Cited by A hybrid FSRF model based on regression algorithm for diabetes medical expense prediction

DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction Using IoT Network DOI

A. Yashudas,

Dinesh Gupta,

G. C. Prashant

et al.

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(9), P. 14539 - 14547

Published: March 14, 2024

The Internet of Things (IoTs)-based remote healthcare applications provide fast and preventative medical services to the patients at risk. However, predicting heart disease is a complex task, diagnosis results are rarely accurate. To address this issue, novel Recommendation System for Cardiovascular Disease (CVD) Prediction Using IoT Network (DEEP-CARDIO) has been proposed providing prior diagnosis, treatment, dietary recommendations cardiac diseases. Initially, physiological data collected from remotely by using four biosensors, such as ECG sensor, pressure pulse glucose sensor. An Arduino controller receives sensors predict diagnose disease. A CVD prediction model implemented bidirectional-gated recurrent unit (BiGRU) attention model, which diagnoses classifies into five available cardiovascular classes. recommendation system provides physical based on classified data, via user mobile application. performance DEEP-CARDIO validated Cloud Simulator (CloudSim) real-time Framingham's Statlog dataset. DEEP CARDIO method achieves an overall accuracy 99.90%, whereas MABC-SVM, HCBDA, MLbPM methods achieve 86.91%, 88.65%, 93.63%, respectively.

Language: Английский

Citations

Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds DOI

Dan Chang, Congjun Rao, Xinping Xiao

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 86, P. 101518 - 101518

Published: Feb. 24, 2024

Language: Английский

Citations

Research on glass-forming ability based on transformer and tabular data transformation DOI

Yuancheng Lin,

Yongchao Liang, Qian Chen

et al.

Journal of Non-Crystalline Solids, Journal Year: 2025, Volume and Issue: 652, P. 123416 - 123416

Published: Jan. 27, 2025

Language: Английский

Citations

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis DOI

Congjun Rao, Qifan Huang, Lin Chen

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(20), P. 57460 - 57480

Published: March 25, 2023

Language: Английский

Citations

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review DOI

Lin Chen,

Ting Dong,

Jin Peng

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(11), P. 2530 - 2530

Published: May 31, 2023

In recent years, there have been frequent cases of impact on the stable development supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, coping techniques now face wholly novel requirements a result escalating level global uncertainty. Although significant literature applies uncertainty analysis optimization modeling (UAO) to study management (SCM) under uncertainty, is lack systematic review research classification. Therefore, in this paper, 121 articles published 44 international academic journals between 2015 2022 are extracted from Web Science database reviewed using Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA). Bibliometric CiteSpace software used identify current developments field summarize characteristics hot topics. selected classified analyzed author name, year publication, application area, country, purposes, methods, gaps contributions, results, comprehensively evaluate SCM UAO. We find that UAO widely especially decision-making, where it common practice abstractly model decision problem obtain scientific results. This hopes provide an important valuable reference future Future could combine theory with segments (e.g., emergency resilience security management), behavioral factors, big data technologies, artificial intelligence, etc.

Language: Английский

Citations

Impact of the introduction of marketplace channel on e‐tailer's logistics service strategy DOI

Lin Chen, Ting Dong, Guofang Nan

et al.

Managerial and Decision Economics, Journal Year: 2023, Volume and Issue: 44(5), P. 2835 - 2855

Published: Feb. 28, 2023

Abstract We recently observe that some giant e‐tailers (e.g., Amazon and JD.com ) introduce marketplace channels by allowing third‐party sellers to sell products through their platforms. Logistics service acts as a vital role in the operation of online In practice, platform can outsource logistics providers (named strategy) or establish self‐run network distribute strategy). This paper investigates e‐tailer's strategy presence channel's introduction. reveal cost plays major determining optimal strategy. The provides low platform, which results higher consumer surplus comparison with Further, we find when is low, prefers channel. Finally, our show under strategy, benefits from channel an intermediate commission fee discount factor. Based on game‐theoretic models, managerial insights are suggested for retailers choose introducing

Language: Английский

Citations

Imbalanced customer churn classification using a new multi-strategy collaborative processing method DOI

Congjun Rao, Y. Xu, Xinping Xiao

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123251 - 123251

Published: Jan. 23, 2024

Language: Английский

Citations

Joint optimization on green investment and contract design for sustainable supply chains with fairness concern DOI

Lin Chen, Hui Shen, Qiurui Liu

et al.

Annals of Operations Research, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 28, 2024

Language: Английский

Citations

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm DOI

Hongyu Chen,

Qiping Geoffrey Shen,

Mirosław J. Skibniewski

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102705 - 102705

Published: Sept. 1, 2024

Language: Английский

Citations

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series DOI

Shuhao Mei, Xin Li, Yuxi Zhou

et al.

npj Systems Biology and Applications, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 15, 2025

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based subtle data patterns. In this study, we introduce novel deep learning-based approach, DeepSpiro, aimed the early prediction of risk. DeepSpiro consists four key components: SpiroSmoother for stabilizing Volume-Flow curve, SpiroEncoder capturing volume variability-pattern through patches varying lengths, SpiroExplainer integrating heterogeneous and explaining predictions attention, SpiroPredictor disease undiagnosed high-risk patients patch concavity, with horizons 1–5 years, or even longer. Evaluated UK Biobank dataset, achieved an AUC 0.8328 detection demonstrated strong predictive performance (p-value < 0.001). summary, can effectively predict long-term progression disease.

Language: Английский

Citations