A Complete Examination and Use of AI Techniques for Investigating Virtual Entertainment Organization Publicizing
G. Ravi Kumar1, G Thippanna2, K Nagamani3
1Dr. G. Ravi Kumar, Assistant Professor, Department of Computer Science, Rayalaseema University, Kurnool (Andhra Pradesh), India.
2Dr. G. Thippanna, Professor, Department of Computer Science and Engineering, Dr. K. V. Subbareddy Institute of Technology, Dupadu (Andhra Pradesh), India.
3Dr. K. Nagamani, Assistant Professor, Department of Computer Science, Rayalaseema University, Kurnool (Andhra Pradesh), India.
Manuscript received on 29 January 2025 | First Revised Manuscript received on 18 February 2025 | Second Revised Manuscript received on 19 May 2025 | Manuscript Accepted on 15 June 2025 | Manuscript published on 30 June 2025 | PP: 15-18 | Volume-4 Issue-4, June 2025 | Retrieval Number: 100.1/ijssl.C116104030325 | DOI: 10.54105/ijssl.C1161.04040625
Open Access | Ethics and Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The vast number of users on social networking sites makes it an integral part of advertisement strategies. The rise of social media platforms like Facebook, Twitter, and WhatsApp as a vehicle for marketing communication has significantly increased the level of popularity they already enjoy among the general public, accounting for a substantial percentage of human activities within and across these social networking platforms on the Web. With some credibility, Face book itself promotes it as a potentially ideal marketing strategy. This brief chronicle recounts how it enables advertising businesses to access and target communication towards anyone’s wall, post, and profile for advertising purposes. Remarkable communication, as instructed in the tube explored herein, has led us to find that no page has been created in any product solely because of an advertisement on a user’s wall, nor does any resolution depend on Facebook in any other matter of concern. Nevertheless, Facebook stands out significantly in facilitating communication among user groups. Using experimental approaches with neural networks and Support Vector Machines (SVMs) for social networking advertisements would be a valuable contribution of this paper. The research examines and compares these two methods, determining their accuracy, precision, and recall.
Keywords: Support Vector Machine (SVM), Neural Network, Machine Learning (ML), Social Network and Model Selection.
Scope of the Article: Social Science