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All issues Volume 399 (2023) E3S Web Conf., 399 (2023) 01017 References
Open Access
Issue
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
Article Number 01017
Number of page(s) 9
Section Electronics and Electical Engineering
DOI https://doi.org/10.1051/e3sconf/202339901017
Published online 12 July 2023
  1. Autcharaporn Sukperm, Polapat Rojnuckarin, Benjaporn Akkawat, Vera Saing, ‘Automatic Diagnosis of Venous Thromboembolism Risk based on Machine Learnin”, 2021 IEEE International IOT, Electronics and MechatronicsConference (IEMTRONICS) | 978-1-6654-4067-7/21/$31.00 © 2021 IEEE DOI: 10.1109/IEMTRONICS52119.2021.9422638. [Google Scholar]
  2. Arulraja K., Guhan R., Ganeshkumar V., Gokulnath A. J., Dr. S. Vijayalakshmi, M.E., Ph.D, ‘The Prediction and Prevention of Varicose vein using Raspberry pi’, 2020 IJCRT | Volume 8, Issue 7 July 2020 | ISSN: 2320-2882. [Google Scholar]
  3. Castaneda D., Esparza A., Ghamari M., Soltanpur C., and Nazeran D., ‘A review on wearable photoplethysmography sensors and their potential future applications in health care’ Int. J. Biosens. Bioelectron., vol. 4, no. 4, pp. 195–202, 2018, DOI: 10.15406/ijbsbe.2018.04.00125 [Google Scholar]
  4. Gregoire Le Gal, Marc Righini, M.D., Pierre-Marie Roy, ‘Prediction of Pulmonary Embolism in the Emergency Department: The Revised Geneva Score | Annals of Internal Medicine’ https://www.acpjournals.org/doi/full/10.7326/0003-4819-144-3-200602070-00004 (accessed Jul. 08, 2020). [Google Scholar]
  5. Jing Liu, Bryan Yan, Shih-Chi Chen, Yuan-Ting Zhang, Fellow, Charles Sodini, Fellow, and Ni Zhao, ‘Non-Invasive Capillary Blood Pressure Measurement Enabling Early Detection and Classification of Venous Congestion’: DOI: 10.1109/JBHI.2021.3055760, IEEE Journal of Biomedical and Health Informatics. [Google Scholar]
  6. Reshma S., Senthil Kumar V., Varsha V., ‘Varicose Veins Patient Monitoring and Automated Treatment’, Volume 2, Issue 3, May 2022 (IJARSCT) [Google Scholar]
  7. Ruizong Zhu, Huiping Niu, Ningning Yin, Tianjiao Wu, Yapei Zhao, ‘Analysis of Varicose Veins of Lower Extremities Based on Vascular Endothelial Cell Inflammation Images and Multi-Scale Deep Learning’, December 16, 2019, Digital Object Identifier DOI: 10.1109/ACCESS.2019.2954708. [Google Scholar]
  8. Y.-W. Chi, R. Lin, K.-H. Tseng, and B. Durbin-Johnson, ‘Effect of subsurface pressure on the interface pressure measurement in an in vitro experiment’ Phlebology, vol. 35, no. 2, pp. 134–138, Mar. 2020, DOI: 10.1177/0268355519857627. [CrossRef] [PubMed] [Google Scholar]
  9. Z. Zhang, Z. Zhu, B. Bazor, S. Lee, Z. Ding, and T. Pan, “FeetBeat: A Flexible Iontronic Sensing Wearable Detects Pedal Pulses and Muscular Activities,” IEEE Trans. Biomed. Eng., vol. 66, no. 11, pp. 3072–3079, Nov. 2019, DOI: 10.1109/TBME.2019.2900224. [Google Scholar]
  10. Zhichao Zhang, Ruoyu Zhang, Chi-Wei Chang, Yaojun Guo, ‘iWRAP: A Theranostic Wearable Device with Real-Time Vital Monitoring and Auto-Adjustable Compression Level for Venous Thromboembolism’, DOI: 10.1109/TBME.2021.3054335, IEEE Transactions on Biomedical Engineering. [Google Scholar]
  11. Rajesh G., Raajini X.M., Sagayam K.M., Dang H., (2020), “A statistical approach for high order epistasis interaction detection for prediction of diabetic macular edema”, Informatics in Medicine Unlocked, Vol. 20. DOI: 10.1016/j.imu.2020.100362 [CrossRef] [Google Scholar]
  12. Kohila S., Malliga G.S., (2017), “Classification of the Thyroiditis based on characteristic sonographic textural features and correlated histopathology results”, 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016, pp. 305–309. DOI: 10.1109/SIPROCESS.2016.7888273 [Google Scholar]
  13. Umapathy K., Balaji V., Duraisamy V., Saravanakumar S.S., (2015), “Performance of wavelet based medical image fusion on FPGA using high level language C”, Jurnal Teknologi, Vol. 76, no. 12, pp. 105–109. DOI: 10.11113/jt.v76.5888 [CrossRef] [Google Scholar]
  14. Kavitha K.V.N., Ashok S., Imoize A.L., Ojo S., Selvan K.S., Ahanger T.A., Alhassan M., (2022), “On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals”, Journal of Healthcare Engineering, Vol. 2022, no., pp.-. DOI: 10.1155/2022/8928021 [PubMed] [Google Scholar]
  15. Vani R.K.K., Sowmya B., Kumar S.R.S., Babu G.N.K.S., Reena R., (2022), “An adaptive fuzzy neuro inference system for classification of ECG CardiacArrthymias”, AIP Conference Proceedings, Vol. 2393, no., pp.-. DOI: 10.1063/5.0079821 [Google Scholar]
  16. Ms. Ritika Dhabalia, Ms. Kritika Dhabalia. (2012). An Intelligent Auto- Tracking Vehicle. International Journal of New Practices in Management and Engineering, 1(02), 08–13. [CrossRef] [Google Scholar]
  17. Mr. Dharmesh Dhabliya. (2012). Intelligent Banal type INS based Wassily chair (INSW). International Journal of New Practices in Management and Engineering, 1(01), 01–08. [CrossRef] [Google Scholar]
  18. Prof. Arun Pawar, Mr. Dharmesh Dhabliya. (2018). Intelligent Modulation Recognition System and its Implementation using MATLAB. International Journal of New Practices in Management and Engineering, 7(01), 08–14. [Google Scholar]
  19. Swetha, A., Lakshmi, M. S., & Kumar, M. R. (2022). Chronic Kidney Disease Diagnostic Approaches using Efficient Artificial Intelligence methods. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 254 [Google Scholar]
  20. Thulasi, M. S., Sowjanya, B., Sreenivasulu, K., & Kumar, M. R. (2022). Knowledge Attitude and Practices of Dental Students and Dental Practitioners Towards Artificial Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 248–253. [Google Scholar]

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