@article {10.3844/jcssp.2025.78.87,
article_type = {journal},
title = {Artificial Intelligence and Mathematical Modelling-Based Techniques to Improve Social Media Marketing},
author = {Chaudhary, Kiran and Rahman, Mohammad Naquibur and Hasan, Nabeela and Alam, Mansaf and Punit, Aakash},
volume = {21},
number = {1},
year = {2024},
month = {Dec},
pages = {78-87},
doi = {10.3844/jcssp.2025.78.87},
url = {https://thescipub.com/abstract/jcssp.2025.78.87},
abstract = {The Businessperson is using social media to do marketing of their product. Almost all people use social media. Most people relate to social media. Social media marketing is beneficial in making the product more popular and easy ways to introduce the product to everyone in economical ways. In this study, we introduce the concept of machine learning and mathematical models to enhance social media marketing for the product. We have used the concept of data analytics in this study to analyze social media data and based on the outcome of the analytics result, we developed strategies for marketing the product. The Bayesian Regularization (BR) Model has achieved the highest accuracy of 93% and the Root mean squire error is as low as 1.2 approximately in decision tree regression (DTR). The Area Under The Curve (AUC) of the model is .57. The model will be beneficial to improving social media marketing. The business personnel will benefit from marketing products on social media networks.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}