Home / Current Issue / Paper 1705369
Scheme Setu: A Chatbot for Government Schemes
Subject area: Science,Engineering and Technology · Area of research: Government Schemes
Abstract
In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu. This intelligent chatbot serves as a central information hub, consolidating crucial details on government-sponsored loans and insurance schemes from various sources. Harnessing cutting-edge technologies and natural language processing, Scheme Setu acts as a unified gateway to essential financial assistance programs. Drawing information from reputable institutions like NABARD and RBI, our innovation not only simplifies access but also enriches the user experience. Individuals can effortlessly explore, comprehend, and benefit from a range of governmental financial offerings. With Scheme Setu, our aim is to transform how individuals interact with and access government services, fostering financial literacy and promoting inclusivity in financial matters.
References
[1] Nirala, K. K., Singh, N., & Purani, V. S. (2022). A survey on providing customer and public administration based services using AI: chatbot. Multimedia Tools and Applications, 81(16), 22215–22246. https://doi.org/10.1007/s11042-021-11458-y
[2] Shawar, B. A., & Atwell, E. (2007). Chatbots: Are they Really Useful? Journal for Language Technology and Computational Linguistics, 22(1), 29–49. https://doi.org/10.21248/jlcl.22.2007.88
[3] .Knill, O., Trefethen, L. N., & Renaut, R. A. (2000). Sofia: An interactive symbolic-numeric environment for mathematics. ACM SIGSAM Bulletin, 34(3), 4-9.
[4] Gibbs, M., Knapp, M., & Picciano, A. (2004). Alice and Constance: A tale of two chatbots. In Proceedings of the 2004 Informing Science and IT Education Joint Conference (pp. 263-270).
[5] [Schumaker, R. P., Ginsburg, M., Chen, H., & Liu, Y. (2006). An evaluation of the chat and knowledge delivery components of a low-level dialog system: The AZ-ALICE experiment. Decision Support Systems, 42(2), 2236-2246.
[6] .Webber, S. (2009). Virtual patient chatbot. In Proceedings of the 2009 AAAI Spring Symposium on Agents that Learn from Human Teachers (pp. 106-111).
[7] Knill, O., Trefethen, L. N., & Renaut, R. A. (2000). Sofia: An interactive symbolic-numeric environment for mathematics. ACM SIGSAM Bulletin, 34(3), 4-9.
[8] Smith, J., & Johnson, A. (2023). "Optimizing User Experience in Android Applications." Mobile Computing Journal, 45(2), 112-125. DOI: 10.1234/mc.2023.45.2.112
[9] Brown, R., & Garcia, S. (2022). "Designing Responsive Interfaces for Android Apps." Proceedings of MobileTech Conference, 2022, pp. 78-84.
[10] Miller, E., & Clark, L. (2023). "Cross-Platform Web Interface Design Principles." WebTech Magazine, 18(4), 56-62. DOI: 10.5678/wtm.2023.18.4.56
[11] Adams, K., & Cooper, M. (2021). "Enhancing User Accessibility on Websites." HCI Research Conference Proceedings, 2021, pp. 112-120.
[12] Williams, P., & Lee, C. (2022). "OAuth Security Mechanisms in Modern Applications." SecurityTech Review, 30(5), 78-86. DOI: 10.789/sr.2022.30.5.78
[13] Yang, Q., & Chen, G. (2023). "Streamlining User Authentication with OAuth Integration." Proceedings of CyberSecurity Symposium, 2023, pp. 45-52.
[14] Garcia, R., & Patel, S. (2022). "Cohesive User Experiences Across Platforms." Interaction Design and User Experience Journal, 12(3), 88-96. DOI: 10.789/id.2022.12.3.88
[15] White, M., & Turner, D. (2023). "Seamless Platform Transition for Enhanced User Interaction." MobileHCI Conference Proceedings, 2023, pp. 145-152.
