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A.Nishanandhini, K.Elamparithi, K.Hariharan, V.Lokesh
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Page No: 1 - 7
Abstract : Government schemes encompass diverse areas such as education, healthcare, agriculture, social welfare, and infrastructure. However, a lack of awareness and difficulty in accessing accurate information often prevents individuals from availing of these benefits. To address these challenges, this project proposes the development of a Natural Language Processing (NLP)-based chatbot designed to provide seamless access to information about Tamil Nadu government schemes. The chatbot leverages advanced NLP frameworks, such as spaCy and Hugging Face Transformers, to process and interpret user queries, delivering precise and relevant responses. Comprehensive data on government schemes is collected, preprocessed, and used to train the model. Integrated into a user- friendly interface, the chatbot ensures effortless interaction, allowing users to inquire about various initiatives and obtain real-time information. The system incorporates testing and monitoring mechanisms to ensure accuracy and adaptability to a wide array of user inputs. Regular updates are planned to reflect policy changes and maintain the chatbot's relevance. Additional features include user authentication for personalized assistance and provisions for human support to handle complex queries.
Keyword NLP-based Chatbot, Government Schemes, Tamil Nadu, Hugging Face Transformers, User-friendly Interface
Reference:
  1. Zhang, Y., & Wallace, B. (2017). A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification.
  2. Goyal, P., Gupta, R., & Goyal, L. M. (2020). A review of chatbot and natural language processing. International Journal of Advanced Research in Computer Science.
  3. Rashid, S. M., Abdullah, A. H., & Ahmed, M. A. (2019). Development of a chatbot using natural language processing for customer service. International Journal of Computer Science and Information Security (IJCSIS).
  4. Singh, A., & Sharma, M. (2020). AI Chatbot: A review of literature. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 23-28). IEEE.
  5. Debnath, B., Chakraborty, D., & Mandal, S. K. (2019). Chatbot for e-learning: A review. In Proceedings of the 2nd International Conference on Inventive Research in Computing Applications (pp. 186-190). IEEE.
  1. Prova, N. N. I. (2024, August). Healthcare Fraud Detection Using Machine Learning. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)(pp. 1119-1123). IEEE.
  2. Prova, N. N. I. (2025). Enhancing Agricultural Research with an Attention-Based Hybrid Model for Precise Classification of Rice Varieties. Authorea Preprints.
  3. Devi, K., & Indoria, D. (2021). Digital Payment Service In India: A Review On Unified Payment Interface.  J. of Aquatic Science, 12(3), 1960-1966.
  4. Devi, K., & Indoria, D. (2023). The Critical Analysis on The Impact of Artificial Intelligence on Strategic Financial Management Using Regression Analysis. Res Militaris, 13(2), 7093-7102.
  5. Devi, K., & Indoria, D. (2022, December). Study on the waves of blockchain over the financial sector. In List Forum für Wirtschafts-und Finanzpolitik(Vol. 48, No. 3, pp. 181-201). Berlin/Heidelberg: Springer Berlin Heidelberg.
  6. Indoria, D., & Devi, K. (2021). An Analysis On The Consumers Perception Towards Upi (Unified Payments Interface).  J. of Aquatic Science, 12(2), 1967-1976.
  7. Devi, K., & Indoria, D. (2024). Impact of Russia-Ukraine War on the Financial Sector of India. Drishtikon: A Management Journal, 15(1).
  1. Devi, K., & Indoria, D. (2021). Role of Micro Enterprises in the Socio-Economic Development of Women–A Case Study of Koraput District, Odisha. Design Engineering, 1135-1151.
  1. Selvaprasanth, P., Karthick, R., Meenalochini, P., & Prabaharan, A. M. (2025). FPGA implementation of hybrid Namib beetle and battle royale optimization algorithm fostered linear phase finite impulse response filter design. Analog Integrated Circuits and Signal Processing, 123(2), 33.
  2. Deepa, R., Karthick, R., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
  3. Kumar, T. V., Karthick, R., Nandhini, C., Annalakshmi, M., & Kanna, R. R. (2025). 20 GaN Power HEMT-Based Amplifiers. Circuit Design for Modern Applications, 320.
  4. Velayudham, A., Karthick, R., Sivabalan, A., & Sathya, V. (2025). IoT enabled smart healthcare system for COVID-19 classification using optimized robust spatiotemporal graph convolutional networks. Biomedical Signal Processing and Control, 100, 107104.
  5. Kalaiselvi, B., & Thangamani, M. (2020). An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques. Measurement, 162, 107885.
  6. Prabhu Kavin, B., Karki, S., Hemalatha, S., Singh, D., Vijayalakshmi, R., Thangamani, M., … & Adigo, A. G. (2022). Machine learning‐based secure data acquisition for fake accounts detection in future mobile communication networks. Wireless Communications and Mobile Computing, 2022(1), 6356152.
  7. Geeitha, S., & Thangamani, M. (2018). Incorporating EBO-HSIC with SVM for gene selection associated with cervical cancer classification. Journal of medical systems, 42(11), 225.
