Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01111
Number of page(s) 13
DOI https://doi.org/10.1051/matecconf/202439201111
Published online 18 March 2024
  1. Sisko AM, Keehan SP, Poisal JA, Cuckler GA, Smith SD, Madison AJ, et al. National health expenditure projections, 2018–27: economic and demographic trends drive spending and enrollment growth. Health Af. 2019;38(3):491–501. [CrossRef] [Google Scholar]
  2. Cubanski J, Neuman T, Freed M. The facts on Medicare spending and fnancing. Washington, DC: Kaiser Family Foundation; 2018. [Google Scholar]
  3. Morris L. Combating fraud in health care: an essential component of any cost containment strategy. Health Af. 2009;28(5):1351–56. [CrossRef] [Google Scholar]
  4. China Medical Security Bureau. Statistical bulletin on the development of China’s medical security business in 2020. 2020. http://www.nhsa.gov.cn/art/2021/6/8/art_7_5232.html. Accessed 20 Feb 2021. [Google Scholar]
  5. BANK OF CHINA. Bank of China Foreign Exchange Rates. 2020. https://www.boc.cn/sourcedb/whpj/. Accessed 8 Feb 2020. [Google Scholar]
  6. Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K. Inductive representation learning on temporal graphs. 2020. arXiv preprint arXiv:2002.07962. [Google Scholar]
  7. Fraud detection in health insurance using data mining techniques: A case study”Authors: Oludayo O. Olugbara, Richard Seglah, Phumlani MpanganeJournal: Expert Systems with Applications, 2017 [Google Scholar]
  8. A survey of data mining techniques in the detection of healthcare fraud”Authors: Reda Alhajj, Mohamad I. AljaaidiJournal: Journal of King Saud University – Computer and Information Sciences, 2014 [Google Scholar]
  9. Healthcare fraud detection: A survey and a clustering model”Authors: M. Zubair Baig, Mohiuddin Ahmed, Sherali Zeadally, et al.Journal: Journal of King Saud University – Computer and Information Sciences, 2018 [Google Scholar]
  10. Fraud Detection in Health Insurance Claims Data: A Hybrid Approach”Authors: M. Zubair Baig, Sherali Zeadally, A. Alanazi, et al.Journal: IEEE Access, 2018 [Google Scholar]
  11. Fraud detection in healthcare insurance claims: Review”Authors: Deepak L. Bhagat, Ajay R. DaniJournal: Journal of King Saud University – Computer and Information Sciences, 2019 [Google Scholar]
  12. Data Mining Techniques in Fraud Detection: A Healthcare Perspective”Authors: Asha Rani, Meenakshi TripathiJournal: Procedia Computer Science, 2015 [Google Scholar]
  13. Syed Umar, Yerragudipadu Subbarayudu, K. Kiran Kumar, N. Bashwanth, “Designing of Dynamic Re-clustering Leach Protocol for Calculating Total Residual Time and Performance”,International Journal of Electrical and Computer Engineering (IJECE)Vol.7, No.3, June2017, pp. 1286~1292ISSN: 2088-8708, DOI: 10.11591/ijece.v7i3.pp1286-1292 [Google Scholar]
  14. Yerragudipadu Subba Rayudu, R M Noorullah and C Praveen Kumar, Scribble Legalization Cryptographic Aspect Based On Data Access Control For Steam Count, VOL. 13, NO. 8, APRIL 2018 ISSN 1819-6608, “ARPN Journal of Engineering and Applied Sciences” ©2006-2018 Asian Research Publishing Network (ARPN). [Google Scholar]
  15. Subbarayudu Y, Reddy R.O. Anjaiah, P. “A study on user mobility in device to device (D2D) networks through distrubted catching”, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017.2018 DOI: 10.1109/ICPCSI.2017.8391798 EID: 2-s2.0-85050095042, URL: http://www.scopus.com/inward/record.url?Eid=2-s2.0-85050095042&partnerid=MN8TOARS [Google Scholar]
  16. Subbarayudu Y. Rakesh, M. Lingappa, E. Umar, S.,” Overview of the new bioinformatics virus goes from the front of next generation sequencing in genomics based on datamining”, Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems,2018, DOI: 10.1109/ICCONS.2017.8250670,EID: 2-s2.0-85047447491, URL:Http://www.scopus.com/inward/record.url?Eid=2-s2.0-85047447491&partnerid=MN8TOARS [Google Scholar]
