [85]

    Enhancing human pose estimation in sports training: Integrating spatiotemporal transformer for improved accuracy and real-time performance

    X. Xi, C. Zhang, W. Jia, and R. Jiang, "Enhancing human pose estimation in sports training: Integrating spatiotemporal transformer for improved accuracy and real-time performance", Alexandria Engineering Journal, 2024, vol. 109, pp. 144--156

    2024

  78. [86]

    CU- Net: a U-Net architecture for efficient brain- tumor segmentation on BraTS 2019 dataset

    Q. Zhang, W. Qi, H. Zheng, and X. Shen, "CU- Net: a U-Net architecture for efficient brain- tumor segmentation on BraTS 2019 dataset", arXiv preprint arXiv:2406.13113, 2024

    2019 arXiv

  79. [87]

    Accel-gcn: High-performance gpu accelerator design for graph convolution networks

    X. Xie, H. Peng, A. Hasan, S. Huang, J. Zhao, H. Fang, W. Zhang, T. Geng, O. Khan, and C. Ding, "Accel-gcn: High-performance gpu accelerator design for graph convolution networks", in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023, pp. 01- -09

    2023

  80. [88]

    MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training

    H. Peng, X. Xie, K. Shivdikar, M. A. Hasan, J. Zhao, S. Huang, O. Khan, D. Kaeli, and C. Ding, "MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training", in Proceedings of the 29th ACM International Conference on Architectural Support for Pro...

    2024

  81. [89]

    Robust domain generalization for multi-modal object recognition

    Y. Qiao, K. Li, J. Lin, R. Wei, C. Jiang, Y. Luo, and H. Yang, "Robust domain generalization for multi-modal object recognition", in 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 2024, pp. 392--397

    2024

  82. [90]

    The Design of Autonomous UAV Prototypes for Inspecting Tunnel Construction Environment

    Y. Dong, "The Design of Autonomous UAV Prototypes for Inspecting Tunnel Construction Environment", Journal of Intelligence Technology and Innovation, 2024, vol. 2, no. 3, pp. 1--18

    2024

  83. [91]

    Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

    G. Liu and B. Zhu, "Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering", International Journal of Computer Science and Information Technology, 2024, vol. 3, no. 1, pp. 219--226

    2024

  84. [92]

    Mix of Experts Language Model for Named Entity Recognition

    X. Chen, K. Li, T. Song, and J. Guo, "Mix of Experts Language Model for Named Entity Recognition", arXiv preprint arXiv:2404.19192, 2024

    2024 arXiv

  85. [93]

    TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA- LSTM Forecasting model

    S. Zheng, S. Liu, Z. Zhang, D. Gu, C. Xia, H. Pang, and E. M. Ampaw, "TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA- LSTM Forecasting model", Journal of Intelligence Technology and Innovation, 2024, vol. 2, no. 3, pp. 78--103

    2024

  86. [94]

    Automatic News Generation and Fact-Checking System Based on Language Processing

    X. Peng, Q. Xu, Z. Feng, H. Zhao, L. Tan, Y. Zhou, Z. Zhang, C. Gong, and Y. Zheng, "Automatic News Generation and Fact-Checking System Based on Language Processing", Journal of Industrial Engineering and Applied Science, 2024, vol. 2, no. 3, pp. 1--11. 16

    2024

  87. [95]

    Application of Deep Learning for Automatic Identification of Hazardous Materials and Urban Safety Supervision

    T. Yan, J. Wu, M. Kumar, and Y. Zhou, "Application of Deep Learning for Automatic Identification of Hazardous Materials and Urban Safety Supervision", Journal of Organizational and End User Computing (JOEUC), 2024, vol. 36, no. 1, pp. 1--20

    2024

  88. [96]

    GTA-Net: An IoT- integrated 3D human pose estimation system for real-time adolescent sports posture correction

    S. Yuan and L. Zhou, "GTA-Net: An IoT- integrated 3D human pose estimation system for real-time adolescent sports posture correction", Alexandria Engineering Journal, 2025, vol. 112, pp. 585--597

    2025

  89. [97]

    Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient

    T. Jiang, L. Liu, J. Jiang, T. Zheng, Y. Jin, and K. Xu, "Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient", arXiv preprint arXiv:2411.13885, 2024

    2024 arXiv

  90. [98]

    Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

    Y. Shen, H. Liu, C. Zhou, W. Wang, Z. Gao, and Q. Wang, "Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles", arXiv preprint arXiv:2406.04821, 2024

    2024 arXiv

  91. [99]

    Harnessing XGBoost for robust biomarker selection of obsessive-compulsive disorder (OCD) from adolescent brain cognitive development (ABCD) data

    X. Shen, Q. Zhang, H. Zheng, and W. Qi, "Harnessing XGBoost for robust biomarker selection of obsessive-compulsive disorder (OCD) from adolescent brain cognitive development (ABCD) data", in Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2...