  8. Thangamani, M., & Thangaraj, P. (2010). Integrated Clustering and Feature Selection Scheme for Text Documents. Journal of Computer Science, 6(5), 536.
  9. Gangadhar, C., Chanthirasekaran, K., Chandra, K. R., Sharma, A., Thangamani, M., & Kumar, P. S. (2022). An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO. International Journal of Engineering Systems Modelling and Simulation, 13(4), 284-288.
  10. Narmatha, C., Thangamani, M., & Ibrahim, S. J. A. (2020). Research scenario of medical data mining using fuzzy and graph theory. International Journal of Advanced Trends in Computer Science and Engineering, 9(1), 349-355.
  11. Thangamani, M., & Thangaraj, P. (2013). Fuzzy ontology for distributed document clustering based on genetic algorithm. Applied Mathematics & Information Sciences, 7(4), 1563-1574.
  12. Surendiran, R., Aarthi, R., Thangamani, M., Sugavanam, S., & Sarumathy, R. (2022). A Systematic Review Using Machine Learning Algorithms for Predicting Preterm Birth. International Journal of Engineering Trends and Technology, 70(5), 46-59.
  13. Thangamani, M., & Thangaraj, P. (2010). Ontology based fuzzy document clustering scheme. Modern Applied Science, 4(7), 148.
  14. Ibrahim, S. J. A., & Thangamani, M. (2018, November). Momentous Innovations in the prospective method of Drug development. In Proceedings of the 2018 International Conference on Digital Medicine and Image Processing(pp. 37-41).
  15. Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., & Hamdi, M. (2022). Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132-148.
  16. Ramesh, T. R., Vijayaragavan, M., Poongodi, M., Hamdi, M., Wang, H., & Bourouis, S. (2022). Peer-to-peer trust management in intelligent transportation system: An Aumann’s agreement theorem based approach. ICT Express, 8(3), 340-346.
  17. Ramesh, T. R., & Kavitha, C. (2013). Web user interest prediction framework based on user behavior for dynamic websites. Life Sci. J, 10(2), 1736-1739.
  18. Ali, A., Naeem, S., Anam, S., & Ahmed, M. M. (2022). Machine learning for intrusion detection in cyber security: Applications, challenges, and recommendations. UMT Artif. Intell. Rev, 2(2), 41-64.
  1. Ramesh, T. R., Raghavendra, R., Vantamuri, S. B., Pallavi, R., & Easwaran, B. (2023). IMPROVING THE QUALITY OF VANET COMMUNICATION USING FEDERATED PEER-TO-PEER LEARNING. ICTACT Journal on Communication Technology, 14(1).
  1. Jayapandiyan, J. R., Kavitha, C., & Sakthivel, K. (2020). Enhanced least significant bit replacement algorithm in spatial domain of steganography using character sequence optimization. Ieee Access, 8, 136537-136545.
  2. Sakthivel, K., Jayanthiladevi, A., & Kavitha, C. (2016). Automatic detection of lung cancer nodules by employing intelligent fuzzy c-means and support vector machine. BIOMEDICAL RESEARCH-INDIA, 27, S123-S127.
  3. Sakthivel, K., Nallusamy, R., & Kavitha, C. (2014). Color image segmentation using SVM pixel classification image. World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, 8(10), 1924-1930.
  4. Jayapandiyan, J. R., Kavitha, C., & Sakthivel, K. (2020). Optimal secret text compression technique for steganographic encoding by dynamic ranking algorithm. In Journal of Physics: Conference Series(Vol. 1427, No. 1, p. 012005). IOP Publishing.
  5. Sakthivel, K., Abinaya, R., Nivetha, I., & Kumar, R. A. (2014). Region based image retrieval using k-means and hierarchical clustering algorithms. International Journal of Innovative Research in Science, Engineering and Technology, 3(1), 1255-1260.
  6. Gopinath, S., Sakthivel, K., & Lalitha, S. (2022). A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification. Journal of Intelligent & Fuzzy Systems, 43(4), 4173-4186.
  7. Bharathi, V., & Sakthivel, K. (2022). Unmanned mobile robot in unknown obstacle environments for multi switching control tracking using adaptive nonlinear sliding mode control method. Journal of Intelligent & Fuzzy Systems, 43(3), 3513-3525.
  8. Sakthivel, K., Nallusamy, R., & Kavitha, C. (2014). Image retrieval using fused features. World Academy of Science, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering, 8(9).
  9. Kavitha, C., Krishnan, A., & Sakthivel, K. (2005, January). Similarity based retrieval of image database: using dynamic clustering. In Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005.(pp. 147-151). IEEE.
  10. Sakthivel, K., Ravichandran, T., & Kavitha, C. (2009). Performance analysis in image retrieval using IRM and K-Means Algorithm. i-Manager’s Journal on Software Engineering, 3(4), 55.
  11. Sidharth, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
  12. Sidharth, S. (2023). Homomorphic Encryption: Enabling Secure Cloud Data Processing.
  13. Sidharth, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
  14. Sidharth, S. (2022). Enhancing Generative AI Models for Secure and Private Data Synthesis.

Sidharth, S. (2021). Multi-Cloud Environments: Reducing Security Risks in Distributed Architectures.