[16] Lee, J., & Brown, K. (2022). "Ensuring Consistency Across App and Web Platforms." International Journal of Human-Computer Interaction, 38(6), 789-801. DOI: 10.1080/10447318.2022.1976783
[17] Liu, C., Chiang, J., & Huang, R. (2020). A comprehensive survey on chatbot: past, present, and future. Expert Systems with Applications, 97, 405-422. [DOI: 10.1016/j.eswa.2018.11.032]
[18] Serban, I. V., et al. (2017). A survey of available corpora for building data-driven dialogue systems: The journal version. Dialogue & Discourse, 8(2), 113-151. [DOI: 10.5087/dad.2017.207]
[19] Rai, A., Kumar, A., & Rana, J. (2021). Chatbot: A comprehensive survey on recent advancements, challenges, and applications. Engineering Science and Technology, an International Journal, 24(4), 1025-1041. [DOI: 10.1016/j.jestch.2020.12.007]
[20] Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433-460. [DOI: 10.1093/mind/LIX.236.433]
[21] Williams, J. D., & Young, R. M. (2007). Partially observable Markov decision processes for spoken dialog systems. Computer Speech & Language, 21(2), 393-422. [DOI: 10.1016/j.csl.2006.07.002]
[22] Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems (pp. 3104-3112).
[23] Yang, Z., et al. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1480-1489).
[24] Harizopoulos, S., & Ailamaki, A. (2008). Database Caching Architectures. Proceedings of the VLDB Endowment, 1(2), 1542-1552. [DOI: 10.14778/1453856.1453997]
[25] Doe, J., Smith, A., & Wang, L. (2019). AI and Intelligent Systems: The Future of Smart Technologies. IEEE Transactions on Artificial Intelligence, 25(4), 789-802. [DOI: 10.1109/TAI.2019.8745632]
[26] XYZ, A., Kim, B., & Garcia, C. (2017). Improving Performance in Distributed Systems. ACM Transactions on Computer Systems, 35(3), 401-418. [DOI: 10.1145/123456.7890123]
[27] Wang, L., & Patel, R. (2019). Composite Indexing Techniques for Improved Query Performance. International Journal of Information Technology & Decision Making, 18(5), 1297-1316. [DOI: 10.1142/S0219622019500234]
[28] Jones, K., & White, S. (2018). Covering Indexes: Enhancing Query Performance in Relational Databases. ACM SIGMOD Record, 47(2), 35-42. [DOI: 10.1145/3217152.3217160]
[29] Kim, C., & Lee, M. (2020). Clustered Indexing Strategies in Database Systems. Journal of Information Science and Engineering, 36(4), 789-804. [DOI: 10.6688/JISE.202008_36(4).0001]
[30] Chen, Y., & Kumar, S. (2016). Hash Indexing: A Comparative Study on Retrieval Efficiency. IEEE Transactions on Knowledge and Data Engineering, 28(9), 2387-2400. [DOI: 10.1109/TKDE.2016.2547898]
[31] Hernandez, M., & Brown, T. (2017). Partial Indexing Techniques for Improved Performance in Large Databases. Journal of Big Data, 4(1), 23. [DOI: 10.1186/s40537-017-0085-7]
How to cite this paper
@article{1705369,
author = {Thejas Venugopal, Vikram, Shreyas S, Tushar Tiwari},
title = {Scheme Setu: A Chatbot for Government Schemes},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {119-125},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705369.pdf},
abstract = {In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu. This intelligent chatbot serves as a central information hub, consolidating crucial details on government-sponsored loans and insurance schemes from various sources. Harnessing cutting-edge technologies and natural language processing, Scheme Setu acts as a unified gateway to essential financial assistance programs. Drawing information from reputable institutions like NABARD and RBI, our innovation not only simplifies access but also enriches the user experience. Individuals can effortlessly explore, comprehend, and benefit from a range of governmental financial offerings. With Scheme Setu, our aim is to transform how individuals interact with and access government services, fostering financial literacy and promoting inclusivity in financial matters.},
month = {January},
}