Enhancing Medicare Fraud Detection Through Machine Learning

Authors

  • Musallam Hasan Wahlan Jabri B.E.Students; Department of Electronics & Communication ISL Engineering College Hyderabad ,India. Author
  • Yahya Bin Ahmed Quraishi B.E.Students; Department of Electronics & Communication ISL Engineering College Hyderabad ,India. Author
  • Shaik Omer B.E.Students; Department of Electronics & Communication ISL Engineering College Hyderabad ,India. Author
  • Mr.Shaikh Azeemuddin HOD Department of ECE ISL Engineering College Hyderabad, India. Author

DOI:

https://doi.org/10.63665/cfbvdb77

Keywords:

Smart Agriculture, IoT, Robotics, Embedded Systems, Soil Monitoring, Autonomous Navigation

Abstract

Agriculture faces critical challenges such as labor shortages, water scarcity, and rising operational costs. To address these issues, this paper presents the design and implementation of a Smart Agricultural Robot Bulldog (SARDOG), an autonomous robotic system integrated with Internet of Things (IoT) technologies and advanced sensing mechanisms. The proposed system utilizes LiDARbased navigation, environmental sensors, and a robotic arm to perform multiple agricultural tasks such as soil analysis, crop monitoring, fruit picking, and automated irrigation. SARDOG aims to improve productivity, reduce manual labor, and enable precision farming through real-time data acquisition and intelligent decision-making. 

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References

[1] D. V. Lindberg and H. K. H. Lee, “Optimization under constraints by applying an asymmetric entropy measure,” J. Comput. Graph. Statist., vol. 24, no. 2, pp. 379–393, Jun. 2015.

[2] J. Smith, “The Impact of Smart Agriculture in Enhancing Crop Yields,” ResearchGate, 2020.

[3] L. Johnson and E. Brown, “Automation in Agriculture,” ResearchGate, 2019.

[4] R. Adams and D. Wilson, “Towards Sustainable Agriculture,” MDPI, 2018.

[5] “Agricultural Robots: The Next-Gen Farmers,” Electronics For You.

[6] “iot and wireless sensor network based autonomous farming robot,” IJCRT, 2023.

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Published

2026-04-28

How to Cite

Enhancing Medicare Fraud Detection Through Machine Learning. (2026). International Journal of Multidisciplinary Engineering In Current Research, 11(4s), 323-328. https://doi.org/10.63665/cfbvdb77