  17. “Subbarayudu Y.” “Patil, S.” “ Ramyasree, B.” “ Praveen Kumar, C.” “Geetha, G”, Assort-EHR graph based semi-supervised classification algorithm for mining health records, Journal of Advanced Research in Dynamical and Control Systems 2017 . [Google Scholar]
  18. Umar, S., Sridevi, G., Subbarayudu Y., Nath, N.Y. “Datamining based multimode approach for estimating the risk under heart failure cases”, Journal of Theoretical and Applied Information Technology Volume 95, Issue 16, 31 August 2017, Pages 3879-388 [Google Scholar]
  19. Yerragudipadu subbarayudu, alladi Sureshbabu “Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance” Expert Clouds and Applications, 16 July 2021, DOI: https://doi.org/10.1007/978-981-16-2126-0_38Springer, Singapore Print ISBN 978-981-16-2125-3 Online ISBN 978-981-16-2126-0 [Google Scholar]
  20. Yerragudipadu Subbarayudu, Adithi Soppadandi, Shreya Vyamasani and Supriya Bandanadam 1, The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms, E3S Web of Conferences 391, 01057 (2023) https://doi.org/10.1051/e3sconf/202339101057 ICMED-ICMPC 2023. [CrossRef] [EDP Sciences] [Google Scholar]
  21. Subbarayudu Yerragudipadu, Vijendar Reddy Gurram, Navya Sri Rayapudi, Bhavana Bingi, Likhitha Gollapalli1 and Ukritha peddapatlolla, An Efficient Novel Approach on Machine Learning Paradigmsfor Food Delivery Company through Demand Forecastıng in societal community, E3S Web of Conferences 391, 01089 (2023) https://doi.org/10.1051/e3sconf/202339101089 ICMED-ICMPC 2023. [CrossRef] [EDP Sciences] [Google Scholar]
  22. Yerragudipadu Subbarayudu, G Vijendar Reddy, M Vamsi Krishna Raj, K Uday, MD Fasiuddin, and P Vishal, An efficient novel approach to E-commerce retail price optimization through machine learning, E3S Web of Conferences 391, 01104 (2023) https://doi.org/10.1051/e3sconf/202339101104 ICMED-ICMPC 2023. [CrossRef] [EDP Sciences] [Google Scholar]
  23. Subbarayudu, Y., Sureshbabu, A. (2023). A distributed densely connected convolutional network approach for enhanced recognition of health-related topics: A societal analysis case study. Ingénierie des Systèmes d’Information, Vol. 28, No. 3, pp. 677-684. https://doi.org/10.18280/isi.280317 [Google Scholar]
  24. P. Gopal Krihsna, Yerragudipadu Subbarayudu, S. Sai Siva Kumar, D. Naveen, Abhishek Srivastava and K. Thangamani “IoT Sensor-based sustainable smart home management for human needs through Micro Controller” Published online: 06 October 2023 DOI: https://doi.org/10.1051/e3sconf/202343001079 [Google Scholar]
  25. P. Gopal Krihsna, Yerragudipadu Subbarayudu, S. Sai Siva Kumar, D. Naveen, Abhishek Srivastava and K. Thangamani IoT Sensor-based sustainable smart home management for human needs through Micro Controller Published online: 06 October 2023 DOI: https://doi.org/10.1051/e3sconf/202343001079 [Google Scholar]
  26. P. Gopal Krihsna, Yerragudipadu Subbarayudu, K. Mythili Rao, V. Jyoshna, Jumaid Aman and G. Vijendar Reddy [Google Scholar]
  27. An Efficient, Novel, and Sustainable IoT-Based Approach for Attendance Detection through RFID Module and IR Sensor Published online: 06 October 2023 DOI: https://doi.org/10.1051/e3sconf/202343001096 [Google Scholar]
  28. Subbarayudu Yerragudipadu, Alladi Sureshbabu,”The Evaluation of Distributed topic models for recognition of health-related topics in social media through Machine Learning Paradigms” International Journal of Intelligent Systems and Applications in Engineering (IJISAE), https://ijisae.orgISSN:2147-6799,2023 [Google Scholar]

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