Abstract
Growth of Artificial Intelligence and its rational thought “doing things right” makes handling fraudulent detection practices by implementing Machine Learning and Deep Learning methods. The detection needs to analyze regular patterns with its anomalies that are easy and speedy manner by machine than human cognitive process. The humans perceive and predict suspicious situation but machines analyze and detect. Analyzing regular patterns to anomalies enable machine to detect rationally than human inspections in various data. Hence human cognition capabilities incorporating in machine is trend of technology to prevent fraudulent practice. This work explores re-engineering on discovering fraudulent practice in dispensing unit. Initially short delivering is established, understand dispensing unit in the retail outlet of Petroleum products in responding to short volume, which is used for fraudulent practice and triggering methods. Then explored concepts are structured as theory for the short delivery which is to propose the secure system to prevent fraud.
Supported by Legal Metrology, Telangana.
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Acknowledgements
We would like to acknowledge Legal Metrology Departments from Telangana and Andra Pradesh states for their support and opportunity to work on Fuel Dispensing pump outlet system fraud analysis by understanding thee entire system functionality. We would also like to acknowledge the Mr. Ajay kumar BPCL, Mumbai willing to collaborate and opportunity for the University students to work in the area of Machine learning and Deep learning methods to explored practical observation data. Finally acknowledgement to School of Computer and Information Sciences, University of Hyderabad for environment to work in this domain with resource support.
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Vimal Babu, U., Nagamani, M., Raju, S., Rama Krishna, M. (2021). Fraudulent Practices in Dispensing Units and Remedies. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_8
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