Engineered an advanced dual CNN architecture for automated hardware failure detection using specialized models for continuous and discrete sensor data.
Implemented custom deep learning pipeline with 48-96-128 progressive feature extraction and strategic dropout regularization (0.1, 0.05 rates).
Developed a comprehensive transfer learning framework utilizing Adam optimization (5e-5 LR), class weight balancing for imbalanced datasets, and L2 regularization (0.0001).
Built production-ready MLOps pipeline with automated model versioning, timestamped backup systems, and real-time performance monitoring.
Created an interactive GUI framework enabling non-technical users to deploy ML models for industrial hardware diagnostics.
Achieved 88.89% accuracy in life-critical submarine mine detection using PCA-optimized Multi-Layer Perceptron neural networks.
Processed 60-dimensional sonar signals with only 3 missed mines out of 29 test cases for enhanced crew survival.
Engineered optimal dimensionality reduction pipeline using Principal Component Analysis, reducing feature space from 60 to 8 components (87% reduction).
Implemented systematic hyperparameter optimization across 60 PCA configurations using scikit-learn MLPClassifier with 100 hidden neurons.
Delivered mission-critical defense technology solution with 10.3% false negative rate for hostile environment navigation.