International Journal of Science, Engineering and TechnologyAuthors: Vishu, Vishal Arya, Rajesh Saxena
Abstract: The demand for appointment scheduling solutions that are smart, efficient, and scalable is rising in service-based industries such as healthcare, education, and professional services due to the rapid growth of digital technologies. In the past, appointment scheduling systems relied solely on manual coordination or fixed form-based scheduling solutions. Some of the challenges connected with traditional appointment scheduling solutions include conflict scheduling, late confirmations, poor resource utilization, a heavy administrative burden, and issues with user experience, especially within industries where growing demands for services have presented a challenge to efficient operations and service delivery. Automated decision-making by AI-based intelligent systems can improve appointment management systems through new scopes provided by recent developments in AI and web technologies. In this context, this research work proposes an AI-based appointment scheduling assistant built on the MERN technology stack, including MongoDB, Express.js, React.js, and Node.js, which is associated with natural language processing and rule-based AI technology. Through the use of natural language statements, the proposed work allows for appointment scheduling, creating a more user-friendly and interactive experience for clients. NLP techniques are employed by the project to extract important appointment details like date, time, and purpose, and then a rule-based AI engine checks appointment constraints and rules for compliance with pre-defined business rules. The implementation involves the use of JSON Web Tokens to ensure security for accessing the system and data integrity. All data related to appointments on the system is stored in MongoDB, a flexible and scalable storage system. The proposed system's experimental verification has shown improvements in scheduling accuracy, conflict resolution, human intervention, and system response time over the conventional scheduling method. The efficiency of a system can be greatly optimized by using NLP intelligence and expertise systems on a contemporary full-stack solution, as suggested by experimental results. The proposed work enables appointment scheduling in a cost-effective, intelligent, and efficient manner and sets the basis for performing improvisations in prediction, machine learning, and multiple language support.
DOI: http://doi.org/10.5281/zenodo.20338752
