ADiTi App: Leveraging Deep Learning and Generative AI for a Chatbot Application with Deep Belief Networks
Keywords:
Chatbot, GenerativeAI, NLP-Natural Language processing, KNN- k-Nearest Neighbors, DBN-Deep Belief NetworkAbstract
A chatbot is an application that can chat with people by using AI. Nowadays, a lot of people have used the chatbot for conservation purposes because of time-saving and getting a fast reply. Sometimes Students or parents visited the university to collect information as the admission process, fee structure, and campus view, etc. This process is too time-consuming, so ADiTi App a generative AI based chatbot system is developed for MIT ADT University. In this paper, the concept of Natural Language Processing(NLP), Artificial Intelligence(AI), Machine Learning(ML), Dialogflow, and communicate tool has been used. This chatbot increased the performance and accuracy results in 96% in answering the questions asked by the user either in the form of voice or text.
Downloads
References
Ait-Mlouk, Addi, and Lili Jiang. "KBot: a Knowledge graph based chatBot for natural language understanding over linked data." IEEE Access 8 ,149220-149230, 2020.
Kidwai, Bushra, and R. K. Nadesh. "Design and development of diagnostic Chabot for supporting primary health care systems." Procedia Computer Science 167 ,75-84, 2022.
Dr. S.Priyadarsini et al. “Smart Chatbot with Voice Recognition” IJRAR, Volume 7, Issue 2, April 2020.
Vamsi, G. Krishna, Akhtar Rasool, and Gaurav Hajela. "Chatbot: A Deep Neural Network Based Human to Machine Conversation Model." 2020 11th IEEE International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020.
Kadali, Bhavyasri, et al. "Home Automation Using Chatbot and Voice Assistant." ITM Web of Conferences. Vol. 32. EDP Sciences, 2020.
Patel, Neelkumar P., et al. "AI and web-based human-like interactive university chatbot (UNIBOT)." 3rd IEEE International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019
Honda, Hiroshi, and Masafumi Hagiwara. "Question Answering Systems With Deep Learning-Based Symbolic Processing." IEEE Access 7 (2019): 152368-152378.
Singh, Rupesh, et al. "Chatbot using TensorFlow for small Businesses." 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018.
Vaira, Lucia, et al. "MamaBot: a System based on ML and NLP for supporting Women and Families during Pregnancy." Proceedings of the 22nd International Database Engineering & Applications Symposium, 2018.
Qiu, Minghui, et al. "Alime chat: A sequence to sequence and rerank based chatbot engine." Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2017.
Ranoliya, Bhavika R., Nidhi Raghuwanshi, and Sanjay Singh. "Chatbot for university related FAQs." ,IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.
Yan, Mengting, et al. "Building a chatbot with serverless computing." Proceedings of the 1st International Workshop on Mashups of Things and APIs. 2016.
AbuShawar, Bayan, and Eric Atwell. "ALICE chatbot: Trials and outputs." Computación y Sistemas 19.4 625-632, 2015.
Jia, Jiyou. "CSIEC: A computer assisted English learning chatbot based on textual knowledge and reasoning." Knowledge-Based Systems 22.4 (): 249-255, 2009.
Patel, Neelkumar P., et al. "AI and Web-Based Human-Like Interactive University Chatbot (UNIBOT)." 3rd IEEE International conference on Electronics, Communication and Aerospace Technology (ICECA)., 2019.
Dharwadkar, Rashmi, and Neeta A. Deshpande. "A medical chatbot." Int J Comp Trends Technol 60.1 ,2018.
Shawar, Bayan Abu, and Eric Atwell. "Chatbots: are they really useful?." Ldv forum. Vol. 22. No. 1. 2007.
Abid Haleem, Mohd Javaid, Ravi Pratap Singh,“An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges”, Bench Council Transactions on Benchmarks, Standards and Evaluations,Vol. 2, Issue 4, ISSN, 2772-4859, 2022.
Dubey, G. ., Khera, R. ., Grover, A. ., Kaur, A. ., Goyal, A. ., Rajkumar, R., Khatter, H. ., & Srivastava, S. . (2023). A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 341–348. https://doi.org/10.17762/ijritcc.v11i4.6458
Paul Garcia, Ian Martin, Laura López, Sigurðsson Ólafur, Matti Virtanen. Enhancing Student Engagement through Machine Learning: A Review. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/163
Timande, S., & Dhabliya, D. (2019). Designing multi-cloud server for scalable and secure sharing over web. International Journal of Psychosocial Rehabilitation, 23(5), 835-841. doi:10.37200/IJPR/V23I5/PR190698
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
