Integrating Machine Learning and Soft Computing for Smarter Problem-Solving Solutions

Lalit Sharma

Integrating Machine Learning and Soft Computing for Smarter Problem-Solving Solutions (P21-P24)

Authors: Lalit Sharma

Published: 2025/3/6

Abstract

This research deals with the integration of machine learning (ML) and soft computing techniques to make problem-solving across all domains much better. The paper investigates the predictive accuracy, adaptability and efficiency of decisions, as influenced by synergy between these methodologies. Reviewing existing literature and conducting case studies, the paper tries to identify benefits, challenges, and applications of this integration. The findings reveal that ML and soft computing together provide improved flexibility, overcome technical barriers, and enable the development of hybrid models with applications in fields such as healthcare, finance, and energy. Further research is still needed to refine frameworks and expand the application of integrated solutions in new domains.

Keywords

Integration Frameworks, Problem-Solving, Predictive Accuracy, Hybrid Models

References

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How to Cite

Lalit Sharma, (2025/3/6). Integrating Machine Learning and Soft Computing for Smarter Problem-Solving Solutions. JANOLI International Journal of Machine Learning, Deep Learning and Soft Computing , Volume 9UCsx9mP3zdzyycimNP7, Issue 1.

ISSN: A/F

JANOLI International Journal of
Machine Learning, Deep Learning and Soft Computing ( JIJMLDLSC )