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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63938
Volume:
10 |
5 |
May 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.
Department of Computer Engineering (Student)
Sinhgad College of Engineering (SPPU)
Department of Computer Engineering (Student)
Sinhgad College of Engineering (SPPU)
Department of Computer Engineering (Student)
Sinhgad College of Engineering (SPPU)
Department of Computer Engineering (Student)
Sinhgad College of Engineering (SPPU)
Department of Computer Engineering (Assistant Professor)
Sinhgad College of Engineering (SPPU)
-The way people shop online is changing fast. This means there is a need for smart systems that can understand what is happening in the market right now make decisions on their own and predict what will happen in the future. The old systems that recommend products and analyse data are still having problems with growing, understanding the context and making decisions quickly. This paper is about TrendScanner AI 2.0 which's the latest and most advanced version of the TrendScanner AI system we talked about before. The new system can work with agents predict what will happen in the market use something called Retrieval-Augmented Generation analyse data in many ways and manage workflows on its own. We are sharing what we learned from this project how we improved the system the challenges we faced and what we discovered along the way. We used a lot of technologies like Gemini-based reasoning systems, special databases, advanced natural language processing, predictive models, Firebase cloud infrastructure and Docker-Kubernetes deployment strategies to create a system that can give us smart insights into online shopping. When we tested the system, we found that it was better at predicting trends analysing data faster working reliably and growing more easily, than the old system. This shows that it is possible to move from analytics systems to new smart systems that can help businesses make better decisions.
KEYWORDS
TrendScanner AI 2.0 is the version of a computer program that helps businesses analyze online shopping trends. It uses intelligence to automate, predict and optimize e-commerce market analysis. · The research behind TrendScanner AI 2.0 shows how it was built and what was learned during its development. · The program combines tools, including adaptive teamwork, predictive forecasting and cloud-based management. · These tools make TrendScanner AI 2.0 reliable, scalable and useful for businesses. The study on TrendScanner AI 2.0 suggests that future commerce intelligence systems will rely heavily on: 1. Autonomous reasoning agents 2. Contextual AI interpretation 3. Predictive analytics 4. Scalable cloud infrastructure TrendScanner AI 2.0 lays the groundwork for research and use, in industries related to AI, business intelligence and commerce analytics. TrendScanner AI 2.0 helps businesses make decisions. The TrendScanner AI 2.0 system is a step forward. It uses TrendScanner AI 2.0 to analyze data. TrendScanner AI 2.0 provides insights. The TrendScanner AI 2.0 tool is very useful.
[1] A. Sharma and R. Verma, “Real-Time Sentiment Analysis on E-Commerce Applications Using Deep Learning,” IEEE Conf. Big Data Analytics, 2019. [2] X. Sun, H. Chen, “Time Series Forecasting for E-Commerce Data,” IEEE Trans. Comput. Soc. Syst., 2019. [3] T. Moore et al., “AI Horizon Scanning – Emerging AI Trends,” IEEE AI Mag., 2024. [4] J. Li, K. Zhang, “E-Commerce Data Analysis Platform Design Based on Cloud Architecture,” IEEE Access, 2021. [5] P. Ramesh, D. Menon, “Multimodal Reinforcement Learning for Product Recommendations,” IEEE Access, 2022. [6] M. Jaiswal, A. Gupta, “LLM-Driven Market Analysis Systems for Decision Making,” IEEE ICAIA, 2023. [7] J. Kwon, “Autonomous Multi-Agent Collaboration for Business Intelligence,” IEEE Systems Journal, 2023. [8] S. Patel, R. Iyer, “Retrieval-Augmented Generation for Enterprise AI Systems,” ACM Intelligent Systems Review, 2024. [9] K. Zhao et al., “Predictive Commerce Analytics Using Hybrid Deep Learning Models,” IEEE Access, 2025. [10] M. Fernandes, “Cloud-Native AI Orchestration for Large Scale Business Intelligence,” International Journal of Advanced Computing Systems, 2025.
Publication Date
5/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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