International Journal of Computer Techniques Volume 12 Issue 3 | Applied Machine Learning in Business Intelligence: Opportunities and Emerging Paradigms
Applied Machine Learning in Business Intelligence: Opportunities and Emerging Paradigms
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
The integration of **Machine Learning (ML) into Business Intelligence (BI) systems** has transformed enterprise decision-making. This study highlights the impact of ML techniques such as **forecasting, classification, and anomaly detection** while exploring emerging trends like **AutoML and Reinforcement Learning (RL)**. The paper proposes **an AI-powered framework** to enhance **data-driven insights and automation**, improving operational efficiency. Future directions such as **Explainable AI and Federated Learning** are examined for their role in advancing **intelligent decision support systems**.
Keywords
Machine Learning, Business Intelligence, AutoML, Reinforcement Learning, Forecasting, Explainable AI, Federated Learning, AI Decision Support.
Conclusion
The **fusion of Machine Learning and Business Intelligence** is reshaping **enterprise analytics**, making AI-driven decision-making more accessible and efficient. Future advancements will focus on **responsible AI, real-time intelligence, and human-AI collaboration** to improve **strategic business automation**.
References
- J. Brownlee (2017). “Time Series Forecasting with LSTM Networks in Python.” Machine Learning Mastery.
- S. Lundberg & S.-I. Lee (2017). “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems.
- P. Domingos (2012). “A Few Useful Things to Know About Machine Learning.” Communications of the ACM.
Post Comment