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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64500
Volume:
10 |
6 |
Jun 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of computer applications
Bapuji Institute of Engineering and technology Davanagere
Master of computer applications
Bapuji Institute of Engineering and technology Davanagere
The neutral of this development is to develop an AI-driven system for robotic group of personalized three-dimensional (3D) announcements, addressing the limitation of conventional online advertising methods where generic advertisements often fail to capture user attention. The proposed system aims to transform digital marketing practices by replacing static two-dimensional (2D) banner advertisements with dynamically generated, data-driven 3D visual content. The core concept of the system involves the bid of Fake Intelligence techniques to analyze userrelated data, including browsing history, geographical location, and purchase behavior, in order to infer user preferences. Based on this analysis, the system automatically generates customized 3D advertisements that align with individual user interests, thereby increasing relevance and engagement. The construction of the system consists of two tightly integrated components. The first component is the Personalization Engine, to process user data and construct a digital user profile. This profile functions as a structured input for the second component, known as the 3D Generative Module. The generative module employs AI-based content generation techniques to produce high-quality 3D advertisements in real time. As a result, the same product may be visually represented in different formats depending on the user profile being processed. The system enables real-time rendering and deployment of advertisements, allowing multiple users to receive distinct visual representations of identical products simultaneously. This approach eliminates the need for manual advertisement design and enables automated, scalable ad generation. In conclusion, the proposed project demonstrates the applicability of non-natural aptitude in enhancing creativity and operational efficiency within the digital advertising ecosystem. By transitioning from manually designed advertisements to real-time AI-generated 3D content, the system aims to improve user interaction metrics such as click-through rates and conversion rates. This AI-based personalized advertising framework represents an advancement in digital marketing by enabling data-driven, immersive, and performance-oriented advertising solutions that benefit both advertisers and consumers.
KEYWORDS
Smart Vision Face ID Detection System proves to be an exemplary application that shows the viability of combining Artificial Intelligence Deep Learning and Computer Vision to build an intelligent and contactless identity recognition system that is secure as well as efficient. This system which makes use of advanced face detection as well as age as well as gender estimation makes identity recognition more efficient as well as convenient. The system uses the capabilities of machine learning algorithms along with image processing algorithms to provide accurate real-time recognition. The system can work under different light conditions and with varying orientations which indicates a higher level of robustness. The system would work effectively under various conditions such as in educational institutions offices hospitals and smart cities. Moreover the system emphasizes the increasing need for touch-less biometric authentication systems in the present world where hygiene security and efficiency are critical concerns. The system’s functionality to carry out automated facial analysis ensures that the organization has secure and efficient access control. In essence this project has fulfilled its purpose of developing a reliable scaleable and intelligent face recognition system for the prediction of demographically linked attributes in a secure and efficient manner. The project structure is quite versatile and includes tasks such as: the detection of emotions masks and multi-person tracking.
1) Mikolov T. Chen K. Corrado G. & Dean J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781. 2) Ghosh S. & Kulkarni S. (2019). AI in Advertising: A Review of Applications and Techniques. International Journal of Computer Applications 182(42) 1–6. 3) Kietzmann J. Paschen J. & Treen E. (2018). Artificial Intelligence in Advertising: How Marketers Can Leverage AI. Journal of Advertising Research 58(3) 263–267. 4) Radford A. et al. (2019. OpenAI Technical Report. 5) McKinsey & Company. (2020). Personalization And How to Get Ready for It. McKinsey 6) Insights. 7) Kumar V. & Gupta P. (2021). AI-Based Personalized Marketing Strategies Using NLP and Deep Learning. Information Technologies 12(1) 45–50. 8) Bansal R. & Sharma A. (2020). A Survey on Content Generation. International Journal of Computer Applications 183(12) 33-38.
Publication Date
6/4/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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