Research Article
Integrating Intellectual Consciousness AI based on Ensemble Machine Learning for Price Negotiation in E-commerce using Text and Voice-Based Chatbot
@ARTICLE{10.4108/eetiot.5370, author={Yagnesh Challagundla and Lohitha Rani Chintalapati and Trilok Sai Charan Tunuguntla and Anupama Namburu and Srinivasa Reddy K and Janjhyam Venkata Naga Ramesh}, title={Integrating Intellectual Consciousness AI based on Ensemble Machine Learning for Price Negotiation in E-commerce using Text and Voice-Based Chatbot}, journal={EAI Endorsed Transactions on Internet of Things}, volume={10}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={3}, keywords={Price Negotiation, E-Commerce Negotiation, Online Shopping, Chatbot system, Voice assistant}, doi={10.4108/eetiot.5370} }
- Yagnesh Challagundla
Lohitha Rani Chintalapati
Trilok Sai Charan Tunuguntla
Anupama Namburu
Srinivasa Reddy K
Janjhyam Venkata Naga Ramesh
Year: 2024
Integrating Intellectual Consciousness AI based on Ensemble Machine Learning for Price Negotiation in E-commerce using Text and Voice-Based Chatbot
IOT
EAI
DOI: 10.4108/eetiot.5370
Abstract
Online shopping has experienced an enormous boost in recent years. With this evolution, the majority of internet shopping's capabilities have been developed, but some functions, such as negotiating with store owners, are still not available. This paper suggests employing a chatbot with a voice assistant to negotiate product prices. Customers can communicate with the chatbot to get assistance in finding a reasonable price for a product. In online purchasing, there is a possibility that the consumers or the product seller's budget may be compromised. In order to assist in purchasing, algorithm has been created in machine learning that uses the forecasting of historical data to avoid compromising situations. However, improper dataset or when irrelevant aspects or at- tributes of the data are used, price prediction might become less accurate. Ecommerce companies do not merely depend on price prediction tools due to the significant financial losses brought on even by a single inaccurate price prediction. Additionally, few models fail to perform well when the data saturates or when an attribute becomes inaccessible after the period for which the model's prediction was reliant. By controlling these alterations, the accuracy and dependability are preserved in the model pro- posed in this study.
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