Authors:
R. Regin, R. S. Gaayathri, P. Paramasivan, S. Suman Rajest, Radwa Radwan

Addresses:
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. 2Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. 3Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. 4Department of Research and Development & International Student Affairs, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. 5Department of Business Administration, College of Humanities and Administration Studies, Onaizah Colleges, Unaizah, Saudi Arabia. 5Department of Business Administration, Higher Future Institute for Specialized Technological Studies, Future Academy, Obour City, Egypt. regin12006@yahoo.co.in1, gr4829@srmist.edu.in2, paramasivanchem@gmail.com3, sumanrajest414@gmail.com4, radwa.mohamed@fa-hists.edu.eg5

Abstract:

This research paper examines the creation and execution of VehiWalnut, a cutting-edge system developed to enhance fuel management processes. VehiWalnut incorporates cutting-edge technologies such as data visualization, cloud-based chatbot evaluation, and deep learning to improve efficiency and user satisfaction. A thorough literature review is conducted to identify important studies in data visualization, Web API analysis, chatbot performance evaluation, and deep learning techniques for machine learning chatbots. These studies are then combined to form the basis of the research. The text describes the development of VehiWalnut, emphasizing its diverse functionalities and the use of the Python programming language for its implementation. The system allows users to enter fuel fill-up data through WhatsApp messages, which are processed using regular expressions to extract and insert the data into a SQLite database. VehiWalnut allows users to request detailed reports on fuel expenses, timing of fuel refills, predictions for the next refill date, and other related information. The system’s effectiveness is assessed by conducting case studies and performance evaluations, showcasing its capacity to enhance fuel management procedures for individual and commercial users. The research findings offer valuable insights into incorporating advanced technologies to improve fuel management systems, thereby paving the way for future advancements in this domain.

Keywords: Fuel Management; WhatsApp Chatbot; Data Visualization; Machine Learning; Predictive Analysis; Python Programming; Automated Reporting; Twilio API; SQLite Database.

Received on: 13/09/2023, Revised on: 29/11/2023, Accepted on: 19/12/2023, Published on: 05/03/2024

DOI: 10.69888/FTSIN.2024.000157

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 1, Pages: 56-71

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