IRO Journals
Journal of Information Technology and Digital World

VisionaryX: AI-Based Business Analysis and Forecasting using Integrated Computational Intelligence Approaches

Open Access
Published: 23 May, 2026
Pages: 116-127
  • Merlyne Sandra Christina C. , Merlyne Sandra Christina C.
    Assistant Professor, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
    Assistant Professor, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
  • Jerome J. , Jerome J.
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
  • Kabilan S. , Kabilan S.
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
  • Hari S. Hari S.
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
    Student, Department of Artificial Intelligence & Data Science, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India
view PDF
view PDF

How to Cite

C., Merlyne Sandra Christina, Jerome J., Kabilan S., and Hari S. 2026. “VisionaryX: AI-Based Business Analysis and Forecasting Using Integrated Computational Intelligence Approaches”. Journal of Information Technology and Digital World 8 (2): 116-27. https://doi.org/10.36548/jitdw.2026.2.004.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Artificial Intelligence (AI)
Business Forecasting
Natural Language Processing (NLP)
Machine Learning Models
Market Trend Analysis
Optimization

Abstract

VisionaryX is an AI-based business analysis and forecasting model designed to help users evaluate their business concepts with the use of intelligent computational and modeling techniques. User inputs are analyzed with the help of natural language processing (NLP) and large language models (LLM) to extract important parameters for the evaluation process. The system performance is evaluated via a detailed framework considering the following parameters: market trends, competitors' analysis, user inputs, and estimation of finances. Machine learning models are applied to simulate and forecast future growth and profits from businesses under analysis. They capture the complex non-linear relations between different influencing factors and predict the dynamics of business development. Experimental testing and evaluation of system performance prove the efficiency and reliability of predictions. VisionaryX combines real-time data analysis and simulation models to generate and optimize strategies for business. Moreover, visualizing modules are utilized for better interpretability of results via interactive dashboards. The combination of AI, big data analytics, and intelligent automation offers valuable perspectives for further business development. The proposed model has proven to be highly efficient with the accuracy of 89%, RMSE of 0.021, and MAPE of 3.8%.

References

  1. Davenport, Thomas H. "Competing on Analytics." Harvard business review 2006, vol. 84, no. 1, 98–107.
  2. Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep Learning. Cambridge: MIT press 2016, vol. 1, no. 2.
  3. Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern Recognition and Machine Learning, New York: springer, 2006, vol. 4, no. 4.
  4. Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski, eds. Machine Learning: A Guide to Current Research, Springer Science & Business Media, 1986, vol. 12.
  5. Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. "Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding, Conference of The North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2019, vol. 1, 2019, 4171-4186.
  6. OpenAI, R. "Gpt-4 Technical Report 2023. arxiv 2303.08774." View in Article 2, no. 5: 1.
  7. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is All You Need." Advances in neural information processing systems 2017, vol. 30.
  8. Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers et al. "Big Data: The Next Frontier for Innovation, Competition, and Productivity." McKinsey Global Institute 2011: 222.
  9. Nedic, Bogdan. "Gartner's Top Strategic Technology Trends." Proceedings on Engineering Sciences Quality Festival 2019, 433-442.
  10. Manoharan, Geetha, Subhashini Durai, Gunaseelan Alex Rajesh, and Sunitha Purushottam Ashtikar. "A Study on the Application of Natural Language Processing Used in Business Analytics for Better Management Decisions: A Literature Review." Artificial Intelligence and Knowledge Processing 2023, 249-261.
  11. Sharma, Neha, Mukesh Soni, Sumit Kumar, Rajeev Kumar, Nabamita Deb, and Anurag Shrivastava. "Supervised Machine Learning Method for Ontology-Based Financial Decisions in the Stock Market." ACM Transactions on Asian and Low-Resource Language Information Processing 2023, vol. 22, no. 5: 1-24.
  12. Singh, Gurpreet, and Subham Kumar Singh. "Analysis of Data Visualization Techniques Useful for Machine Learning and Visual Reality." 2nd International Conference on Edge Computing and Applications (ICECAA) 2023, 1077-1082.
  13. Domingos, Pedro. The master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake our World. Basic Books, 2015.
  14. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "The Elements of Statistical Learning." 2nd Edition 2009.
  15. Customer Segmentation and Market Basket Analysis. https://www.kaggle.com/code/mgmarques/customer-segmentation-and-market-basket-analysis