Go back to publicationsView external

Hyperparameter Optimization for Improved Mine Detection Using Machine Learning: A Methodical Analysis of Classification Techniques

November 2025 Parag Ekbote, Suraj Maharana, Priyanshi Mulwani, Swati Sharma, Dr. Santosh Kumar, Mily Lal 2025 IEEE 2nd International Conference for Women in Computing (InCoWoCo)
#Machine Learning#Hyperparameter Optimization#AutoML#Model Evaluation#Research

Abstract

The potent performance of ML models is essential for obtaining intelligent insights from the complexity of modern data. To enhance the performance, we explore the usage of hyperparameter optimization across models. In this study, we analyze several hyperparameter optimization strategies that can enhance the performance of the models. The findings are reproducible and reliable, thereby significantly advancing the academic applications by providing a scalable foundation for model building.

My Contributions

  • Evaluated multiple hyperparameter optimization strategies for machine learning models.
  • Demonstrated reproducible experimentation for fair model comparison.
  • Established a scalable workflow for improving model performance through systematic parameter tuning.
  • Highlighted the importance of automated optimization techniques in modern machine learning pipelines.

Research Areas

  • Machine Learning
  • Hyperparameter Optimization
  • AutoML
  • Model Evaluation

Resources