    2024 doi

  92. [100]

    IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose

    F. Ren, C. Ren, and T. Lyu, "IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose", arXiv preprint arXiv:2411.12676, 2024

    2024 arXiv

  93. [101]

    Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach

    Z. Zhang, "Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach", Journal of Management Science and Operations, 2024, vol. 2, no. 3, pp. 16--43

    2024

  94. [102]

    Few-shot name entity recognition on stackoverflow

    X. Chen, K. Li, T. Song, and J. Guo, "Few-shot name entity recognition on stackoverflow", arXiv preprint arXiv:2404.09405, 2024

    2024 arXiv

  95. [103]

    Fortifying the global data fortress: a multidimensional examination of cyber security indexes and data protection measures across 193 nations

    Y. Weng, J. Wu, and . others, "Fortifying the global data fortress: a multidimensional examination of cyber security indexes and data protection measures across 193 nations", International Journal of Frontiers in Engineering Technology, 2024, vol. 6, no. 2, pp. 13--28

    2024

  96. [104]

    Optimized CNNs for Rapid 3D Point Cloud Object Recognition

    T. Lyu, D. Gu, P. Chen, Y. Jiang, Z. Zhang, H. Pang, L. Zhou, and Y. Dong, "Optimized CNNs for Rapid 3D Point Cloud Object Recognition", 2024

    2024

  97. [105]

    Intelligent design and optimization of exercise equipment based on fusion algorithm of YOLOv5-ResNet 50

    L. Wang, W. Ji, G. Wang, Y. Feng, and M. Du, "Intelligent design and optimization of exercise equipment based on fusion algorithm of YOLOv5-ResNet 50", Alexandria Engineering Journal, 2024, vol. 104, pp. 710-- 722

    2024

  98. [106]

    DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

    Z. Xu, D. Deng, Y. Dong, and K. Shimada, "DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles", in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 250--256

    2022

  99. [107]

    Multi-GNSS satellite clock estimation constrained with oscillator noise model in the existence of data discontinuity

    C. Shi, S. Guo, S. Gu, X. Yang, X. Gong, Z. Deng, M. Ge, and H. Schuh, "Multi-GNSS satellite clock estimation constrained with oscillator noise model in the existence of data discontinuity", Journal of Geodesy, 2019, vol. 93, pp. 515--528

    2019

  100. [108]

    Expected file-delivery time of DTN protocol over asymmetric space internetwork channels

    B. Cao, R. Wang, A. Sabbagh, S. Peng, K. Zhao, J. A. Fraire, G. Yang, and Y. Wang, "Expected file-delivery time of DTN protocol over asymmetric space internetwork channels", in 2018 6th IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), 2018,...

    2018

  101. [109]

    Dualvd: An adaptive dual encoding model for deep visual understanding in visual dialogue

    X. Jiang, J. Yu, Z. Qin, Y. Zhuang, X. Zhang, Y. Hu, and Q. Wu, "Dualvd: An adaptive dual encoding model for deep visual understanding in visual dialogue", in Proceedings of the AAAI conference on artificial intelligence, 2020, vol. 34, no. 07, pp. 11125--11132

    2020

  102. [110]

    Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence

    B. Zhu and G. Liu, "Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence", Academic Journal of Science and Technology, 2024, vol. 12, no. 3, pp. 12--15

    2024

  103. [111]

    Risk Analysis in Customer Relationship Management via QRCNN-LSTM and Cross- Attention Mechanism

    Y. Huang, J. Der Leu, B. Lu, and Y. Zhou, "Risk Analysis in Customer Relationship Management via QRCNN-LSTM and Cross- Attention Mechanism", Journal of Organizational and End User Computing (JOEUC), 2024, vol. 36, no. 1, pp. 1--22

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.