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Vanya Mishra,
- Assistant professor, Department of Computer science, Greater Noida College, India, Uttar Pradesh, India
Abstract
Blending predictive data mining with metaheuristic optimization has become essential for tackling tough, real-world problems across all kinds of fields. Most existing methods stick to fixed algorithms, each focused on a tiny slice of the puzzle, barely budging when new variables or unpredictability show up—especially with messy, human-generated data. So, here’s the idea: a Unified Metaheuristic and Predictive Data Mining (UMPDM) framework that finally connects adaptive search methods with powerful predictive analytics. This framework doesn’t just mash together existing tech—it weaves dynamic optimization with probabilistic data models into something that fits healthcare, engineering, social computing, you name it. By laying out a clear methodology and a solid plan for testing, this study shows that mixing flexible metaheuristics with predictive models that understand data noise really gives you an edge. Systems that can handle uncertainty, work faster, and actually find better solutions, even when everything keeps changing.
Keywords: Metaheuristics, Predictive Data Mining, Cross-Domain Optimization, Adaptive Frameworks, Human Uncertainty.
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Research and Reviews : Journal of Computational Biology
| Volume | 15 | |
| 02 | ||
| Received | 24/07/2026 | |
| Accepted | 27/07/2026 | |
| Published | 10/08/2026 | |
| Publication Time | 17 Days |