A Survey on Smart Expense Recorder using Machine Learning
- 1. Professor, The Department of CSE, HKBKCE, Bangalore, India
- 2. Student, The Department of CSE, HKBKCE, Bangalore, India
Description
ABSTRACT
Expense Tracker is a daily expenditure management system modeled to track of day-to-day expenses easily and effectively. It helps the user to track the expenditure on the regular basis, of all types of transactions using Machine Learning theorems based system, which eliminates the necessity for hardcopy results. It systematically stores and shows the record of all transactions done and easily helps the user to monitor all the payment data kept by it. An Android Application is to be developed which can read the data from the user’s transactions alerts/SMS and record it in its own database categorizing it automatically where the user has transacted the money. This will make it easier for the user to analyze where the user has spent money. User can extract the data of their expenditure, where he had transacted the money and can keep a track of his expense wisely.
Files
B7_A Survey on Smart Expense -Formatted Paper.pdf
Files
(513.3 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:8d013770f530c2fbed0b21158a680b1b
|
513.3 kB | Preview Download |
Additional details
References
- 1. Coin Keeper. Kim, A. (2013). International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 3, 03 2013.
- 2. Velmurugan, A., Mayan, J. A., Niranjana, P., & Francis, R. (2020, December). Expense manager application. In Journal of Physics: Conference Series (Vol. 1712, No. 1, p. 012039). IOP Publishing.
- 3. Eswar, V. O. S., Vinil, B., & Ankayarkanni, B. (2018, April). Integrated Collective Node Behavior Analysis with Onion Protocol for Best and Secured Data Transmission. In 2018 International Conference on Communication and Signal Processing (ICCSP) (pp. 0918-0921). IEEE.
- 4. Prasad, K. M., Goru, R. S. N., Vamsi, D., & Mayan, M. J. A. (2019, October). Automated payroll using GPS tracking and image capture. In IOP Conference Series: Materials Science and Engineering (Vol. 590, No. 1, p. 012026). IOP Publishing.
- 5. Gao, S., Wu, W., Lee, C. H., & Chua, T. S. (2006). A maximal figure-of-merit (MFoM)-learning approach to robust classifier design for text categorization. ACM Transactions on Information Systems (TOIS), 24(2), 190-218.
- 6. Klinkenberg, R., & Joachims, T. (2000, June). Detecting concept drift with support vector machines. In ICML (pp. 487-494).
- 7. Gómez Hidalgo, J. M., Bringas, G. C., Sánz, E. P., & García, F. C. (2006, October). Content based SMS spam filtering. In Proceedings of the 2006 ACM symposium on Document engineering (pp. 107-114).
- 8. Popovac, M., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2018, November). Convolutional neural network based SMS spam detection. In 2018 26th Telecommunications Forum (TELFOR) (pp. 1-4). IEEE.
- 9. Gupta, M., Bakliwal, A., Agarwal, S., & Mehndiratta, P. (2018, August). A comparative study of spam SMS detection using machine learning classifiers. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-7). IEEE.
- 10. Deepak, N. R., & Balaji, S. (2016, April). Uplink Channel Performance and Implementation of Software for Image Communication in 4G Network. In Computer Science On-line Conference (pp. 105-115). Springer, Cham.