I specialize in designing and implementing neural networks, such as custom U-Net models and ResNet-101 integration, for tasks like image classification and glioma detection. I have a proven track record of improving diagnostic accuracy by 25% and reducing false positives by 30%.
I have expertise in developing AI-driven systems for real-time object detection, achieving high accuracy rates like 92% for smoke detection. My experience includes working with multi-band data and threat detection algorithms.
I possess strong skills in data analysis using Pandas and NumPy, and data visualization with Matplotlib and Seaborn. I have experience with predictive modeling techniques like SVM, Naive Bayes, and Random Forest, and conducting exploratory data analysis for drug effectiveness prediction.
I hold a patent for an AI-based intruder detection system and have optimized algorithms for threat detection, improving accuracy by 40%
I have hands-on experience with deep learning frameworks like TensorFlow, Keras, and PyTorch. I’ve optimized models, such as reducing prediction error by 20% through hyperparameter tuning for an age prediction model.
I am certified in Oracle Cloud Infrastructure (OCI) and proficient in deploying AI/ML solutions in the cloud, managing data, and ensuring security
